Attention is increasingly focused on the protection of water systems as critical infrastructure, including subsystems of supply, sanitation, hygiene, and management. Similarly increasing consideration is paid to the growing role and impact of data on water systems and management. We explore key challenges associated with data-driven water systems as critical infrastructure. First, we describe the status of water infrastructure as a part of national critical infrastructure. Second, as this infrastructure increasingly relies on the constant flow of data from a huge variety, quality, and complexity of sensors, we provide a descriptive framework to map in detail the particular expertise needed across data-driven water management, applied to the UK water infrastructure as our use case. Third, through the framework of Capabilities Approach (CA) we analyze the specific challenges of data-driven water management, and argue that the current predominant narratives in the water infrastructure discourse have difficulties to effectively convey existing and emerging challenges. Fourth, we further demonstrate the widening gap between infrastructure services and consumer goods, arguing for increased convergence of the utilization of consumer data, and developing open data ecosystems. Leonardo da Vinci called water the driving force of nature Kalen, , and EarSince ancient Egypt, humanity has relied on data and monitoring of water levels to achieve large-scale water management  Resolution A/RES/64/292 acknowledges clean drinking water and sanitation as essential to realizing all human rights UN, . In EuroKalen  concludeYet, over 800 million people continue to lack basic water services, and 2.1 billion experience difficult access to drinking water services in their living premises UN, . IncreasAgainst this backdrop, our objectives with this contribution are: first, to describe the status of water infrastructure as a part of national critical infrastructure. Second, this infrastructure increasingly relies on the constant flow of digital data from a huge variety, quality, and thus complexity of sensors and actuators. This creates challenges from the viewpoint of required expertise, risks, and benefits, as well as unforeseen consequences. Third, we analyze these through the framework of the Capabilities Approach (CA) and argue that the current predominant narratives in the water infrastructure have difficulties in conveying the existing and emerging challenges . Following detailed analysis, we further demonstrate the widening gap between infrastructure services and consumer goods, arguing for increased convergence of the utilization of—currently wasted—consumer data and the development of open data ecosystems.Information used in connection with water manifests itself in many forms ranging from the basic  to the complex datasets used to verify hydraulic models. Given this variability in use and the complexity of datasets in this domain, for any analysis to be meaningful, consideration must be given to an analytical framework against which conclusions can be formed.Before these considerations are addressed, it is first necessary to understand the difference between data, information, structured and unstructured data, and knowledge; and the relationship between machine learning (ML) and the cyber-physical environment.It has been estimated that around 80% of all current datasets are unstructured ; clean treatment; clean distribution; waste collection; waste treatment; flood management; pollution control; weather forecasting, etc. Similarly, this paper is not confined to the type of data or information exchanged, be that: conferences and industry bodies exchanging best practices; data and information gathered and used by water companies to control and operate their assets; information exchanged between commercial and governmental organizations ; water resource management ; or any other form.Likewise, the scope of this paper has no restriction on the scale of information shared, including everything from the civil public verbally communicating the location of the nearest water hole to the large, sophisticated datasets described as “big-data” Raheem, . Data caHistorically, it has always been assumed that humans are better than machines at recognizing patterns in unstructured data such as images and sound  but less so for the repetitive processing of structured data  devices , which together create many combinations. Kitchin and McArdle  defined For our analysis, to circumvent this complexity, the upper bound is set in the following section through a case study based on the United Kingdom water industry, which also gives tangible examples of the origins of data, how it is processed into information and how it is applied to water management. Although limited to a single, regionally constrained sector, this description provides a foundation upon which other use cases can be compared. However, before embarking on this avenue, it is necessary first to define the different types of data and the methods to convert them to information.To concretize the overlaps between data-driven infrastructure and water management, we focus particularly on the UK Water Industry as a case illustration to set the upper bound of data complexity as follows:In contrast to other process industries , which process their main asset independently, the large UK water companies are highly reliant on data to centrally manage both production and distribution asset types as an integrated system.The UK Water Industry relies on data to manage their Clean and Waste businesses as a unified enterprise, along with non-regulated businesses such as energy from waste.The recent focus by UK water companies on innovative technologies across Clean and Waste ;via research);- Increased ability to collaborate with local communities and digital developers on innovative solutions for water-related problems  take real-world measurements by sampling sensor data, time stamping it, converting it to time series data, and transmitting the results to a central system of record for processing “] which sample real-world parameters but primarily for control and automation of the process Love, . This prThere is no single underlying technology associated with the third class yet, defined by the ability to take consecutive samples at a fixed frequency to record and process unstructured data such as audio, moving images, vibration data, etc.The UK water industry relies on the coverage of sophisticated technologies mostly available in developed countries to harvest this data, ranging from simple Public Switched Telephone Network (PSTN)Similarly, the capacity to process and utilize this data depends on resources  not available in many countries. For instance, traditionally leakage detection relied on reports by members of the public and teams of employees looking and listening for evidence of leaks. Following the drought in 1995 the UK regulator tightened restrictions on permitted leakage OfWat, , which sThis, in turn, has created a growth in the three data categories defined earlier, which has been matched by an increased reliance on traditional ML . Furthermore, the introduction of deep learning allows one to gain further insights into these existing datasets while also analyzing the new unstructured data, such as audio files acquired from acoustic loggers.This level of analytics requires a modern infrastructure, such the hybrid model illustrated in Returning back down the technology stack, this same model also applies to the Clean production, and Waste treatment facilities, the difference being that these assets are more complex, collect larger volumes of data, require a higher level of automation, and are controlled locally by operators using site SCADA systems.Despite the restrictions placed on this case illustration  it does describe the variety of data and information available, across all water management sectors and nations assuming the existence of the necessary, infrastructure and skills base required to support them.The presented case illustration of the UK water infrastructure highlights the absence of the associated IT and OT engineering/technical expertise with the pressing societal challenges related to the human right to water, and the present climate emergency. In the next section, we approach the convergence of data and water infrastructures from a higher-order descriptive framework, which is based on innovation stages. We aim to investigate the reflection of societal and normative challenges in the current conceptualizations of data-driven water infrastructure systems.Data-driven water management usually combines big data, Digital Twins Verma, , and StrThe four stages of industrial revolutions provide a convenient classification system to align the continuum of data collection and usage