This research aims to analyse the applications of IoT in agriculture and to compare the most widely used IoT platforms. The problem of determining the most appropriate IoT system depends on many factors, often expressed by incomplete and uncertain estimates. In order to find a feasible decision, this study develops a multi-criteria framework for IoT solution selection in a fuzzy environment. In the proposed framework, a new modification of the Multi-Attribute Border approximation Area Comparison (MABAC) method with a specific distance measure via intuitionistic fuzzy values has been presented as a decision analysis method. The new technique is more precise than existing crisp and fuzzy analogues, as it includes the three components of intuitionistic numbers  and the relationships between them. The effectiveness of the new decision-making framework has been verified through an illustrative example of ranking IoT platforms. The rise of information technologies and the expansion of the Internet have transformed traditional machines and devices in part of a new Internet of Things (IoT) economy, gaining ground across many industrial sectors. Over the past two years, the outbreak of COVID-19 has aggravated food security and exacerbated the problem of digital transformation in agriculture. Agriculture 4.0, IoT in agriculture and smart agriculture are synonyms of technological innovation that has fundamentally change the business processes in the agricultural value chain .Augmented reality is an important direction for the digital transformation of agricultural systems. It enriches the capabilities of field checking and the early detection of risk of crop failure and thus speeds up operations and increases yields . ThroughThe incorporation of blockchain into IoT platforms is also useful for farmers, suppliers and consumers, as this combination guarantees the quality of production from the field to the table. Blockchain ensures the security of data and operations in a supply chain through cryptographic algorithms ,6. The fDuring the COVID-19 pandemic, the preventative measures  have revealed weaknesses in food supply chains and have called into question the state of food security and farmers’ incomes. The pandemic has revealed the need for sustainable and efficient agricultural value chains .Saving resources (time and labour) by remotely monitoring environmental and infrastructural conditions instead of on-site inspections;Increasing the efficiency and sustainability of production with traceability, timeliness and the security of the food supply and reduced waste;Improving decision-making even in the case of abrupt changes in market demand through real-time data analysis of the entire value chain.The benefits of connecting physical objects in farms to the Internet are as follows:The decision-making process for IoT system selection could be supported by methods taking into account many factors in their calculations. According to recent studies, three main approaches have been developed: mathematical programming (optimization) ,12,13, MOptimization models often indicate only local optimums instead of the global solution. A disadvantage of ML methods is that the quality of their solutions depends on dataset quality. In contrast, MCDM methods work well even on small datasets; dozens of methods have been developed for determining object rankings and weights of attributes and their combinations; the obtained results are easily interpreted.Each MCDM model consists of three main elements: a finite set of alternatives, at least two criteria and at least one decision-making method with crisp or fuzzy estimates and criteria weights. As MCDM has been an active area of research in recent years, decision analysis methods have found applications to solve the IoT system selection problem.Some researchers have proposed referential models for IoT platform selection, including one or more existing MCDM methods; for example, Simple Additive Weight (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) , a set oAnother segment of studies has suggested new hybrid models combining a method for determining the importance of criteria weights with a multi-criteria method. For example, Mashal et al. have built a meta-model combining Analytical Hierarchy Process (AHP) and SAW , YoussefAs the estimates of compared IoT platforms are often inaccurate and uncertain, a mix of multi-criteria decision-making and fuzzy set theory could be employed to rank them . Lin et Kondratenko et al. have built a Mamdani inference model for the comparison of specialized IoT systems. The most important criteria that determine the priority of compared platforms are reliability, dependability, safety and security .Ilieva et al. have presented a new methodology combining several MCDM methods for cloud service choice in a fuzzy environment. According to this study, service functionalities, customer support and rating and security are the most important factors influencing cloud systems evaluation. To demonstrate the effectiveness of the pro-posed procedure, the authors have solved the problem of cloud storage analysis using the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method via triangular fuzzy numbers .Chakraborty et al. have evaluated nine cloud service providers using the Distance-Based Approach (DBA) with triangular fuzzy values of fourteen evaluation criteria  .(1)Often, criteria systems include only few technical characteristics of IoT systems; however, evaluation should also be a function of many other factors; for example, social and organizational parameters;(2)If IoT system assessment depends on qualitative factors, then their estimates are often subjective and should be made by a team of experts and fuzzy numbers;(3)The majority of the proposed fuzzy multi-criteria solutions implement only one or two MCDM methods.Although there have been many studies recently concerning the IoT system selection problem, there is no universal approach or unified procedure for its solution. The studies described above provide some insights on the comparison of IoT systems, but they do also demonstrate some drawbacks:To overcome these shortcomings, in this manuscript we propose a new fuzzy methodology for IoT solutions’ evaluation. The selection of an appropriate IoT system according to the needs of a given agricultural company is a complex procedure, depending on many factors. The group multi-criteria approach has the potential to solve this multi-factor problem. Alternatives include a variety of IoT  systems available on the market. The solution to the problem is the system that best meets the preferences of a particular farm.The aim of this study was to create a methodological framework for the multi-criteria management of the procedure for the selection of an IoT system in a fuzzy environment with a focus on agriculture companies.(1)present the most widely used IoT platforms;(2)explore the impact of multi sensors IoT systems on agricultural companies;(3)develop a conceptual framework for the multi-criteria analysis of an IoT system;(4)create a modification of Multi-Attribute Border approximation Area Comparison (MABAC) for an intuitionistic fuzzy environment;(5)verify the proposed framework and the new method through a practical example.The tasks of this study are as follows:The problem of finding the most suitable IoT system can be solved using different approaches, but our study is limited to the decision analysis methods.In order to make an informed decision, decision-makers need to have relevant information  for the preliminary assessment of the need for an IoT system, the formulation of user requirements and the calculation of weighting coefficients and comparison index.In the new framework, only IoT systems for which the necessary information  to build a decision matrix is provided can be evaluated.The limitations of our study are as follows:The rest of this article is organized as follows: In Smart farming represents the applications of IoT through a variety of autonomous devices connected to each other via the global network. By using intelligent sensors, drones, robots and agricultural machinery, farmers inspect and supervise the agricultural processes, from planting, growing and dispatching to the storage and distribution of crops ,28.However, managers and other stakeholders in agribusiness are still not familiar with the capabilities of IoT technology in the remote monitoring of a huge amount of data and controlling physical objects in real-time without human interference. In the next section, the basic characteristics of IoT technology are presented. Then, the most widely used IoT systems are compared and applications of IoT in agriculture are analysed.According to the NIST definition, IoT is a network of devices that contains hardware, software, firmware and actuators which allows the devices to connect, interact and freely exchange data and information ,30,31. TDuring the fourth revolution in agriculture, farms must adopt new digital technologies in order to be competitive. However, the digital transformation through IoT poses three main technological and business challenges.The first one is the growing demand for connectivity. The proliferation of 5G stimulates the need for communication between an increasing number of sensors. A possible solution to the server overload problem is the deployment of blockchain storage in decentralized computer networks.The next challenge is related to device security and data privacy. The threat of unauthorized access to data or the replacement of a device’s instructions requires manufacturers to continuously improve IoT protection and reliability .The third challenge is the lack of standards unifying the requirement specifications in the manufacturing of IoT components. The multitude of devices from different manufacturers using various communication protocols hinders the portability, interoperability and manageability of IoT systems. Several standardizing bodies have already established some important standards for seamless communication between different IoT systems and among entities within an IoT system . UnfortuIoT technology connects a variety of objects to the Internet, uniting the virtual and physical worlds. This merging of the two worlds is a prerequisite for the more efficient usage of available resources. IoT minimizes human efforts because devices can connect and communicate directly with each other and perform various tasks autonomously. Existing security, data protection and interoperability challenges can be resolved through the active engagement of stakeholders.From a cloud technology point of view, IoTs platforms connect devices and applications within a cloud ecosystem. They are also known as IoT Platforms as a Service (PaaS), i.e., by using these platforms, users create their own applications and services employing built-in IoT features. An IoT application usually includes the connecting of remote devices through the cloud, collecting data from devices, remote control, analysis and visualization and the management of application versions and user access rights. This section briefly describes the main features of the most widely used IoT platforms .https://aws.amazon.com/iot/, accessed on 1 March 2022) is a managed cloud service that connects IoT devices and interacts with cloud applications and other devices. The platform supports a large number of devices, processes and messages, and routes those messages reliably and securely. AWS IoT users create their own solutions with a combination of over 60 AWS cloud and edge services. Common service elements across all deployment models are AWS IoT Greengrass, Amazon Monitron and AWS Panorama. AWS also offers a portfolio of capabilities for edge-centric computing that includes intelligence and inference closer to monitored events. Unfortunately, edge cloud and on-premises deployment models offer fewer capabilities, especially relating to data management and analytics , here λ = 0.5  is the most suitable for AF company according to the given requirements.The obtained ranking by using new intuitionistic fuzzy MABAC is as follows:L2 discrimination measure [Step 7. In order to check the reliability of proposed model and the consistency of the results produced by intuitionistic fuzzy assessments, the same problem is solved by using crisp SAW, crisp MABAC and IFNs MABAC with  measure   and 1.0 , 0.6  assign it to the leading group, while alternatives A4 (IBM Watson IoT) and A5 (Oracle IoT) falls into the second part of the ranking  ,26, Lin The comparison with results obtained from similar previous studies shows the following:The proposed framework systematizes common rules and procedures for group multi-criteria selection of IoT platforms. The new framework ensures feasible solution using users’ needs and avoiding subjectivism in experts’ opinions. It facilitates the construction of complex indices for systems evaluation, including technical specifications, key performance indicators and metrics for the sustainability of IoT ecosystems. Furthermore, it is flexible and timesaving, reducing the possibility of errors while preparing relevant input data for each step of the decision-making process. Unlike previous similar studies, the new framework could implement multi-criteria analysis in different fuzzy environments .The process of determining the best suitable IoT platform depends on many factors, , i.e., it is in fact a multi-criteria decision-making problem. This study outlines a multi-criteria framework for IoT platform selection in a fuzzy environment. In the proposed framework, a new modification of Multi-Attribute Border approximation Area Comparison (MABAC) method with specific similarity measure via intuitionistic fuzzy values has been presented as a decision analysis method. The new technique is more precise than existing crisp and fuzzy analogues, as its (1) calculations include the three semantic components of intuitionistic fuzzy numbers and (2) distance formula takes into account the relationship between cross-evaluation of membership (truth) degrees as addition to the difference between main intuitionistic components. The effectiveness of the new decision-making framework has been verified through an illustrative example of ranking IoT platforms.(1)The comparison is based on a complex index that covers technological and business requirements of a team of experts and consumer preferences;(2)MCDM methods have the capability to handle vague and uncertain estimates of both cost and beneficial criteria via fuzzy values;(3)The proposed evaluation system is flexible. It can be further extended with additional decision-making algorithms and adapted to other organizations or sectors;(4)The weighting coefficients and decision matrix are determined by a group of experts familiar with the company’s business processes and IoT technology.The new framework automates the process of ranking of IoT systems and has several advantages:In addition to membership and non-membership parameters, a hesitancy degree is included;The difference of maximum of the cross-evaluation factor and the difference of minimum of the cross-evaluation factor also participates in the calculation.The advantage of the proposed fuzzy modification of MABAC method is that the distance between compared alternatives is calculated by an improved formula for similarity in intuitionistic environment:The validity of the new framework is demonstrated using a practical example for the selection of IoT platforms. The problem is to find the best ranking alternative from five IoT platforms  according to twenty-one criteria for comparison. The analysis of obtained results shows that the proposed methodology is reliable and correctly reflects user’s requirements.In the future, the theoretical framework will be improved by aggregating several rankings obtained through different multi-criteria methods using meta-methods. Additionally, the proposed mechanisms for the ranking of IoT alternatives will be expanded to address uncertainty of estimates with different types of fuzzy sets . We also have a plan to develop new hybrid methods for IoT systems’ evaluation combining weights determination algorithms with multi-criteria decision-making methods.