defined above (Section 2) for further analysis. This analytical framework assigns abstracted facets of data usage to four quadrants of a matrix according to data collection and utilization .x-axis (data collection) aligns with that for the 2nd and 3rd Industrial Revolutions—with low collection typified by manual and analog techniques and high by the move to automated digital sampling of physical  parameters. The term “manual” in this context includes data collection in a digital form requiring a high degree of human input . Therefore, leakage detection derived from piloted drones  is best divided based on the ability to analyze structured and unstructured data sets—i.e., broadly divided between the 3rd and 4th Industrial Revolutions—manual and semi-automatic data processing of the 1st and 2nd Revolutions was best suited to structured data processing along with the highly structured sample and time series data associated with the 3rd Industrial Revolution.The Our approach is predicated on the understanding that no single use case or domain inhabits just one of the four stages of industrialization. Still, the relevant quadrant can be identified based on the degree of adoption of technology, as it relates to a specific argument. For instance, nowadays most countries have access to satellite information enhanced by ML (despite not having the technology to derive it themselves). In contrast, countries with access to the full gamut of data gathering resources still resort to some manual recording of data (even if it is later inputted into a computer system). Despite both using the full range of available data collection and analytical methodologies, it is the highly industrialized countries who adopt the most up-to-date techniques at scale, and are therefore better able to benefit from the ability to share data.Mapping the innovation stages to this descriptive framework, it is apparent there is a tendency to increase both the quantity and the quality of data collected to facilitate more efficient utilization of these for service maintenance. However, this shift is not even across the operational and information systems, while they reflect normative and societal challenges only indirectly. In the following analysis, we, therefore, highlight the need for and a proposal of a normative framework that can better facilitate the explicit reflection on societal challenges facing present and future water infrastructures.To apply this descriptive-analytical framework, we explore a real-world application mapped onto the 2 × 2 matrix in The matrix delineates water management through data ownership and utilization, following the paradigms present at the stages of industrial revolutions. As the stage of industrial revolutions continue, both the amount and processing of data increase. Moreover, the shift from structured/semi-structured toward unstructured data occurs during this development.The UK Canal and River Trust's central control room is connected to a network of 600+ strategically deployed canal monitoring stations  to detect leaks across their drinking water networks. The theory is simple, given that pressure in a pipe is a function of height and that any deviation from that profile must equate to a leak. However, similar variations are more likely to result from legitimate use.Historically this structured (time series data) harvested from the loggers has been, and continues to be, processed using traditional database tools and teams of experts analyzing the data using human experience to identify leaks.Although the use of human experience to analyze pressure and flow data is still the predominant model, it is being increasingly augmented by machine learning looking for similar patterns as the human element, and in some cases looking for the patterns of activity that might result in a future leak.In addition to these back office “leakage teams” (whose job it is to identify leaks and schedule their repair) the central control room also makes use of this data to identify major leaks in real time  that could have immediate customer impacts.Traditionally this has been achieved by setting fixed alarm limits around each incoming data point alerting operational staff if any pressure measurement is either too high or too low. However, given the variability of normal usage, setting these alarm setpoints has always proved problematic. Water companies are now turning to machine learning to identify patterns of usage to dynamically adjust the alarm profiles according to demographics, time of day, time of year, holiday periods, weather etc.UK water companies are increasingly investing in more sophisticated methods of leak detection. One approach replicates the ability of humans to recognize the sound of a leak.Companies are investing in large network of acoustic data loggers that take recordings from microphones attached to the drinking water network. This data is then made available to ML systems capable of interpreting the acoustic files.Internal sewer flooding occurs in areas  where one, or more, properties are connected to a sewer pipe with a sub-optimal fall. Due to slow flow rates these pipes are prone to partial blockages that go unnoticed until it rains at which point contaminated water backs up flooding the properties.Historically detecting these partial blockages to clear them ahead of bad weather has been notoriously difficult.With recent advances in IIoT level sensors (devices that do not themselves encourage blocking and that are capable of transmitting data from a buried pipe), it is now possible to detect partial blockages. However, these devices are of little value without the associated machine learning required to differentiate genuine blockages from the noise generated by normal but intermittent use of the sewer.It is recognized by the regulator (UK Environment Agency) that some sewer networks are expected to handle both effluent and rainwater (combined sewers) and it is unrealistic for treatment works to cope with the volumes of water associated with bad weather events. It is, therefore, necessary (under these conditions) to discharge a proportion of the untreated effluent into rivers. In practice this is managed by specially engineered chambers (Combined Sewer Outlets—CSOs) . Therefore, CSO levels are constantly monitored so that teams can be dispatched to clear blockages.To deliver this requires a large network of level sensors and data loggers, however the processing of this highly structured data into operational alerts is very simple.The supply of water and collection of waste is dependent on a large number of geographically dispersed pumping stations. To improve the reliability of these assets, while also reducing costs, water companies are increasingly investing in Condition Based Monitoring (CBM).Unlike traditional approaches to maintenance (scheduled or fix on fail) CBM monitors the performance of the asset with maintenance only occurring when the asset falls outside a predefined performance envelope.One of the most common parameters analyzed in pumping applications is vibration. However, for dispersed assets it is not practical to do this in real time—it being more common to acquire vibration data with a handheld device on a predefined rota . Although the data collected is highly structured it requires some sophisticated processing before it can be interpreted. Furthermore, machine learning is being increasingly deployed to augment the interpretation of the information  derived from the data.The description of the data-driven water infrastructure systems highlights the inherent socio-technical nature of these cyber-physical systems, which is as such a complex system. Due to the narrow and distinct foci of IT and OT technical domains, in the following sections, we discuss how governing managerial frameworks struggle to convey normative  challenges associated with data-driven water systems. We elaborate on the lack of focus on respecting the human right to drinking water, for current and future generations.After describing the complementary relationship of modern water management ecosystems with digital data-driven infrastructure, in the second part of this article, we analyze whether this relationship sufficiently tackles the challenges ahead of water management.to what extent does the datafication of water management systems sufficiently address the future challenges of water systems? We utilize an adjusted version of Capabilities Approach (CA) for this normative analysis to answer the question.We identified earlier that the main future challenges of water management lie in the interrelated relationship between the (a) fulfillment of the human right to drinking water and hygiene; and (b) climate emergency that negatively influences the quality, availability, and access to drinking water. However, besides these points of departure, the overall purpose of water management goes beyond the narrow requirements of maintenance and protection. As all these aspects and the respective challenges are normative in nature, we seek to answer the following question: We employ a theoretical approach to reflect on the normative underpinnings of data-driven water systems as critical infrastructure. The CA focuses on concrete and future capabilities, which—in this case, a data-driven critical infrastructure—should enable every human being to function. Through such a normative lens, we can better highlight the complex elements and prospects of a well-functioning data-driven water system as critical infrastructure for individual people now and in the future within a single analytical framework. For this, we will apply the CA developed by Nussbaum  and Sen CA builds upon a normative commitment to conceptualizing wellbeing in terms of capabilities and functionings ; compared with practitioners focusing on privacy, security, accuracy, data recovery, etc. (represented by IT experts)  Directive EC,  by DCMS Water systems have been studied from social  data-driven; exchanges will increasingly become more artificial; data as a resource provides competitive advantage; and transactions in socio-technical systems demand governance forms and capabilities other than the traditional organization of mere human agents. We will now elaborate on three challenges water systems face in becoming data-driven.voluntary actions of groups or individuals that are motivated by the returns they are expected to bring and typically in fact bring for others”  and a commodity  of water systems Smith, . We borrThe very nature of the easy exchange and transmission of digital data may hinder the proper implementation of strategies that aim at improving the efficiency and effectiveness of the whole system Miller, . This poFrom the CA viewpoint, it can be argued that greater data transparency may address many technical issues in water management , but the nature of this option needs to be explicitly established and validated NWG, . NeverthFinally, the transaction-cost theory highlights the evolving socio-technical challenge of data-driven water management. Transaction Cost Theory argues that systems must decide to either make something themselves or buy it instead Ouchi, . The basThe fact of uncertainty regarding whether shared data can be implemented, used, or analyzed may—from the CA viewpoint—hinder further innovations in data-driven water systems and limit the capabilities of both water management systems as well as consumers  film Water is essential for life, and access to it is a widely accepted, basic, and universal human right. Water systems are, therefore, critical infrastructure with Information and Operational Technologies involved. The digital transformation of our societies includes the datafication of water resources and the digitalization of water systems, which introduces many new complexities into these evolving socio-technical systems. Viewing these systems through the lenses of the social exchange theory, resource-based view, and transaction cost theory has revealed their limitations. Viewing these complexities through the lens of CA highlighted that the focus on present-future capabilities can incorporate broader normative claims  and settle decisions for future courses of action. The multidisciplinary exploration of several key challenges encountered with data-driven water systems as critical infrastructure has determined potential paths to address these challenges, and improve and optimize data-driven water infrastructures. These include putting in place governance that implements the standardization of data features ; the development of clear regulation, codes of conduct, and best practices within industries; and the facilitation of data sharing that more widely engages citizens as well as researchers and which fosters innovation and the healthy growth of economies and communities, in addition to helping realize actual freedoms and capabilities for individual persons. These are just some of the possible routes to optimizing data-driven water systems as critical infrastructure to responsibly and justly use and share water and data on water and water systems to ensure the wellbeing of all persons, other species, and the natural world on our blue planet, now and in the future.https://data.europa.eu/en.Publicly available datasets were analyzed in this study. This data can be found here: All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


This paper aims to examine the impact of CEOs' early-life disaster experiences on corporate earnings quality. We proxy the disaster experience with whether CEOs lived through the Great Chinese Famine and the famine intensity they experienced. The results indicate that CEOs' early-life famine experience is significantly positively associated with corporate earnings quality, and the famine effects are more obvious for CEOs who experienced the famine at adolescent ages. Further tests show that the famine experience effects are more pronounced in companies with high investor protection and cross-listing and with CEOs who have a relatively high level of education or background in economic management. The findings suggest CEOs would bear the imprint of an adverse early-life experience, which has risk aversion and learning effects on their decision making in corporate earnings information disclosure. Imprinting theory holds that the process through which a focal entity develops characteristics that reflect prominent features of the environment, and these characteristics persist despite significant environmental changes  to match it with the occurrence of the Great Famine and the severity of the famine's impact on different regions. We proxy the CEOs' famine experience with whether a CEO lived through the years of the Great Chinese Famine (1959–1961) and the famine's intensity in the CEOs' birthplace. Following prior studies  database. The CSMAR database contains financial and corporate governance information from 1999 to date. However, our sample was limited to 2000 to 2015 because observations for some test variables in 1999 are missing from the database. Our sample ending range is 2015 because the CEOs' background information that we manually collected has been limited to 2015.*ST listed companies; samples whose CEOs were of a non-Chinese nationality or born in Taiwan, Hong Kong, Macau, or Tibet; and samples with missing financial information or missing place of CEO's birth. To control the influence of outliers, all continuous variables were winsorized at the 1 and 99% quantiles. Ultimately, we obtained 23,974 enterprise-year samples.We applied the following exclusion criteria: samples from the financial and insurance industry; ST and 1, k2, and k3.We used the modified Jones model proposed by Dechow et al.  to measuTACCt represents the total accrued profits in year t, calculated by NIt-CFOt, where NIt represents the net profit in year t and CFOt represents the net cash flow from operating activities in year t; TAt−1 is the total assets in year t−1; ΔSalest represents the changes in corporate operating income calculated by the difference between corporate operating income in years t and t−1; and PPEt represents the net amount of fixed assets in year t.In model (1), Second, we calculated the controllable accruals of the sample companies based on model (2), as follows:DACCt represents discretionary accruals in year t; ΔARt is the change in corporate receivables; and the estimated values for parameters k1, k2, and k3 are estimated from model (1). Specifically, DACCt is the degree to which actual discretionary accruals of an enterprise deviate from ideal discretionary accruals. The greater the deviation, the more severe the earnings management. We take the absolute value of DACCt obtained by model (2) and expressed it as EQt. The larger the EQt, the worse the earnings quality, and the smaller the EQt, the better the earnings quality.famine experience to 1 and otherwise encoded to 0. Second, we performed the following steps to measure famine intensity: calculate the difference in average death rates between 1959–1961 and 1956–1958 for each province and the difference in average death rates between 1959–1961 and 1962–1964 for each province; estimate the mean average excess death rate (EDR) using these two differences to obtain the EDR for each province during 1959–1961. Further, based on the EDR, we set a dummy variable EDD, which was encoded to 1 if the province experienced a median-above EDR during the three famine years and otherwise 0. Finally, following Hu et al. ; Ctrls are the control variables as described above. Further, due to the economic cycle, industry environment, regional factors, and CEO growth environment may