The selenium (Se) applications in biomedicine, agriculture, and environmental health have become great research interest in recent decades. As an essential nutrient for humans and animals, beneficial effects of Se on human health have been well documented. Although Se is not an essential element for plants, it does play important roles in improving plants’ resistances to a broad of biotic and abiotic stresses. This review is focused on recent findings from studies on effects and mechanisms of Se on plant fungal diseases and insect pests. Se affects the plant resistance to fungal diseases by preventing the invasion of fungal pathogen through positively affecting plant defense to pathogens; and through negative effects on pathogen by destroying the cell membrane and cellular extensions of pathogen inside plant tissues after invasion; and changing the soil microbial community to safeguard plant cells against invading fungi. Plants, grown under Se enriched soils or treated with Se through foliar and soil applications, can metabolize Se into dimethyl selenide or dimethyl diselenide, which acts as an insect repellent compound to deter foraging and landing pests, thus providing plant mediated resistance to insect pests; moreover, Se can also lead to poisoning to some pests if toxic amounts of Se are fed, resulting in steady pest mortality, lower reproduction rate, negative effects on growth and development, thus shortening the life span of many insect pests. In present manuscript, reports are reviewed on Se-mediated plant resistance to fungal pathogens and insect pests. The future perspective of Se is also discussed on preventing the disease and pest control to protect plants from economic injuries and damages. AlthougSeO42−) , and it SeO42−) , which pSeO42−) . FurtherSeO42−) . Since tPlant resistance to diseases refers to the characteristic or ability of plants to prevent the establishment of diseases ensued by the pathogens . There a-1) in the soil significantly enhanced the microbial diversities and the relative abundance of plant growth promoting rhizobacteria (PGPR). The bioconcentration of Se in plant tissues and the improvement of microbiome diversities are related to the enhancement of plant resistance to pathogen infection, showing that the Se content in the soil could indirectly affect the occurrence and transmission of soil-borne diseases  significantly increased the number of mesophyll cells (-1 and 0.5 mg kg-1) in soil, the degree of mitochondrial permeability transition pore was significantly decreased after inoculation with S. sclerotiorum, indicating that Se could be helpful in maintaining plant cell structures (-1 and 0.5 mg kg-1) also significantly reduce the lesion diameter and the incidence of sclerotinia stem rot caused by S. sclerotiorum due to improving the defense ability and antioxidant capacity of rape leaves  increased the activities of antioxidant enzymes such as catalase, polyphenol oxidase and peroxidase in plants. Particularly, Se leads to the up-regulation of defense genes including CHI, ESD1, NPR1, and PDF1.2 in rapeseed leaves (2) can inhibit the extension of F. graminearum in wheat ears and also reduce the percentage of diseased spikelets  could effectively inhibit the invasion and extension of A. solanacearum on pepper and tomato leaves pre-infected by A. solanacearum  can improve plant reproductive success by reducing the herbivore load of a focal plant while increasing herbivory on competitors . Often, S. exigua and influences their selection of plants and feeding tissues or sites , and both chemical and morphological plant defenses mediate antibiosis . When plS. exigua into the pupal and adult stages .The future research on Se and plant immunity needs to focus on mechanisms regarding the beneficial and toxic properties of different chemical forms of Se in plants. The practical exposure and dose ranges of Se on different fungal pathogens and insects also need to be well determined. The plant Se tolerance in relation to the biological characteristics of pathogens and phytophagous insects should be addressed when determining effects of different Se concentrations in different chemical forms. Previous studies primarily focused on fungal diseases, with only a few on bacterial and/or viral diseases. One might speculate that the effects of Se on bacteria and viruses would be similar to the effects of Se on fungi in plants, providing a research hypothesis that needs to be tested in future research. Due to potential biomagnification of Se through food chains, it may also be important to carefully monitor the Se accumulation in insects to ensure ecological safety during pest control particularly with Se-biofortified crop production.QL and TL conceived the conceptual idea. QL wrote the initial draft of the paper. L-MX collected or retrieved research publications or inline the useful literature. LY, ZL, XC, JW and TL revised and finalized the manuscript. All authors contributed to the article and approved the submitted version.