affect the validity of the regression estimation. Thus, in order to control the different fixed effects, we added five dummy variables to our model: year (Y), industry (I), province (P), CEO birthplace (BP), and CEO birth year (BY).In model (3), Famine and EDR (EDD), these two variables do not change with time and province. While we controlled the fixed effects of BY and BP, we did not add the variables Famine and EDR (EDD) to the model again. In model (3), our variable of interest is Famine*EDR (EDD). If our hypothesis is true, we can expect that the coefficient of β2 will be negative and significant even for CEOs who experienced the famine during the same age as different intensities of famine in different regions may lead to differences in earnings quality.It is worth noting that although the famine experience is an interaction term for EQ) is 0.047, and the median is 0.036, but the standard deviation is 0.051, indicating a large difference in earnings quality in the sample. The average value of Famine is 0.419, indicating that about 42% of the CEOs in the sample experienced the Great Chinese Famine. The average EDR is 0.591%, which indicates that the average excess mortality rate during the 3-year famine is close to 0.6%. Given China's large population, this number indicates a painful and profound impact. The standard deviation of the EDR is 0.534, which is close to the average, indicating that the impact of the famine differed greatly in different provinces. The descriptive statistics of other control variables are mostly consistent with prior studies and have not been described in detail in this paper.EDR in each region from 1959 to 1961. As shown in the table, EQ is relatively higher among CEOs who were born in provinces with higher mortality, indicating the average earnings quality is slightly lower for CEO who did not experience the famine.Based on the intensity of the famine in the region where the CEO was born, we further described the association between CEOs' famine experience and the distribution of the earnings quality. The results are given in Famine = 1 is lower than the straight line of Famine = 0, indicating that famine experience improves corporate earnings quality. Regardless of Famine = 1 or 0, corporate earnings quality increases with an increase in the impact of the famine (the trend line slopes downward to the right). Compared with Famine = 0 companies, the downward trend represented by Famine = 1 companies is more obvious. These results suggest that when a CEO experiences Great Famine, their company's earnings quality improved significantly, and the more severe the famine, if experienced, the higher the earnings quality.To intuitively describe the relationship between famine experience and earnings quality, we plotted the excess mortality rate in a CEO's birth place during the famine and the company's earnings quality based on descriptive statistical analyses. As shown in EDR). The results are given in Famine = 0), no significant difference was seen in the mean and median of EQ between the EDD = 0 and EDD = 1 groups. However, for CEOs who experienced the famine (Famine = 1), the average EQ and median EQ in the EDD = 1 group were 0.038 and 0.036, respectively, which were significantly lower than those in the EDD = 0 group. This indicates that CEOs' famine experience significantly improved earnings quality. We also tested the DID results for EQ in the famine and EDD groups. The difference of the mean and median EQ was −0.008 and −0.002, respectively, both of which are significant at the 10% level at least. These results indicate that whether a CEO experienced the famine and the extent to which he experienced it have a significant impact on earnings quality.Before regression analyses, we performed a univariate test on earnings quality to check whether CEOs experienced the famine and the extent of famine intensity in CEOs' birthplace  and (4), all firm-level and CEO-level control variables were controlled in the regresses. The coefficient of Famine*EDR was significantly negative at the 5% level in column (2), and Famine*EDD in column (4) was significantly negative at the 1% level. It should be noted that since the fixed effect of the birthplace and birth year of the CEO was considered, the influence of famine experience expressed by Famine and the intensity of famine expressed by EDR (EDD) were absorbed by the fixed effect of birth year and birthplace, and their coefficients were omitted due to collinearity (similar to subsequent regressions). Overall, the regression results given in Size), the return on assets (ROA), the nature of property rights (SOE), audited by one of the Big 4 or 5 audit firms (Audit), and CEOs' education degree (Degree) were significantly negatively correlated with EQ, indicating that when an enterprise is larger, its return on assets is higher, it is state-owned, it is audited by a major accounting firm, or the CEO has a higher degree, the quality of corporate earnings is relatively higher. Financial leverage (Lev), company growth (Grow), the largest shareholder's ratio of shares (First), and the age of a CEO are significantly positively related to EQ, indicating that when companies have a higher debt ratio, they experience a higher speed of revenue growth and have more shareholding by the largest shareholder; likewise, when the CEO is relatively old, the earnings quality is worse. The results for the control variables are basically consistent with the findings of previous research  for each interactive variable in infancy, childhood, and adolescence are similar to those in the first column.As shown in column (1) of t-tests on the estimated coefficient value after regression. The statistical results are reported in the last three rows of each column in EDR were significantly bigger than those for other three interactions. This shows that the impact of CEOs' famine experience in adolescence is significantly greater than those for experience in infancy, childhood, and adulthood. In summation, the results of To compare whether the above differences in coefficients are statistically significant, we performed Although the regression results obtained with the DID method in previous studies support the theoretical expectations of this article, the results may be distorted by endogenous problems, such as missing variables, the assumption of an equilibrium trend for the DID method, and unobservable factors at the company or macro-level. We adopted the following methods to address possible endogeneities and tested the robustness of our results.We mainly used DID to test our hypotheses. However, there is a very important prerequisite for using this method, the equilibrium trend hypothesis. Without this, the DID method cannot effectively alleviate potential endogeneity problems. Accordingly, we referred to the practices in the previous studies and designed the following model to test the equilibrium:Cohort−1 denotes CEOs born 3 years after the famine, 1962–1964; Cohort−2 denotes CEOs born during the fourth to sixth years after the famine, 1965–1967. In model (4), we used CEOs born after 1967 as the benchmark for the equilibrium trend test. The coefficient for Cohort*EDR (EDD) represents CEOs who were born during these two periods after the famine. Assuming equilibrium trends, we expected that the coefficients of Cohort*EDR (EDD) should not be significantly different from 0.where Famine*EDR and Famine*EDD as variables of interest, respectively. The results in these columns indicate that the coefficients of EDR (EDD) and EDR (EDD) were not significant, suggesting that CEOs born after the famine have no significant differences in terms of influence on corporate earnings quality. These results are in line with the assumption about common trends in DID estimation methods. More importantly, for CEOs born before the Great Famine, the coefficients of Famine*EDR (Famine*EDR) are all significantly negative at the 5% level, indicating that CEOs' famine experience are positively associated with corporate earnings quality.Famine*EDR (Famine*EDD) remained significant and negative at the 5% level, which is consistent with earlier findings, indicating that the results were not sensitive to the firm-level factors that do not change over time.Fixed factors of a company that do not change over time, such as honesty in the corporate culture, may also cause differences in corporate earnings quality. To control for the impact of factors that may be omitted in the regression results, we further test the main hypothesis by controlling firm fixed effects. The results are reported in the first two columns of EDR due to the famine with neighboring provinces with lower-level EDR. Specifically, we selected three pairs of matched samples from Shandong and Shanxi, Henan and Hebei, and Guangxi and Guangdong. These regions differ little in geography and culture, so we could use it to control some unidentified macro-level factors. The results based on the samples from the six provinces are shown in the last two columns of Famine*EDR (Famine*EDD) remained significantly negative at least at the 5% level, which is consistent with the previous findings.Other factors that do not change over time may impact the conclusions of this study, such as cultural differences between different regions. To resolve this issue, we matched the provinces with higher-level Famine = 1) and whether famine was severe in the region where the CEO was born (EDD = 1). A one-to-one closest-match procedure was performed to obtain two sets of highly similar samples and to eliminate differences in the earnings quality due to differences in characteristics. Subsequently, the samples were regressed. If the CEO's famine experience continues to have a positive effect on corporate earnings quality, the aforementioned indicators can be ruled out as causing differences in the corporate earnings quality.Differences in company characteristics, the CEO, and the region where the company is located may also cause bias. We use propensity score matching (PSM) to test the basic hypothesis. The procedures are as follows: the financial characteristics and CEO characteristics of our sample were assessed for whether the CEO experienced a famine (Age), the mean differences in other characteristic variables were no longer significant, indicating that there are no differences between the treatment group and the control group. Panel B in Famine*EDR and Famine*EDD) was still significantly negative, which is consistent with the earlier research conclusions. The matching results based on PSM showed that the promotion effect for the CEO's famine experience on the quality of corporate earnings was not due to differences in characteristic variables.Panel A in Famine*EDR (Famine*EDD) remained significantly negative at least at the 1% level, which is consistent with the previous conclusions, indicating that the impact of the famine experience on the quality of corporate earnings was not due to the region of birth.As given in In the previous test, we used the modified Jones model as a proxy for earnings quality. To test whether the results are sensitive to the measurements of earnings quality, we retested the basic assumptions with different measures. First, the method of Dechow and Dichev  was usedFamine*EDR (Famine*EDD) are all significantly negative at the 5% level, which is consistent with the previous results, indicating that CEOs' famine experience improves corporate earnings quality. The results given in Famine*EDR and Famine*EDD are −0.632 and −0.010, respectively. They were significant at the 1% level, which still supports our hypothesis.The period of our sample begins in 2000, which may ignore the huge potential impact of the new accounting standards for enterprises in 2007 on earnings quality. In our previous model, we have controlled the annual fixed effect to control these factors. We conducted a new robustness test using the sub-sample after 2007 in this part. The results are shown in columns (1) and (2) of Year*Industry and Year*Province. The results show that the coefficients of Famine*EDR and Famine*EDD are significantly negative at least at the 5% level. The results given in Addition, some factors that have a strong influence on the motivation of earnings management are not included in the model, such as the motivation to avoid losses. We added more fixed effects to the model to weaken the possible impact of these missing variables. In columns (3) and (4), we conducted a robustness test to control the fixed effects of The aforementioned regression results showed that CEOs' famine experience was significantly positively associated with corporate earnings quality. As detailed in hypothesis 1, we considered two potential mechanisms: the risk prevention effect and the famine learning effect. Next, to reveal the internal mechanism of CEOs' famine experience affecting the corporate earnings quality, we further tested the potential mechanisms.Risk prevention effects assume that the early-life famine experience of CEOs makes them more risk-sensitive and risk-averse. Low-quality earnings information disclosure may lead to a higher risk of litigation. Therefore, CEO's manipulation of earnings information is more cautious, and the quality of corresponding earnings information disclosure is thus relatively high. Based on the above analysis, if the CEO's early famine experience affected the corporate earnings quality mainly through risk prevention effects, it can be expected that when a company's low-quality earnings information disclosure incurs relatively higher risks, the CEO's risk prevention motivation would be relatively stronger, and the association between the CEO's famine experience and corporate earnings quality should be more obvious.We measured risk intensity in terms of investor protection and whether the company was cross-listed. Regarding investor protection, the existing literature shows that in regions with high levels of investor protection, laws and regulations are more easily met, so the degree of coordination between laws and market mechanisms is relatively high, and investors' awareness of protections is more visible and intense , while in the high-IPI group, the Famine*EDR is not significant but Famine*EDD is significant at the 10% level. In the last four columns, the results show that Famine*EDR and Famine*EDD are all negative at least at the 10% level in the AH list group and only A list group. We further tested the coefficient differences between these divided groups using the chi-square method. The results indicated that the absolute value of the coefficient of Famine*EDR and Famine*EDD in the high-IPI (AH list) group is significantly larger than that for the low-IPI (A list only) group. These results suggested that when the risk intensity for low-quality information disclosure is higher, CEOs' famine experience has a more significant effect on the corporate earnings quality, which is consistent with our expectations regarding the risk prevention effect.Based on the above considerations, we used the investor protection index (IPI) from the China Marketization Index Report prepared by Fan et al.  to measuThe famine learning effect holds that the epochal characteristics and reasons behind the occurrence of the Great Famine increase the knowledge of famine-experienced CEOs. Under the influence of the learning effect, the probability of the CEO to implement earnings manipulation and disclose untrue earnings information is lower, resulting in higher quality earnings information disclosure. Based on the above analysis, if CEOs' early famine experience affected the quality of corporate earnings, mainly through the famine learning effect, it can be expected that when the corporate CEOs' learning ability and knowledge accumulation of the famine event are relatively higher, the association between CEOs' famine experience and corporate earnings quality should be more obvious.We used CEO education and professional background to measure CEOs' learning ability and knowledge accumulation related to the early famine event. Jiang et al.  found thDegree), we followed Jiang et al. (Famine*EDR (Famine*EDD) is −0.832 (−0.104) in the high-degree group and is significant at the 1% level, while in the low-degree group, the coefficient is not significant. In the remaining four columns, the coefficients of Famine*EDR and Famine*EDD in the E&M group are negatively significant at the 5 and 1% levels, respectively, while in the non-E&M group, the Famine*EDR is not significant, but Famine*EDD is significant at the 5% level. Using the chi-square method, we further test the differences of coefficients between these two groups. We found that the absolute value of the coefficient for Famine*EDR and Famine*EDD in the high-degree (E&M) group is significantly larger than that for the low-degree (non-E&M) group. In summation, these results indicated that when the CEO's learning ability and knowledge accumulation are relatively higher, the famine experience has a more significant effect on corporate earnings quality, which hold against the expectations of the famine learning effect.Based on CEO education and professional background data compiled in the CSMAR database, we manually collected samples to supplement the missing data as much as possible. To measure CEO education  and the 2021 project of Guangzhou Planning of Philosophy and Social Science (Grant Number: 2021GZQN05).