Digitalization is an excellent opportunity for the manufacturing industry all over the world to improve the core competitiveness and break through the “low-end locking” dilemma. However, it is not clear whether the digitalization of the manufacturing industry has positive ecological and environmental benefits under the resource and environmental constraints. To answer this question, we use the data from the world input–output database (WIOD) to investigate the impact of manufacturing input digitalization on carbon emission intensity by an extended analysis. The results show that the input digitalization of the manufacturing industry has mixed effects on reducing carbon emission intensity. The productive input digitalization can reduce carbon emission intensity, but the distributional input digitalization may increase carbon emission intensity. Non-pollution-intensive manufacturing and high-input digital manufacturing have stronger carbon emission reduction effects than the other industry sectors. From the perspective of input sources, input digitalization from domestic sources has a significant inhibitory effect on the carbon emission intensity. In contrast, input digitalization from foreign sources may increase carbon emission intensity. Since the 1990s, the digital economy has gradually become an important driving force for economic transformation and upgrading in developed countries, such as the United States and countries in Europe. The business, transportation, trade, and other economic modes have undergone revolutionary changes ,3. DigitIn the face of increasingly severe resource and environmental constraints, major economies have recognized the importance of green development. The United States proposes to achieve ‘net-zero emissions’ by 2050. The European Union has submitted the European Climate Law, which aims to legally ensure that Europe becomes the first ‘climate-neutral’ continent by 2050. China plans to achieve carbon peaking in 2030 and carbon neutrality in 2060. China’s extensive economic growth model comes at the expense of an environmental overdraft and ecological damage. At present, China is one of the countries with the highest carbon emissions in the world, of which industrial emissions account for about 68% of the total emission. However, energy requirement is still increasing and carbon emissions have not yet peaked. The realization of the structural transformation of the manufacturing industry and the transformation of low-carbon energy are urgent problems that China needs to solve currently .2 emissions by expanding the economic scale of tertiary industries, reducing the proportion of coal consumption, and promoting green technology innovations [Some scholars have explored the relationship between the digital economy and environmental problems ,6. The rovations . HoweverIn this paper, the input digitalization of the manufacturing industry is defined as the value of the goods and services of all digital assets consumed and converted by all manufacturing industries in the process of producing or providing goods and services in a certain period . The input digitalization is classified into two types: productive input digitalization and distributive input digitalization. Productive input digitalization refers to the integration of digital technology and manufacturing technology. The digital input consumed in the production links the support of virtual reality, computer network, and other support technologies. Distributive input digitalization refers to the digitalized investment of the manufacturing industry in allocating resources, such as final goods or services, and the digitalized cost of goods in Internet retail. The key issue is the impact of manufacturing input digitization on the reduction of carbon emission intensity. In addition, there is a strong need to identify the types of input digitalization of the manufacturing industry that may have consistent impacts on carbon intensity, to find the transmission mechanisms of manufacturing service that affect carbon emission intensity, and to explore the characteristics of carbon emission reduction effects of input digitization of manufacturing in different industries and in different countries.To answer these questions, we use WIOD input–output table and environmental ac-count data to calculate the input digitalization levels and carbon emission intensities of 17 manufacturing industries in China from 2000 to 2014. The fixed effect model is used to analyze the various impacts of productive input digitalization and distributive input digitalization on carbon emission intensity. This paper also carries out extensive analysis of the data, including the mechanism test of manufacturing input digitalization on carbon emission intensity, as well as the heterogeneity analysis of the manufacturing industry and the input digitalization sources.(1)Based on the latest achievements of manufacturing input digitalization as the reference , we use (2)We estimate the impact of different types of input digitization on carbon emission intensity and discuss the different roles of productive input digitalization and distributive input digitalization.(3)We include the cross-terms of input digitization and other variables into the model. The mechanisms of manufacturing input digitalization affecting carbon emission intensity are analyzed.(4)Multi-angle heterogeneity analysis is conducted, such as the impact of input digitization on the carbon emission intensity of different manufacturing industries and the impact of input digitization from different economic sources on carbon emission intensity. Finally, the conclusions and policy recommendations are targeted.The study of these issues will have not only substantial theoretical values, but also application significance for China and other developing countries to achieve both high-quality economic development and carbon emission reduction commitments. The potential contributions of this paper include the following aspects:The rest of this paper is organized as follows. Digital industrialization is the process of digital upgrading, transformation, and reengineering of all elements in the upstream and downstream of the traditional industrial chain, empowered by the data. Research studies have been conducted in recent years regarding the environmental effects of industrial digitalization, but derived different conclusions.Some studies propose that digital industrialization has positive impacts on carbon emissions. Industrial upgrading and technological progress have positive effects on stimulating the reduction of carbon emissions. For example, the application of wireless technology reduces the demand of physical office space for manufacturers. The popularity of a remote office mode has dramatically reduced the commuting demands related to work and shopping, which is conducive to decrease energy intensity . The advHowever, digital industrialization may also have negative impacts on carbon emissions. The indirect energy plundering effect, caused by economic expansion and profit maximization, can be considered as a substitution effect. The integrated development of the digital economy and traditional industries will increase the revenues of production sectors. Driven by the increasing profits, enterprises tend to choose the continuous expansions of the production scale. Simultaneously, the scale of energy demand expands rapidly and the total carbon emissions also rise . The negIn summary, the impact of digital industrialization on carbon emissions mainly concentrates on the inhibition of income effects and the growth of substitution effects . The mecThe initial studies of carbon emissions from manufacturing focused on the perspective of international trade on the global carbon emissions, which gave rise to the “pollution paradise” hypothesis ,24,25,26In recent years there have been substantial research interests to analyze the environmental impact of manufacturing servitization by taking the subdivisions of the manufacturing industry as the research object. Relevant enterprise case studies show that service-oriented manufacturing enterprises can reduce resource consumption. Overall, this subdivision is beneficial to the sustainable development of the environment ,33. The The research on carbon emissions in China’s manufacturing industry mainly includes carbon emission decomposition and analysis of influencing factors. If we consider the total amount of carbon emissions only, the main factors affecting carbon emissions are energy intensity, energy structure, and economic growth . HoweverThe development process of developed countries is generally followed by industrialization and then informatization. China needs to take into account informatization while developing industrialization. Today, the international division of labor is no longer limited to the manufacturing industry, but has rapidly expanded to the digital fields . DigitalUnder the framework of the division of the global labor and value chain, the relevant research studies concentrate on the levels of manufacturing enterprises and sectors. The results show that the level of input digitalization can affect the participation and status of the global value chains. On the enterprise level, scholars have theoretically discussed the mechanisms of digitalization in the upgrade of the enterprise value chain, reduction of transaction costs, and improvement of operational efficiency . A few qAlthough these theoretical and empirical studies have investigated the economic effects of manufacturing input digitalization, it is imperative to investigate the ecological and environmental benefits of manufacturing input digitalization. To address this issue, this study uses the input–output method to investigate the impact of input digitalization of manufacturing on carbon emissions and the corresponding transmission mechanisms.Based on the environmental pollution and supply model , we intrAccording to the measurement method for China’s regional carbon emission intensity , the totIn this paper, the digitalization of manufacturing is defined as the degree of intermediate digitalization input in the manufacturing industry, which comes from the industries containing digital economic elements. Based on the International Standard Industrial Classification (ISIC Rev4.0) of the United Nations Statistics Agency, we use the digital economy elements of the manufacturing industry to define the digital related industries of intermediate input sources  is defined by the ratio of energy consumption of each manufacturing industry to the output value of this industry.Energy consumption intensity (ECS) is the ratio of non-fossil energy consumption of each manufacturing industry to the energy consumption of this industry.Energy consumption structure (INDS) is expressed by the ratio of the output value of each industry to the total output value of the manufacturing industry. It is used to control the scale effect of the manufacturing industry.Industry scale (CLR) gives the ratio of the total value of fixed assets of each manufacturing industry to the total number of employees in this industry. It is used to control the structural effect of the manufacturing industry.Capital labor ratio (RDS) is the proportion of the internal R&D expenditure of each manufacturing industry in the total output value of this industry. It is used to control the technological effect of the manufacturing industry.R&D expenditure (FDI) is expressed by the proportion of the total industrial output value of foreign investment and Hong Kong, Macao, and Taiwan investment in the total output value of this industry. It is used controls the scale, structure, and technology effects of FDI spillovers to host countries.Foreign direct investment (ERI) are also important, as enterprises are likely to increase pollution control expenditure and reduce pollution emissions due to environmental regulations issued by the government. This variable controls the binding effect of social responsibility on the manufacturing industry. There are three mainstream accounting indicators of environmental regulation: pollution discharge cost, pollution emission intensity, and the ERS comprehensive index [Environmental regulation  adopts the International Standard Industry Classification (ISIC Rev. 4), while China’s manufacturing industry classification is based on the National Economic Standard Industry Classification, these two classification systems are different. Therefore, we integrate the research objects into 17 manufacturing industries according to the two systems, as shown in The original data for measuring the digital level of manufacturing inputs come from the input–output table of WIOD (2016), which provides the inter country input and output data of 56 industries between 44 major countries from 2000 to 2014. The data required for carbon emission intensity calculation are from the WIOD environmental account data. The original data used for control variables are from the All data related to the gross industrial output value, fixed assets and other currencies are deflated using the relevant price index with the base period of 2000, to obtain constant price data. Some missing data are processed by interpolation or moving average. The sample data cover 17 manufacturing sectors in China from 2000 to 2014. The statistical description of the variables explained in We first qualitatively analyze the characteristics of manufacturing input digitalization and carbon emission intensity. The benchmark regression results in lnDigital is significantly negative, indicating that the input digitalization of manufacturing has significantly reduced the carbon emission intensity. Columns (3)–(8) consider different types of input digitalization. Columns (3) and (4) show that the coefficient of lnPRODIG is negative, indicating that productive input digitalization is conducive to reducing carbon emission intensity. Interestingly, columns (5) and (6) show that the coefficient of lnDISDIG is significantly positive, suggesting that the distributive input digitalization may aggravate the carbon emission. The reason may be that the digitalization of distribution is difficult to replace the traditional labor and material resources, and the development of this new model requires more energy consumption. The results of digital segmentation show that different types of input digitalization have different impacts on the carbon emission intensity. For comparative analysis, columns (7) and (8) include lnPRODIG and lnDISDIG in the measurement model, respectively. The results show that the digitalization of productive input and digitalization of distributive input have opposite effects on the carbon emission intensity of the manufacturing industry.Columns (1) and (2) in The application cost of productive digital technology is low, which positively affects carbon emission reduction in the manufacturing industry, significantly. However, the transformation of distributive input digitalization, such as internet trade and online retail, has high transport costs and is easily restricted by digital trade barriers, which leads to an increase in the carbon emission intensity. Since distributive digitalization accounts for a low proportion of overall input digitalization, the comprehensive impact of input digitalization on reducing carbon emission intensity is still positive. The estimated results of control variables through statistical tests are consistent with expectations. However, the effects of some control variables on carbon emission intensity are not robust. The reason may be that the two types of input digitization have different impacts on carbon emission intensity.lnDigital, lnPRODIG, and lnDISDIG are consistent with those of 2000–2008. Compared with the results in Since advanced countries were greatly affected by the 2008 financial crisis, their input digitalization investment in China’s manufacturing industry were changed substantially. The trend of China’s manufacturing carbon emission intensity has also changed. Therefore, two sample time periods, namely 2000–2008 and 2008–2014, are selected to estimate the impact coefficient of input digitalization on carbon emission intensity, to verify the robustness of the model conclusions. Columns (1)–(4) in L.lnDigital, lagged by one period , is used as the instrumental variable of manufacturing input digitization lnDigital . Then we estimate the impact coefficients on carbon emission intensity by a two-stage least square method (2SLS).Manufacturing industries with a lower carbon emission intensity may be more technologically mature and have low production cost for digitalization, making it easier to promote industrial digital transformation. Therefore, there may be endogenous problems caused by the reverse causality between input digitization and carbon emission intensity. The input digitization level p-value of 0, which significantly rejects the null hypothesis of “insufficient recognition of tool variables” at higher levels. The results suggest that the estimation results all significantly reject the null hypothesis of insufficient identification and weak instrumental variables. The coefficients of input digitization and productive input digitization are significantly negative, and the coefficients of distributive input digitization are significantly positive. After considering possible endogenous problems, the core conclusion of this paper is still robust.(1)Scale effect: increase the industry scale of the manufacturing industry and absorb more digital elements.(2)Structural effects: improve the substitution of capital  for labor factors and optimize the industrial structure.(3)Technological effects: increase manufacturing R&D spending and upgrade low-carbon technology levels.(4)Spillover effects: increase the proportion of foreign direct investment and increase the technological and human spillovers of multinational enterprises to the host country (China).(5)Binding effects: strengthen the monitoring of environmental regulation and strictly restrain the negative impact of the manufacturing industry on the environment.Next, we discuss the mechanisms by which manufacturing invests in digitization to reduce carbon emission intensity. Referring to the previous analysis, the following approaches may help reduce carbon emission intensity:lnINDS), capital-labor ratio (lnCLR), R&D expenditure (lnRDS), foreign direct investment (lnFDI), environmental regulation (lnERI) and core variables  in the benchmark regression model, to examine the path of input digitalization to reduce carbon emission intensity. The principle of this test is that if the sign of the test result for lnDigital is negative and the sign of the cross item is positive, it means that this effect mechanism has a more significant role in reducing carbon emission intensity.To verify the above effects, we add manufacturing industry scale  is a mechanism test for structural effects. The coefficients of lnDigital and the cross-term lnCLR × lnDigital are significantly negative, indicating that the higher the proportion of capital factors, the more important the role of input digitalization in reducing carbon emission intensity. Thus, the results verify the structural optimization path for the effect of investment in digitalization to reduce carbon emission intensity.lnDigital is significantly negative, and the coefficient of the cross-term lnCLR × lnDigital is negative but not significant, which is inconsistent with the expected results. The possible reason may be that the current industrial digitalization is still in the immature stage of rapid development. R&D investment in new digital technologies has increased significantly, which requires more production resources and energy input, thus triggering an increase in carbon emissions. However, as the application scale of newly developed digital technologies continues to expand and the degree of application continues to deepen, more and more traditional production factors will be replaced by digital factors, playing a positive role in reducing carbon emission intensity. The offset of the above two effects may suggest that the mechanism of input digitalization in reducing carbon emission intensity through technical effects is not significant. Columns (4) and (5) are the mechanism test results for the spillover effect of foreign direct investment and the restrictive effect of environmental regulation. The coefficients of lnDigital and the cross-terms lnFDI × lnDigital and lnERI × lnDigital all are significantly negative, indicating that digital investment can be achieved by increasing direct foreign investment, or that enhanced environmental regulation can promote carbon emission reduction.Column (3) shows the mechanism test results for technology effects. The coefficient of Due to the difference in product types, development models and technical levels among manufacturing industries and the degree of environmental pollution are also different. Therefore, input digitalization may have a heterogeneous impact on the carbon emission intensity of different manufacturing industries. This subsection will explore this heterogeneity.Clean, where Clean = 1 represents the non-pollution-intensive manufacturing industry, and the pollution-intensive manufacturing industry (Clean = 0) is the benchmark group. We introduce the cross-item Clean × lnDigital  to analyze the differential impact of input digitalization  on the carbon emission intensity of manufacturing industries with different pollution intensities. The results are shown in columns (1)–(3) of The manufacturing industry is separated into two types: pollution-intensive manufacturing and non-pollution-intensive manufacturing, according to the degree of industrial pollution . We set a dummy variable lnDigital is significantly negative, indicating that input digitalization can reduce the carbon emission intensity of pollution-intensive manufacturing. The coefficient of Clean × lnDigital is significantly negative, meaning that the input digitalization of the non-pollution-intensive manufacturing industry has a more substantial inhibitory effect on carbon emission intensity. For every 1% increase in input digitalization, the carbon emission intensity of pollution-intensive and non-pollution-intensive manufacturing will decrease by 0.390% and 0.550%, respectively.Column (1) of lnPRODIG is also significantly negative, indicating that productive input digitalization can reduce the carbon emission intensity of pollution-intensive manufacturing. The coefficient of the cross-item Clean × lnPRODIG is positive but not significant, indicating that the productive input digitalization in the two manufacturing industries has no significant difference in the inhibition of carbon emission intensity.Column (2) shows that the coefficient of lnDISDIG is significantly positive, indicating that distributive input digitalization will increase the carbon emission intensity of the pollution-intensive manufacturing industry. The coefficient of Clean × lnDISDIG is significantly positive, indicating that the distributive input digitalization in non-pollution-intensive manufacturing increases carbon emission intensity even more than pollution-intensive manufacturing. The carbon emission intensity of the pollution-intensive manufacturing industry and non-pollution-intensive manufacturing industry will increase by 6.510% and 8.417%, respectively, for each 1% increase in the distributive input digitalization.However, column (3) shows that the coefficient of High, where High = 1 represents the high-digital-input manufacturing industry, and High = 0, the low-digital-input manufacturing industry, as the benchmark group. The cross-term High × lnDigital  is introduced to investigate the differential impact of input digitalization  on the carbon emission intensity of the manufacturing industry at different input digitalization levels. The results are shown in columns (4)–(6) of According to the median of the input digitalization level of each manufacturing industry in 2014, we classify the manufacturing industries that are above this median into high-digital-input manufacturing industries, and low-input digital manufacturing for the rest of the manufacturing industry . We set a dummy variable lnDigital is significantly negative, and the coefficient of the cross-term High × lnDigital is also significantly negative, indicating that the input digitalization of the high-digital-input manufacturing industry has a more substantial inhibitory effect on carbon emission intensity. For every 1% increase in input digitalization, the carbon emission intensity of the high-digital-input and low-input digital manufacturing industries will decrease by 0.540% and 0.389%, respectively.Column (4) of lnPRODIG and High × lnPRODIG are significantly negative, indicating that the productive input digitalization in these two types of manufacturing industries has a significant difference in the suppression of carbon emission intensity. The productive input digitalization in the high-digital-input manufacturing has a more substantial inhibitory effect on carbon emission intensity than the low-digital-input manufacturing. The carbon emission intensity