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Telepsychiatry is the best-documented e-Mental Health application. It refers to the use of videoconferencing in the provision of mental health services. During the COVID19 pandemic, in response to physical distancing, mental health services worldwide have turned to online consultations. For the vast majority of clinicians, it was the first time they use telepsychiatry, and very few have received training in how to do it.- to present the main objectives and messages of the WPA Global Guidelines for Telepsychiatry related to competencies & skills, educational & legislative needs, and international collaboration.A structured review of the main challenges, innovations, and settings in the first Global Telepsychiatry Guidelines, published by WPA in February 2021.The benefits of increased access to telehealth services are apparent for telepsychiatry, but benefits can only be realized if the tools are used by clinicians who have the appropriate training and guidance. With proper preparation and thoughtful risk management, telepsychiatry can be an invaluable tool for allowing greater access to care. However, certain prerequisites must be fulfilled to achieve the desired goals. These prerequisites are e.g. choice of the technology, settings, patient/provider preferences as well as competencies and skills described in this document.The need for training among health care professionals is the highest priority. The urgent need for clinical training and skills building around e-mental health inclusive telepsychiatry, will determine the influence that psychiatry can have in addressing the mental health sequelae of the COVID19 pandemic via competent practice and increased international collaboration.I am the main author of “WPA Telepsychiatry Global Guidelines”


To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research , and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice. According to the reports of the International Energy Agency (IEA), despite the destabilization of global energy consumption due to the pandemic, electricity demand will reach pre-pandemic levels in 2022, and continue to increase . AnalogoAfter excluding unrelated topics such as economics, psychology, etc., a total of 7187 documents were collected. The chart in Overall, the concept of online condition monitoring using digital sensors and communication protocols, or the concept of digital switchgear , is theThe longest standing method for the development of switchgear was based on optimizing the physical and electrical properties of the components that make up the current path. Some examples of previous and still ongoing optimization efforts of the busbar configuration are achieved either by geometric distribution to reduce losses , reductiresistive or capacitive voltage dividers as voltage sensors;Rogowski coil as current sensor; current sensor with non-saturable magnetic core. As mentioned, the development of microcontroller-based IEDs, which do not require the high-level outputs of ITs, enabled the implementation of sensors in switchgear. The operating principles of these sensors, which replace traditional ITs in measuring voltage or current, are not new, but the area of implementation is. Some of the most popular solutions offered to replace obsolete components are:non-saturable, since no iron core is used;high degree of accuracy;increased personnel safety (low secondary voltages);small size and weight;wide dynamic range;environmental friendliness, as less raw material is used; no damage caused in case of overload.The general advantages offered by these sensors compared to conventional ITs are:A more detailed comparison of conventional ITs with current sensors and conventional ITs with voltage sensors is presented in . two incoming feeders; twelve outgoing feeders. To illustrate the energy savings from the use of sensors, a typical panel model with conventional current transformers is compared to a model with implemented current sensors in . The selThe results show that energy consumption can be significantly reduced by the use of sensors, especially in the long-term comparison, contributing to the worldwide efforts to reduce energy consumption. In view of the above comparison, it is obvious that replacing IT with sensors represents a major advance in the field.As mentioned earlier, the predominant transmission of measurement data in the IT era was to hardwire the output to the desired point of data manipulation. This severely limited the scope of data transmission and the ability to use the data for a larger purpose. Sensors, with their low-power signals, simplified the transmission of measured data, but the broader application of measured values at the switchboard level, or between multiple IEDs and a central management station, required the development of a standardized communications protocol. The development of the IEC-61850 communication protocol can be considered the foundation for the switchgear digitization process. As explained in , “IEC 61Considering the various phenomena that manifest the symptoms of failure modes in switchgear, such as the increase in temperature as the electrical connections deteriorate, the effect of increased humidity on the deterioration of insulation, and the development of partial discharges, which are also indicative of various deteriorations of switchgear operation, the need to measure various parameters is obvious. For example, a study was performed to analyze failure phenomena and characteristics, while also highlighting the complex relationships among cause, effect, and failure modes in switchgear . temperature sensors;humidity sensors;partial discharge sensors.Based on the parameters they measure, sensors can be divided into the following categories:surface acoustic wave (SAW) temperature sensors;infrared (IR) temperature sensors;IR window implementation;fiber optic sensors.A temperature rise at the electrical connections, as shown by several examples in ,30, can SAWs measurement technology offers the ability to passively measure temperature, with the biggest advantage being that it is wireless. The sensors themselves contain an inter-digitized transducer (IDT) that is interrogated wirelessly. The major advantage over active sensors, especially if they are not easily accessible, is that they do not require battery replacement. The sensitive element is a piezoelectric quartz substrate housed on a hermetically sealed stainless-steel base. The sensor can be securely attached to the surface to be measured with a screw .The most popular IR solutions used in switchgear assemblies are the separate IR temperature sensors housed in the switchgear  or manuaThe SAW temperature sensors mentioned above can also be used as humidity sensors . The impSimilar to the importance of a temperature rise in switchgear, the occurrence of a partial discharge is a sign that stable operation of either the switchgear or a major component has been compromised. Depending on the magnitude of the partial discharge, the consequences can lead to critical failures if not detected as soon as possible, underscoring the importance of measurement technology. Due to the nature of the partial discharge phenomenon, several paths can be taken to predict or detect it, and the detailed explanation and comparison of different possibilities can be found in . With the different types of data that can be collected by different sensors, the question of the influence of sensor accuracy or measurement uncertainty arises when they are translated into different methods of measurement data manipulation (described in the following chapter). Since every measurement is subject to inaccuracies, repeated measurements lead to fluctuations, which are mostly caused by random effects . Consequently, the measurement uncertainty caused by sensors significantly affects the accuracy of machine/deep learning models, especially in safety-critical applications. To combat this recognized problem, several studies have been conducted with positive results, such as , which iThe digital applications used in the “smart grids”, an intertwined term used to describe the digital distribution network, i.e., all distribution components including the switchgear, as investigated in  in 2019,Where, in system balance real time condition monitoring and anomaly detection/failure localization, related papers make up 32%, respectively, process optimization digital twin and predictive maintenance-related papers make up 75%. The significant increase of research in the field in the last two decades has seen an exponential growth of predictive maintenance-related papers, while machine learning-infused predictive maintenance papers present around 44% of all related publications between 2020 and 2021 . The proIn the above figure, the orange arrows depict the bidirectional flow of information, and condition monitoring is utilized in the following scheme for data acquisition that is further processed for use with the purpose of fault detection or predictive