of the high-digital-input manufacturing industry and low-digital-input manufacturing industry will decrease by 0.952% and 0.790%, respectively, for every 1% increase in productive input digitalization.Column (5) shows that the coefficients of lnDISDIG and the cross-item High × lnDISDIG are significantly positive, indicating that distributive input digitalization will increase the carbon emission intensity of high-digital-input manufacturing. The carbon emission intensities of low-digital-input and high-input digital manufacturing will increase by 5.414% and 7.745%, respectively, for each 1% increase in distributive input digitalization. The possible reasons may be that China’s manufacturing industry, which has a high degree of digital investment, is currently at a critical stage of tackling digital transformation and distribution, together with the existence of digital trade barriers, and the lack of ability to reduce energy consumption per unit of product.However, column (6) shows that the coefficients of Considering the absorption and transformation of digital elements from domestic and foreign sources in the manufacturing industry, the impact of input digitalization on carbon emission intensity may be different.DDIG) and foreign input digitalization rate (FDIG) of manufacturing. We divide the multi-regional input–output table provided by WIOD into two parts: China’s own input from domestic sources and input from other countries. Then we calculate the domestic input digitalization rate  and (2). The absolute values of the estimated coefficients of lnDDIG and lnFDIG decrease significantly. Every 1% increase in domestic input digitalization will reduce the carbon emission intensity by 0.745%, but every 1% increase in foreign input digitalization will increase the carbon emission intensity by 3.775%. The possible reasons for this heterogeneous result may include the following two aspects: on the one hand, China has an enormous potential for carbon emission reduction, and the digital economy is developing rapidly. The domestic digital elements are abundant and easy to obtain, and manufacturing is in a rapid digitalization process. The reform combined with energy structure, low-carbon technology, and other aspects can significantly promote manufacturing carbon emission reduction. On the other hand, introducing digital technology from other economies requires more cross-border transport costs. Due to the digital trade barriers, China’s ability to trigger a change is relatively limited. It is not as good as the domestic input digitalization, that can bring obvious carbon emission reduction dividends. It may even hinder the process of carbon emission reduction in the manufacturing industry to a certain extent.Column (3) includes (1)The digital level of China’s manufacturing investment is on the rise, while the carbon emission intensity declines simultaneously. Both were affected by the financial crisis in 2008, and the trend variated. From the attribute of input digitalization, the proportion of productive input digitalization in the manufacturing industry is relatively high. In contrast, the proportion of distributive input digitalization is relatively low.(2)The improvement of manufacturing input digitalization can have a significant positive impact on carbon emission reduction. Among them, the productive input digitalization has a positive role in promoting carbon emission reduction in manufacturing. In contrast, the distributive input digitalization has a negative role in inhibiting carbon emission reduction in manufacturing. This conclusion is robust after changing the sample time period and dealing with sample endogeneity .(3)The mechanism analysis results show that the input digitalization promotes the carbon emission reduction process by exerting the scale, structure, spillover, and constraint effects. In contrast, the emission reduction effect is not apparent through new digital technology.(4)The industry heterogeneity analysis shows that input digitalization plays a significant role in reducing the carbon emission intensity of non-pollution-intensive manufacturing. The input digitalization of manufacturing with a higher degree of digitalization has a corresponding stronger inhibition on carbon emission intensity.(5)The analysis of national sources of input digitalization shows that input digitalization relying on domestic sources has a positive role on carbon emission reduction in manufacturing, while input digitalization relying on foreign sources has a negative role in inhibiting carbon emission reduction in the manufacturing industry.To explore the mechanisms for developing the manufacturing industry and maintaining ecological environment, this paper focuses on the emerging industrial development trend of digital investment. From the perspective of input and output, we analyze the digital levels of input and carbon emission intensities of 17 manufacturing industries in China. This empirical study examines the impact of the digital levels of manufacturing investment on the carbon emission intensity. We also explore the mechanisms regarding the influence of input digitalization on the carbon emissions of the manufacturing industry. In addition, we analyze the industry heterogeneity in the digital carbon emission reduction mechanism and the heterogeneity of the impact of input digitization from different countries on carbon emissions. The major conclusions of this study are that:The above conclusions confirm the positive effect of input digitalization on carbon emission reduction. However, two different types of input digitalization have an opposite impact on carbon emissions. The impact of each manufacturing industry on its total carbon emission depends on the balance of these two opposite forces. Since the technology effect of input digitalization is mainly dependent on distributive digitalization, the final comprehensive impact on carbon emission intensity is not significant. Although the impact of distributive input digitalization on inhibiting carbon emission intensity is negative, the conclusions of this study still have important application significance. The reason is that, if we do not realize this problem, the manufacturing industry that blindly relies on distributive digitalization may ignore this issue, which leads to the painful reality that the total carbon emissions will continue to grow.(1)The government should fully seize the opportunity of digital development, break through the bottleneck of core digital technology, cultivate comprehensive digital talents, and strengthen the ability of the manufacturing industry to acquire and apply digital elements.(2)The government should pay attention to the heterogeneity of the impact of input digitalization from different manufacturing sectors on carbon emissions. Different levels of carbon emission reduction targets should be formulated according to the development characteristics of each manufacturing industry. Non-pollution-intensive manufacturing or industries with a high degree of digitalization can achieve more significant environmental effects through digitalization.(3)Manufacturing industries should strengthen independent research studies and development of digital technology. Enterprises should absorb and integrate digital elements according to the industry characteristics to achieve the win-win goal for both the structural transformation of the manufacturing industry, and carbon emission reduction.Based on the above research conclusions, we put forward the following policy recommendations:In the context of the international division of labor and global carbon emission reduction, our study provides quantitative evidence of the impact of digital input on the ecological and environmental benefits of the manufacturing industry. This paper enriches and expands the economic and ecological benefits of digitalization from theoretical and empirical aspects. Finally, it is worth noting that although this work aimed to study the carbon emission reduction of China’s manufacturing industry, the proposed research principles and methodologies can be applied to the study of the impact of input digitization on carbon emission intensity in other countries.Although we have analyzed the impact of input digitalization on carbon emission reduction, there are still many important questions requiring further analysis. In this work we study 17 manufacturing industries in China. However, the enterprise levels are not included in the research framework. It is expected that the study results may show different influences of enterprise levels on carbon emission. Thus, we need to investigate the micro-effects of enterprise levels. In addition, our analysis only draws the conclusion that the digitalization of foreign investment harms carbon emission reduction. We did not discuss the input digitalization heterogeneity from different countries with different levels of national digital economy development, due to the lack of statistical data and measurement indicators. Currently, there is not a uniform measurement method for digital development levels in different countries. The relative levels of input digitalization relying on foreign sources need to be precisely rescaled. Therefore, it is imperative to conduct micro-effect analysis, depict the degree of input digitalization of various countries accurately, and integrate multiple world economies into a uniform analysis framework to make broad and deep conclusions. All these issues will be topics of future research.


Rabies is a global problem and is endemic in India. Rabies cases occur throughout the year, and the majority of cases are associated with dog bites. We report a rabies outbreak investigation in an urban area of Delhi conducted by our multidisciplinary team, and its role in proactively controlling a rabies outbreak by concerted efforts and timely advice to various stakeholders using a “One Health Approach.” A veterinary care NGO from Delhi picked up a suspected rabid stray dog and submitted a brain sample for diagnosis of rabies, as they had received information from a resident of the locality about an unprovoked animal bite incident involving a girl (category III bite) and close contact of two more stray dogs living in the vicinity of the suspected rabid dog. The laboratory diagnosis of rabies in the suspected dog brain sample was confirmed by using Fluorescence Antibody Test (FAT). A multi-expert investigation team with expertise in medicine, microbiology, veterinary sciences, laboratory diagnosis, and public health was constituted to investigate the outbreak. The timely, adequate, and appropriate anti-rabies management initiated for the animal bite victims in this incident could prevent rabies. Proactive involvement of multiple stakeholders and knowledge attributes and practice of local residents could prevent human rabies. As there were no further reports of dog bites from the area, the chain of rabies transmission in that area could be controlled. The presented work is a classical case scenario where concerted efforts of all stakeholders achieved effective control and prevention of rabies by adopting the “One Health approach”.  Rabies virus is a neurotropic virus usually presenting with acute neurological symptoms. Rabies is a global problem and is endemic in India. Rabies cases occur throughout the year, and the majority of cases are associated with dog bites. It has also been reported in a study from the anti-rabies clinic of the tertiary care hospital in Delhi that the majority 91.0%) of animal bite cases belonged to category three, and were due to dog bites from the years 2010 to 2018 [.0% of anRabies cases in dogs and wildlife animals such as deer have been reported in the past from the National Zoological Park in Delhi. Mass mortality of spotted deer was recorded at the National Zoological Park, New Delhi, India, in 2016, due to a rabies outbreak that was laboratory-confirmed by direct fluorescent antibody test (FAT) and nucleic acid amplification test (NAAT) followed by sequencing in our previous study ,4. National reference laboratory for rabies at the Centre for Arboviral and Zoonotic Diseases (CAZD), National Centre for Disease Control (NCDC), is a part of the World Health Organization collaborating centre for rabies epidemiology. It routinely provides referral diagnostic services for rabies in animal bite victims and rabies in dogs/other animals, and contributes to epidemiological and knowledge generation activities pertaining to rabies. We report a rabies outbreak investigation in an urban area of Delhi conducted by our multidisciplinary team, and its role in proactively controlling a rabies outbreak by concerted efforts and timely advice to various stakeholders using a “one health approach”.A veterinary care Non Government Organization (NGO) from Delhi, picked up an alive suspected rabid stray dog in Delhi on 20 February 2022 for diagnosis of rabies, as they received information from a resident of the locality about an unprovoked animal bite incident involving a girl (category III bite), and close contact of two more stray dogs living in the vicinity of the suspected rabid dog.The suspected rabid dog died during the first day of the observation period and the cerebellum part of the brain was submitted to the CAZD, NCDC, New Delhi, for laboratory diagnosis.The laboratory diagnosis of rabies in the suspected dog sample was confirmedby detecting rabies virus antigen on brain impression smears by DFAT, using anti-rabies nucleocapsid fluoroisothiocynate conjugate  performed as per the manufacturer’s recommendation. Other laboratory test done for Rabies in brain sample were detecting rabies-specific RNA in the sample by pan-lyssavirus nested RT-PCR protocol as per the WHO Manual on Laboratory Techniques in Rabies  and RapiPreparation for the field visit. On laboratory confirmation of rabies in a dog sample, all required administrative approvals were obtained from the Director, National Centre for Disease Control, New Delhi, to undertake a field investigation to establish the extent of rabies exposure in animals and humans in the vicinity. A multi-expert investigation team with expertise in medicine, microbiology, veterinary sciences, laboratory diagnosis, and public health was constituted;Verification of the laboratory diagnosis. The team of experts verified the laboratory confirmation of rabies;Orientation of data in terms of time, place, and person. The antecedents of the laboratory-confirmed rabid dog were established. The Veterinary NGO was approached to identify the exact location from where the dog was picked up. The team then prepared a plan to visit the place and interview the locals to understand the extent of rabies exposure to humans and animals. The investigation team visited the site and established that one girl had significant exposure to the laboratory-confirmed rabid dog with a category three bite. An inquiry was made to establish if there were any other cases of significant human exposure in that area during the incident;Evaluation and implementation of control and preventive measures. The investigation team inquired about the rabies control practices for animal bite victims in the area. The exposed girl was immediately taken to an anti-rabies clinic. The public was informed about the importance of rabies prevention and control using the material prepared in the native language;Communication. A detailed report with recommendations was shared with all the relevant shareholders for further public health actions.For the outbreak investigation of the incident, a framework based on the recommendations of Goodman et al.  was usedThe cerebellum part of the brain of the suspected rabid dog was submitted to CAZD on 21 February 2022. The sample tested positive for rabies virus antigen (by FAT and RDT), and rabies virus nucleic acid (by RT-PCR). Based on the test results, the case was classified as a laboratory-confirmed case of dog rabies .Based on the available information, an online review of the site revealed that the place of incidence was a slum cluster surrounded by gated communities. The gated communities had limited access to stray dogs from the street. Therefore, the site visit and interview were limited to the slum cluster area. The slum cluster had 40 households limited to approximately 0.2 square kilometer area . For theOn interviewing, residents informed that the witness of the incident had a shop near the incident site. The area was clean. However, an empty plot with garbage and waste was found near the incident site .The resident who witnessed the incident recalled a significant unprovoked exposure (category III bite) in one case of a 7-year-old girl on the evening of 20 February 2022. The interviewee took the team to the animal bite victim’s (ABV) house. The team visited this ABV’s home and interviewed the girl’s mother. The mother noted that there were multiple deep wounds with profuse bleeding on the girl’s right thigh .ABV’s mother immediately cleaned the wound with Dettol  and water and took her to a tertiary care hospital  in Delhi. She was given anti-rabies serum, tetanus toxoid, and anti-rabies vaccine as per the prescription dated 20 February 2022. The team examined the bite site and observed that the wound was healthy with scabs and granulation tissue. The mother was advised to complete the full vaccination course and visit the CAZD division for anti-rabies antibody titer estimation after 15 days of completion of post-exposure prophylaxis if desired.A group of around 20 residents from the locality who were witnessing the interview were targeted for IEC activities. The residents were informed to have timely, adequate, and appropriate anti-rabies management in case of any other significant exposure from an animal bite in the locality.They were also informed about steps and points to remember for animal bite management and the importance of wound washing with soap and water. IEC material was handed over to the Anganwadi helper (person overlooking the government run childcare centers in India) in that area. The team could locate three more dogs in that vicinity. The residents and animal care givers were also informed to take care of anti-rabies vaccination/birth control in street dogs, keep a note on other dogs who had been in close contact with the suspected rabid dog for behavior change, and were advised to contact Veterinary Hospital, opposite Bhagat Chandra Hospital, below Palam flyover, Palam Dabri Road, Palam, New Delhi for further necessary action if any required.A visit was made to the Deputy Director, Southwest Delhi, Animal Husbandry Department, Govt of Delhi, debriefing the whole incident to him. The team shared the relevant documents with the authorities. Authorities were also requested to coordinate with South Delhi Municipal Corporation, Delhi, for animal birth control and mass vaccination to control dog-mediated rabies in that area.Anti-rabies vaccination camp/animal bite management of other animals with exposure/animal birth control activities:For vaccination in India and several Asian countries, many commercially available rabies vaccines are licensed for post-exposure prophylaxis in animals and may be given as per the 5-dose intramuscular schedule for humans  [As there is no consensus worldwide regarding the isolation/observation period of animals exposed to a suspected rabid dog, the observation period for exposed dogs may vary based on epidemiological information such as previous vaccination status, type of injury, or the fate of the biting animal. The treating veterinarian may recommend euthanasia or isolation for a specific observation period of such animals. The treating veterinarian must follow all the regulatory guidelines for institutional isolation or euthanasia in case of high-risk exposure. Relevant international guidelines may be used as a reference at the veterinarian’s discretion for animal observation or isolation periods. The recommended period of isolation for dogs in cases of significant exposure to a human being is 10 days . After sAnimal Birth Control (Dogs) Amendment Rules 2010 [Prevention of Cruelty to Animals Act, 1960 [A target of vaccinating 70% of the canine population in the area may be achieved to minimize dog-mediated rabies . For aniles 2010  under thct, 1960 .In view of the above, the Veterinary/Municipality department may immediately enhance the anti-rabies vaccination camp/animal bite management of other animals with exposure/animal birth control activities if changes in the behavior of stray dogs or an increase in animal bites are reported in the area.2.IEC ActivitiesMulti stakeholders team including human health authorities, veterinary authorities, sanitation and municipality authorities, animal activists, public and civic bodies, NGOs etc., may be formed to carry out an IEC campaign for resident to address the knowledge gaps which may help to minimize such animal-to-human and animal-to-animal bites in future.In areas where animal bite incidences are being increasingly reported in humans, the IEC campaign may focus on the importance of wound washing with soap and water followed by timely and appropriate management of category III animal bite victims by postexposure prophylaxis measures .3.Environmental cleanliness and solid waste management Residents may inform the health authorities/animal welfare authorities/sanitation and municipality if incidences of animal-to-animal bites or stray dog menaces increase in the area. One of the main reasons for the increase in stray dog numbers is the inappropriate waste disposal methods, especially the remaining food to which stray dogs naturally get attracted. Environmental cleanliness and solid waste management  should be enhanced, which is a shared responsibility of citizens and the Municipality Department.4.Monitoring the efficacy of the anti-rabies vaccineThe parenteral anti-rabies vaccines available commercially for humans and animals are quite efficacious if administered as per schedule. The anti-rabies titer estimation for humans and canines who have received post-exposure prophylaxis is usually not recommended if the vaccination schedule is strictly followed.The treating physician or veterinarian may recommend anti-rabies antibody titer estimation given a deviation of schedule, default in route or dose prescribed for vaccination, immune status of the vaccinated individual (immune tolerant or immunocompromised), or probability of repeated occupational exposures.In India, an existing network of referral government laboratories performs anti-rabies antibody titer estimation either freely or on nominal charges . Testing5.Pre-exposure and Post-exposure ProphylaxisPeople in the area who are at risk of repeated exposure, for example, animal caregivers providing food to stray dogs, sanitary workers, etc., are advised to ensure that they receive pre-exposure anti-rabies prophylaxis as per the national guidelines.Health authorities should ensure uninterrupted and adequate supply of Anti-rabies vaccine and serum at all times in designated Anti-rabies clinics and health care facilities.This manuscript reports a rabies outbreak in an urban setting in Delhi, India. A suspected rabid dog had bitten a child. The dog was picked up by an NGO and the dog died during the first day of the observation period. The brain sample of the dog was submitted for laboratory confirmation to CAZD, NCDC, Delhi. An outbreak investigation was initiated after the laboratory confirmation of rabies to assess the spread of rabies in and around the area. This work highlights the role of proactive involvement of multi-stakeholders and knowledge attributes practices of residents in preventing human rabies. The timely, adequate, and appropriate anti-rabies management initiated for ABV in this incident prevented rabies. It has been reported that animal bite cases are increasing over the years and the most commonly reported animal bites in Delhi are dog bites. Therefore, there is a need to implement IEC activities and carry out awareness programs among people regarding animal bite management . The “OnCoordination and communication of health professionals play an essential role in the overall success of strategies for controlling and preventing rabies ,18,19. AApart from technical aspects of inter-sectoral cooperation, the successful application of “One Health” approaches also requires the development of nontechnical expertise and collaborations such as advocacy, knowledge translation, evidence-based decision making, and capacity development . ThroughAs humans, domestic animals, and wildlife territories are in close proximity, coordination of all sectors is equally essential to execute a successful plan. It has been found that mass vaccination of canines (both stray and pet) at a common site and a combined approach of vaccination and dog population management are important strategies in rabies control . TherefoAs there were no further reports of suspected rabies from the area, the chain of rabies transmission in dogs could be controlled. The presented work is a classic case scenario where concerted efforts by all stakeholders achieved effective control and prevention of rabies by adopting the “one health approach”.