maintenance. Conversely, the results of the analysis of predictive maintenance or the detection of faults can improve the equipment monitoring by providing data as to optimize the location of sensors or add/remove different types of sensors. In the following subsections, the various applications in each category will be exemplified, but due to significant interconnection they are not always easy to differentiate. labor-free measurements;accurate data due to real time operational measurements;improved service decision due to failure start/progress information;unnecessary maintenance reduction due to decisions based on data from continuous measurements; prioritization of equipment repair order.The process of monitoring multiple conditions of an object  that are considered to be health indicators, in order to identify changes in operation that indicate the development of a possible fault, is called condition monitoring (CM). It is a significant part of predictive maintenance, as the acquired data is used to optimize and perform scheduled and preventive actions in order to prevent asset failure and unwanted downtime. In the case of a marine switchgear, condition monitoring (and the implied predictive maintenance and fault detection processes) is a viable tool for increasing operational safety, efficiency of the equipment, and reducing major failures risk and the accompanying possibility of arcing faults during maintenance and repair operations that are described in . The opeIf a full condition monitoring system for switchgear is utilized, such as in , it woulConsidering the influence of partial discharges on the development of catastrophic failures, determining the source location (of the defect) is of great importance in maintenance and repair procedures. The implementation of sensors in switchgear automates the detection of faults based on real time operational data obtained through condition monitoring. Confirming that partial discharge has occurred is not enough for failure prevention, unless the location of the discharge is provided in order to determinate the insulation state. One possible solution for defect localization is by utilizing transient earth voltage (TEV) sensors, as in , in whicConsidering that predictive maintenance leverages operational data  captured through real time condition monitoring (if implemented) and provides a significant amount of data, the need to automate the recognition, classification, and prediction of potential failures/anomalies is obvious. Until recently, the classification and detection of failures, degradation, or defects have been conducted manually, which had the disadvantage of depending on the expertise of staff. More recent trends use artificial intelligence (AI) algorithms to automate all or part of the data analysis process. In the literature, the terms AI, neural networks, machine learning, and deep learning are often used interchangeably, giving the impression that they are not easily distinguishable. In short, machine learning is a subset of AI, while deep learning is a subset of machine learning, with neural networks being the basis of deep learning, which is distinguished by the number of node layers , where a deep learning algorithm must have more than three; the subdivision is shown in According to , the algThere are many more applications of AI, but the common outcome is the detection or prediction of faults or the assessment of the condition of assets, which are then used to make decisions about predictive actions to reduce unwanted downtime in the power grid. In recent years, research has shifted primarily to deep learning (DL) because “traditional” machine learning techniques rely on feature extraction using a predefined set of rules, i.e., rely on the programmer’s accuracy. Deep learning provides a solution to this problem as it can use raw data, and feature selection is integrated within the learning process . A comprThe concept of digital switchgear, as presented in this paper, is an emerging technology, the benefits of which can be seen as a useful addition in monitoring and optimizing power distribution processes, in monitoring the condition of equipment, and as an essential component requiring the implementation of predictive maintenance procedures. Data collection by the various types of implemented sensors will inevitably lead to the establishment of “big data” in distribution networks, which is a compelling step for the full use of various machine/deep learning techniques, as they require large data sets to achieve the desired predictive accuracy. The principles of machine learning/deep learning, or more precisely, their training, are also a major challenge since most techniques are currently trained on data generated either artificially or under laboratory conditions that lack the complex relationships between various parameters in the real world. In the case of , the goaAnother problem that needs to be emphasized is the impossibility of directly comparing different approaches. There is no question about the accuracy of each study, but it is not necessarily the best standard of comparison, since each method has been validated under different conditions and frameworks. Several studies have compared different approaches using the same data set; they are listed in Following these examples on a global scale, it would be interesting to measure the accuracy of different methods under the same conditions if a well-structured and freely available data set were formed, which would facilitate the tracking of the most recent accuracy achieved and most optimal method. Therefore, future research efforts in both land and marine applications must focus on the creation or acquisition of a freely available database for future analysis to optimize learning algorithms, since fitting each method to a custom database is impractical at this scale. A possible solution to this problem could be to extend or modify the existing COMTRADE file format. COMTRADE is a format for files containing transient waveform and event data  collected by power systems. The format itself is standardized by IEEE C37.111-2013 , which iThis paper describes and analyzes the path of switchgear digitization, divided into past challenges and their solutions, past and current research on their applications, and further divided into condition monitoring, fault detection, and predictive maintenance. The use of machine/deep learning in predictive maintenance is highly appreciated as it enables effective prediction of potential faults by removing the major obstacle of reliance on manual labor for data analysis. The conclusions from this work can be summarized as follows.Since the framework for conducting experimental studies varies, there is an obvious need to “standardize” the procedures, which are also categorized by the type of data used . “Standardization” would lead to a universal framework for data collection (creation of a freely accessible database) that could be used to accurately compare the developed algorithms, and adequately monitor improvements in the application of machine/deep learning algorithms in prediction maintenance.The developed dataset could also be used in digital twin simulations of both shore and marine power grids to increase the overall model accuracy, as real data would be implemented, and as the complex dynamics of switchgear are mostly ignored today. Overall, digital twin models offer great optimization opportunities, especially in the maritime domain, which are in line with IMO’s future goals.The development of digital switchgear will lead to an effective predictive maintenance plan that will be continuously optimized with the growing measurement database and machine/deep learning analysis. Repair procedures are optimized, as the source of PD is automatically located, reducing labor and replacement costs. In summary, digital switchgear provides a safer and more cost-effective distribution system compared to its “analog” counterparts. Safety and reliability are enhanced by the active monitoring of equipment to predict failures, and there is a reduction in manpower requirements for data interpretation and equipment monitoring/repair procedures.As mentioned, the biggest barriers to machine/deep learning adaptation are unbalanced, inconsistent, and limited data sets. The significant lack of data directs research toward generating data that mimics real-world data, but the methods are also inconsistent. Therefore, future work should address the development of a universal dataset, as all studies consequently lack the important step of validation through direct comparison of the proposed methods, which prevents tracking progress and consequently slows it down. The authors future work will include a further investigation of feasible file systems with the aim of solving this issue.