Background: Heart failure represents a life-threatening progressive condition. Early diagnosis and adherence to clinical guidelines are associated with improved outcomes for patients with heart failure. However, adherence to clinical guidelines remains limited in Kosovo. Objective: To assess the clinical decision-making related to heart failure diagnosis by evaluating clinicians’ preferences for clinical attributes. Method: Conjoint analysis with 33 clinical scenarios with physicians employed in public hospitals in Kosovo. Setting: Two public hospitals in Kosovo that benefited from quality improvement intervention. Participants: 14 physicians (internists and cardiologists) in two hospitals in Kosovo. Outcome measures: The primary outcome was the overall effect of clinical attributes on the decision for heart failure diagnosis. Results: When considering clinical signs, the likelihood of a heart failure diagnosis increased for ages between 60 to 69 years old  and a stable heart rate  and decreased for the presence of edema , orthopnea , and unusual fatigue . When considering clinical examination findings, the likelihood for heart failure diagnosis decreased for high jugular venous pressure , pleural effusion , hearing third heart sound, , heart murmur , troponin levels , and NTproBNP levels . Conclusions: We often found odd and wide variations of clinical signs and examination results influencing the decision to diagnose a person with heart failure. It will be important to explore and understand these results better. The study findings are important for existing quality improvement support efforts and contribute to the standardization of clinical decision-making in the public hospitals in the country. This experience and this study can provide valuable impetus for further examination of these efforts and informing policy and development efforts in the standardization of care in the country. Heart failure is a complex clinical condition caused by any functional or structural heart defect that impairs ventricular filling or blood ejection to the systemic circulation to meet systemic needs ,2. CurreHeart failure is the chronic stage of any disease that leads to cardiac functional dysfunction, making it difficult to pinpoint a particular cause . MultiplHeart failure remains a serious clinical and public health problem  and an eUsing clinical guidelines and protocols is not common in the Kosovo healthcare system. Treatment of cardiovascular conditions is not an exception. There is a limited state-of-the-art treatment ,11. ClinTo document practice variations and assess the early impact of the training (intervention), we have opted for conjoint analysis ,25. ConjThis study aims to understand the clinical decision-making related to heart failure diagnosis based on clinical signs or examinations by evaluating clinicians’ preferences through the discrete choice design of various hypothetical clinical scenarios. In addition, this study was interested in examining the quality measures’ early impact on reducing practice variations and improving service quality.We performed two conjoint experiments. The idea of using conjoint analysis stems from the need to evaluate how a physician makes clinical decisions for a specific condition, such as heart failure, and if they consider the combination of potential attributes during the decision-making process. The first experiment examines the role of clinical signs (mainly from examination of patient history) on the decision for heart failure diagnosis. The second experiment examines the effect of clinical examination findings on the decision for heart failure diagnosis. Standard conjoint analysis methodology was used to design and implement this study, including five steps: defining attributes, assigning attribute levels, creating scenarios, obtaining preference data, and estimating model parameters. The conjoint analysis design is consistent with previous studies ,25,27.A thorough literature review of textbooks and research papers through search databases such as Google Scholar, PubMed, and Scopus was performed to define each experiment’s attributes and attribute levels. This exercise was instrumental in identifying the most important and specific attributes related to heart failure. We chose attributes from symptoms, clinical signs, and biochemical markers of heart failure. The 2021 European Society of Cardiology (ESC) Guidelines for diagnosing and treating acute and chronic heart failure served as the main reference . InterviThe attributes were used to design the final scenarios, utilizing IBM SPSS Statistics V.22.0 software  orthogonal design facility. The orthogonal facility yielded a total of 32 scenarios for each experiment. An additional scenario (vignette) was added manually to each experiment for validation purposes, with features agreed upon in the literature as indications for heart failure.We used structured interviews to collect the data. The questionnaire had two main sections. The first part of the questionnaire collected information about the participants (physicians) of the study, including socio-demographics, education, experience, and exposure to scientific work. The second and main part of the questionnaire contained all 33 scenarios of each experiment that physicians had to read and make hypothetical clinical decisions. Examples of vignettes are provided in the The sample consisted of internal medicine or cardiology specialist physicians working in both intervention hospitals. To be considered for the study, each participant was required to treat heart failure daily. A small sample size was deemed appropriate as it contained most respondents out of the total population. The data collection for each experiment was performed by an experienced researcher (I.H.) using in-person interviews in most cases. Online interviews were conducted when in-person interviews were not possible due to COVID-19 restrictions. After explaining the process for both experiments, during the first part of the interview, we collected data on the participant characteristics  After that, in the central part of the interviews, the physicians were presented with a typical case history  and asked whether they would diagnose each scenario as heart failure. They had to examine and decide on each of the 33 scenarios of each experiment, in a total of 66 scenarios.We first performed a descriptive analysis of the key characteristics of respondents. For each experiment, the main analysis consisted of multinomial logistic regression models with robust variance estimates used to examine the effect of different clinical signs and attributes in the diagnosis of heart failure. Each clinical sign effect is represented by the Relative Risk Ratio (RRR). Statistical analyses were performed with STATA 17BE . The study protocol has been reviewed by the Ethical Review Committee at Heimerer College with protocol number 2045/21. Although initially planned, we were restrained from performing sub-group analysis due to the small sample size.The final sample comprised 14 respondents  out of 1Only one of the participants was female (7%), and the rest were male (93%). Internal medicine was the specialty of 12 participants, while ten participants reported having a sub-specialty. Eight participants reported the use of approved SOPs. None of the participants regularly attended continuous medical education (CME) activities. More than half of the participants reported they had benefited from the training, but a smaller percentage reported they had also benefited from study visits. Regarding the ward’s project benefits, five of them reported their ward had benefited (36%). In contrast, nine respondents reported no project benefits of the ward in the form of equipment or renovation of working space (64%).The attributes and levels (of attributes) used to generate case scenarios in both experiments are documented in For the first experiment, consisting of signs of heart failure, participants decided that a case was a heart failure for 257 (56.6%) out of 462 cases presented to them. The age of 60–69 increased the likelihood of the diagnosis of heart failure compared to <60 and >70 old patients . Furthermore, a stable heart rate also increased the chances of heart failure diagnosis compared to a decreased heart rate . On the other hand, the presence of edema decreases the likelihood of heart failure diagnosis compared to the non-presence of edema . Similarly, the presence of orthopnea and unusual fatigue decreases the chances of clinical diagnosis for heart failure compared to the lack of orthopnea  and unusual fatigue .In the conjoint experiment with attributes consisting of clinical examination parameters, participants decided that a case was a heart failure for 276 (59.7%) out of 462 cases presented to them. The presence of high jugular venous pressure decreased the chance of heart failure diagnosis compared to the non-presence of elevated jugular venous pressure . Similarly, the presence of pleural effusion , the third heart sound presence , heart murmur presence  decreased the likelihood of the diagnosis for heart failure. The troponin levels at >0.4 ng/mL decrease the chance of diagnosis of heart failure compared to the troponin levels at 0–0.4 ng/mL . In addition, NTproBNP levels at <400 pg/mL also decreased the chance of the diagnosis of heart failure compared to NTproBNP levels at >400 pg/mL .Using the multi-attribute compositional models , we explored various factors associated with the clinical decision for heart failure diagnosis in Kosovo. The likelihood of heart failure diagnosis is influenced by age, heart rate, orthopnea, edema, and unusual fatigue. In addition, the heart failure diagnosis was less likely to happen in the presence of high jugular venous pressure, pleural effusion, third heart sound presence and heart murmur, higher troponin levels, and lower NTproBNP levels.Few studies evaluated the symptoms, clinical signs, shared decision-making, ,31, and The strength of this study is a design informed by a literature review and testing performed to identify attributes used in the analysis of the stated preferences . By incorporating the conjoint methodology to design and implement this study, we could generate attribute combinations and further evaluate how a physician would decide the diagnosis of heart failure. However, the conjoint analysis also has its limitations, including the limited experience of the respondents in understanding the process . FurtherIt has been almost a quarter century since we entered the modern age of clinical guidelines to facilitate evidence-informed clinical decision-making, and several guidelines have been developed . The diaThe results of this study show a considerable variation in clinical decision-making regarding the diagnosis of heart failure, reflecting a somewhat limited use of the guidelines, protocols, and scientific evidence among staff in public hospitals in Kosovo. This fact does not come as a surprise, considering that it is common in low-income and low-middle-income countries , where oThe results also highlight the “skewness” of clinical decision-making for heart failure diagnosis toward factors, i.e., levels of attributes that are not correct or secondary for clinical diagnosis of heart failure . In most studies, an increased high heart rate is associated with heart failure ,39,40,41Shifting from a traditional to a more evidence-based approach in clinical decision-making in a country like Kosovo is challenging and needs time. The potential for improvements is high with different incentives and activities in place. The adoption of SOPs can be regarded as an important starting point. However, the guidelines used in highly developed countries are not always applicable to the circumstances and situations in low-income countries (LIC) and low and middle-income countries (LMIC). Therefore, adjustments should be made and the inclusion of local experts and stakeholders is highly important and recommended . ImproveThis study can be a useful introduction to the problems with standardization of care using advanced designs like the conjoint analysis. This initial work can help generate awareness based on facts and trigger discussions for moving forward. Replicating this design to a wider group of physicians in other public hospitals could provide more meaningful and robust results. Replication of similar study design and analysis for other procedures can extend the examination of standardization of care in a wider spectrum of clinical procedures and hence provide deeper insights into the issues with standardization in the country. This whole expertise can be useful for countries with similar problems. Our study attempted to evaluate the clinical decision-making and use of standard operating procedures for heart failure. We often found odd and wide variations of clinical signs and examination results influencing the decision for heart failure diagnosis. These findings should be further investigated and understood as quality improvement efforts move forward. The study findings are important for existing quality improvement support efforts and contribute to the standardization of clinical decision-making in the public hospitals in the country. This experience and this study can provide valuable impetus for further examination of these efforts and informing policy and development efforts in the standardization of care in the country.


While the current update keeps the basic features of earlier versions, the platform has been upgraded not only with many more ready-to-use plasmids but also with features that expand the range of target species, harmonize DNA assembly methods and enable new applications. In particular, SEVA 4.0 includes (i) a sub-collection of plasmids for easing the composition of multiple DNA segments with MoClo/Golden Gate technology, (ii) vectors for Gram-positive bacteria and yeast and [iii] off-the-shelf constructs with built-in functionalities. A growing collection of plasmids that capture part of the standard—but not its entirety—has been compiled also into the DB and repository as a separate corpus (SEVAsib) because of its value as a resource for constructing and deploying phenotypes of interest. Maintenance and curation of the DB were accompanied by dedicated diffusion and communication channels that make the SEVA platform a popular resource for genetic analyses, genome editing and bioengineering of a large number of microorganisms.The SEVA platform ( Escherichia coli and Bacillus subtilis. Such tools include on one hand formatted methods of DNA assembly  to the ultimate recipient  serves primarily as an annotated and information-rich index of functional DNA sequences and constructs that are available to the community. The platform maintains a simple architecture, which consists of a relational database as the records storage layer, a series of modules and utilities that are hosted by an application server and a web-based presentation layer (gbk or gb file of the plasmid), and to the sequences in Synthetic Biology Open language (SBOL) format (see below). However, built-in visualization of physical maps of each of the plasmids (which was available in earlier DB versions) has been removed and users are encouraged instead to export the DNA sequences of interest to exterior platforms of their choice, e.g. DNASTAR Lasergene (https://www.dnastar.com/) or Benchling (https://www.benchling.com/).As was the case with its predecessor, the updated SEVA database .The graphic identity of the new website and its main sections have been largely kept as in the previous version , althoug in 2013  and we mBackbone modules section constitutes the core of the platform, as the user has access to an intuitive graphic tour through the different functional segments of the plasmids and where visitors can see and copy all relevant sequences of the SEVA frame . Optional gadgets include stabilization parts for securing plasmid retention in the absence of antibiotic selection and a yeast origin of replication for inter-kingdom transfer. Finally, users can survey a limited number of preset off-the-shelf functionalities for some specific applications (see below). This canonical collection  includes the catalogue of SEVA vectors immediately available which fulfil every rule of the standard. While most plasmids of interest use to be ready in such a normative repository, combinations of functional parts that may not be physically assembled yet is facilitated to users-to-be by providing precursors following the directions spelled out in this same section. Availability of sequences in Genbank and SBOL formats enable their export for multiple visualisation options as indicated above.The next section (SEVA Golden standard (cargo #19 and derivatives). These plasmids enable a Type IIS assembly method for combinatorial multi-part composition of standardized genetic elements to create complex genetic constructs with many transcription units. The detailed description of such platform is described in  mTurquoise2 (in plasmid pSEVA2313T) where the gene encoding this marker was placed under the control of the constitutive PEM7 promoter  anter  constructs in the section r Figure . The vectrations . Also, ttrations . This coE. coli , a useful resource for Synthetic Biology-based biotechnology, (ii) access to the collection of blog posts in social media (see below) and (iii) contact forms for users to request the plasmids free of charge.Finally, the homepage of the platform incorporates (i) updated links to the addresses of depositors along with a connection to the autonomous aadD gene from pUB110-S. aureus) and #3a (cat gene from pC194-S. aureus) correspond to resistance cassettes derived from Gram-positive bacteria, that allows cognate bacterial cells to grow in kanamycin or chloramphenicol respectively  followed by one or two letters corresponding to the downstream component . The four types of cargoes applications originally described in SEVA 1.0  belong thttp://sevahub.es), the design repository providing access to SEVA vectors converted to the Synthetic Biology Open Language (SBOL) . Users can access this conversion tool at https://github.com/Biocomputation-CBGP/GBK2SBOL. Resulting SBOL files can then be used for the in-depth analysis of sequences and constructs (SEVA 2.0  enabled e (SBOL)  has beene (SBOL) . This vee (SBOL)  and bug nstructs  leading nstructs .One of the trademarks of the SEVA platform is making standardized vectors freely available to all types of users, whether academic or industrial. All users have to do for ordering SEVA plasmids and Golden Standard vectors is filling the request form of the corresponding section of the SEVA webpage. Petitioners find there an easy procedure for asking (a reasonable number of) specific vectors. These are then shipped free of charge to individuals who contact through institutional email addresses of research centres, universities and accredited companies. For bulk orders of plasmids (10 or more), please contact the Webmaster as some handling fees could be applicable. Please note that we do not send materials requested through commercial email platforms or private addresses. Note also that we disclaim carrier liability. While most of the functional parts entered in the SEVA vectors enjoy freedom of operation, we cannot rule out that some given sequences are subject to some intellectual property (IP) rights. Should utilization of SEVA plasmids eventually led to a commercial application, users are encouraged to inspect the IP status of the corresponding DNA segments and deal with the issue with the original IP holder. In this way, IP and potential licensing issues are handed over to end-user scenarios rather than raising them upfront.https://twitter.com/SEVAplasmids) along with an interactive blog with comments, novelties and posts related to vector development. More recently, these have been added with a free newsletter sent every three months to subscribers. External access to these platforms has enabled us to map and follow the evolution of the interest in the SEVA resource, especially in the period of time since the last update in 2020. As seen in Figure https://seva-plasmids.com/blog/) and the quarterly Newsletter to which one can subscribe through the web page as well. Worth to mention also is the considerable visibility and appeal bestowed by the explanatory animation video (https://www.youtube.com/watch?v=I8gZjXDRQ2I) which appears in the SEVA homepage and provides a quick, user-friendly glimpse of what the platform is all about.The setup of the first SEVA webpage in 2013  was follWhat could be called the SEVA space includes a collection of general-purpose, broad host range cloning vectors, heterologous expression systems, reporters and specific purpose-constructs. The entries of the canonical vector collection, which is the core of the corpus, follows rigorously the arrangement of functional DNA segments established in the earliest publication Figure ; 10 and Despite the number of excellent plasmid repositories available to molecular biologists and synthetic biologist, the distinguishing marks of the SEVA platform include (i) the emphasis on compositional standards for the sake of interoperability and reproducibility, (ii) the adoption of broad host range functionalities  and (iii) open and free upfront access to the materials at stake. While other repositories of biological materials may also take all or some of these features, we strongly advocate standardization as the way to go for moving the field of synthetic biology forward.Since the last update of the SEVA DB we have noted publication of a number of articles that further develop the concept in different directions explicitly related to the original platform ,51. In oIn the meantime, some additional expansion of the SEVA platform could be expected for improving e.g. the host range of the activities encoded in the constructs. This involves not just the replication origins but also expression signals for enabling genes to be expressed in a large variety of hosts . Also, thttps://seva-plasmids.com). No new data were generated or analysed in support of this research.All relevant data is available through the SEVA platform (


Rosai-Dorfman disease (RDD) is characterized by primary proliferation of histiocytes in the lymph node sinusesThis case report aims to describe an atypical manifestation of RDD with primary involvement of the paranasal sinuses alone, unaccompanied by lymph node or adenomegaly; the case also reinforces the relevance of considering systemic disease when CT scans indicate nose and sinus polyposis in pediatric patients.L.S.O., male, had ongoing nasal obstruction since the age of 5 and underwent adenotonsillectomy. The symptom persisted and he was prescribed topical steroids. At age 10 the patient suffered from marked worsening of his nasal obstruction, additionally to malnutrition, weight loss, and learning disorders. When he sought help at another center a CT scan of the facial sinuses was ordered and revealed a formation with soft-tissue density occupying the ethmoid cells (obliteration of the intercellular substance) in both maxillary sinuses, along with obstruction and enlargement of the ostiomeatal complexes. The scan showed soft-tissue densities in the nasal concha topography consistent with nose and sinus polyposis . InitialThe patient was then sent to surgery to have the tumors removed and further tested. During surgery it was observed that the tumors had a brittle, granulomatous aspect resembling histiocytosis. Pathology and immunohistochemistry tests were ordered as the patient was suspected for juvenile xanthogranuloma. Histiocytes, giant cells, and lymphocytes were visualized in the microscope, but immunohistochemistry was positive for CD68 and S100 to confirm the diagnosis of Rosai-Dorfman disease.The patient was sent to the oncology service, and no systemic involvement was found. He was kept in observation for one year and only then complained of pain in his left leg. MRI scans showed multiple solid nodes in the bone marrow of his lower limbs located predominantly in the patient's left tibia. Such signs suggest bone marrow neoplastic disease consistent with Rosai-Dorfman. The patient underwent chemotherapy for one year and is being currently followed up and seen every six months.RDD occurs more frequently within the first decades of life. Most patients are males (58%)Diagnosis is verified through immunohistochemistry tests positive for proteins S100 and CD68.The CT-based diagnosis suggestive of nose and sinus polyposis was questioned from the beginning, as this disease rarely affects children and adolescentsJuvenile xanthogranuloma was included in the early roster of diagnostic possibilities. This disease must be considered for differential diagnosis purposes, as type II histiocytosis (non-Langerhans cell histiocytosis) occurs during childhood. Males are slightly more affected than females. Microscopic examination of specimens show histiocytes, giant cells, and inflammatory cells. These findings are consistent with RDD, and the difference can be told solely through immunohistochemistryThere is no consensus in the literature on how to manage RDD patients. Surgery is indicated to improve obstruction-related symptoms.This case serves as an example of the myriad clinical manifestations seen on RDD patients and illustrates the difficulties in getting to a diagnosis based exclusively on the presence of nasal obstruction. Additionally, it stresses the relevance of considering systemic disease when pediatric patients present symptoms consistent with nose and sinus polyposis.


A). The patient is then lost to follow-up and comes back 6 months later with proptosis and frontal bossing (B).A 2-year-old male is brought by his parents. The history is that a white reflex was noted in the eye several months ago. The eye has now become red and painful  Retinoblastoma(e) All the above. Clinical examination confirms the laterality of disease. Imaging of the orbit confirms the presence of extraocular extension, and confirms the presence or absence of optic nerve extension with/without associated intracranial extension. The two most common sites of metastases from retinoblastoma are cerebrospinal fluid and bone marrow. Cytology from these two sites is indicated in all cases of retinoblastoma with extraocular extension.(e) Combination treatment of systemic chemotherapy, enucleation, and orbital external beam radiotherapy. Primary enucleation or exenteration should not be done in cases with overt extraocular or optic nerve tumour extension, since this can worsen the prognosis due to tumour spill over. The ideal treatment modality is a multimodal approach of chemoreduction followed by surgery, radiotherapy, and adjuvant chemotherapy.