This article aims to share the online collaborative experience of interprofessional teamwork among healthcare undergraduate students based on community learning during the coronavirus disease 2019 (COVID-19) pandemic in Chile. This experience took place in 48 different communities in Chile from November 10, 2020 to January 12, 2021. It was a way of responding to the health education needs of the community when the entire Chilean population was in confinement. Students managed to adapt to the COVID-19 pandemic despite the challenges, including internet connectivity problems and the limited time available to do the work. The educational programs and videos shared in this article will be helpful for other interprofessional health educators to implement the same kind of program. This study was conducted among 650 students from the 8 healthcare undergraduate programs , grouped into 28 teams with 28 professors from the Faculty of Medicine of the University of Chile, and 48 leaders from social organizations. This experience took place in 48 different communities of Santiago, and the rest of Chile during the COVID 19 pandemic, from November 10, 2020 to January 12, 2021  pandemic. This program was done through online educational interventions in healthcare seeking to satisfy community needs. It also pursued the strengthening of students’ skills for social commitment, respect for diversity, and a rights approach. It was conducted in collaboration with community leaders. Information about the interprofessional education program can be accessed through the Faculty of Medicine of the University of Chile available at: 12, 2021 .This article aimed to share the collaborative experience of interprofessional teamwork, including programs and video files of education for community residents.In 2020, due to the COVID 19 pandemic, all activities were performed online . This waStudents had to carry out an online healthcare educational project based on community needs related to the COVID-19 pandemic care such as hand washing and alcohol gel disinfection, mental health care during the pandemic, and responsible pet ownership. During this experience, effective communication stood out, in the virtual framework with people and groups in different areas of this intervention, as participants adhered to ethical and bioethical principles in their praxis. At the same time, the experience set out new challenges related to accessibility and coordination with community leaders via new communication technologies such as Zoom, Meets, WhatsApp, Instagram, YouTube, and infographics.Students inquired into their former knowledge associated with disciplinary and experiential spheres. They also faced situational contexts, which presented an opportunity to apply that knowledge to community education during the COVID-19 pandemic. The experience enabled dynamism and the acquisition of teamwork skills through a virtual modality. Students also valued the contribution of different professionals and community roles to face a problem that had characteristics transferable to their future professional performance. Although the experience was positive, it would still be premature to draw a definitive conclusion regarding the impact of this modality of work on the skills acquired through interprofessional education [The material developed by students was shared with the community for educational purposes: educational videos, audio reports, and infographics were generated and published on Instagram –4.Students were able to adapt to the COVID 19 pandemic situation to educate community residents despite challenges, including a lack of internet connectivity and limited time available to do the work. The flexibility of all team members and their commitment to carry out the community project stood out as important factors. The implementation of this program was achieved by planning, articulating, implementing, and assessing an intervention that became useful for the community and the team. The educational materials and videos shared in this article will be helpful for other interprofessional health educators to implement the same kind of program.


We asked the contributor to drill-down to the fundamental questions in order to define the baseline upon which we can build our understanding and establish the future pathways for the field. What are the most typical cases? Is there a more frequent case type? Does the way the issues are being solved reflect advancements in the discipline in the country? Are standards procedures being followed? What are the main obstacles faced by the experts in this field?What is the current state of Forensic Anthropology in Brazil and where do we stand when identification is concerned? Coming to terms with the This special issue compiles papers from experts from five different states: Minas Gerais (3 papers); Rio de Janeiro; Distrito Federal (3 papers); Santa Catarina; and Paraíba, shedding light on the forensic anthropology perspective of practice in the identification of the deceased in the country.The experts invited were asked to tell us how their institutions adapted to the new challenges of this century and what they are doing in terms of groundbreaking research.This special issue was born on an idea based on the knowledge of one of the authors (EC) regarding the forensic reality of Brazil. After more than 20 years of on-site work, she felt the need to share what was being done in the country. Because of that, several Brazilian experts were invited to participate and to present a typical case related to the reality of forensic anthropology in Brazil, whose major issues/difficulties will spark discussion. To this end, it was presented to inspire a debate on the larger set of challenges doing forensic/human rights work in Brazil and the reasons that make pursuing this work so important. Invited authors were forensic anthropology practitioners in Brazil representing the main cities in which practice is being pursued. They were asked to present a paradigmatic case that highlights the difficulties that they are faced with in general when performing casework. Likewise, they were also asked to explain, more specifically, the problems that this particular case has posed in terms of its assessment and identification, offering suggestions for why it is/was a complicated, hard case to resolve; and to discuss the strategies  that they believe may be helpful to overcome these challenges now and in the near future. Above all, we hoped to show the Reality of Forensic Anthropology in Brazil, specifically in the context of identification of the dead. Furthermore, we aimed to stimulate research and scientific publications.Forensic Sciences Research was the optimal recipient with a strong peer review system, which led to the rejection of four of the planned articles. Yet, we believe that the ones now published fulfill the requirements and are successfully representative.Two main achievements resulted from this launched challenge. First, a symposium—unfortunately held online due to COVID-19 restrictions in 2020—with a large number of participants and where the presentations achieved a good quality, illustrating the context and cases of six different Brazilian states, sponsored by Centre for Latin American Studies, from Stanford University, where the senior guest editor (EC) held a fellowship. The success of the symposium led to the idea of this publication: there was an obvious need to disseminate what was being done in Brazil regarding forensic anthropology and create a discussion around these particularities. This second challenge was accepted by the large majority of the symposium presenters. It took more time than previewed, in part because the practice of publishing peer review articles in Brazil in this field is not well established. The remarkable work done by the team of Belo Horizonte with the mass disaster due to the collapse of a mine tailing dam in Brumadinho, stands out for their main achievements . The facSilva , from BrAlso from Brasília, Damasceno et al.  provide The third contribution from this city, Filho  brings uLemos et al. , from BeAnother case study is from the team of Marcos Paulo Machado from Rio de Janeiro , where tFrom Paraíba, we have a case report by Evelyne Soriano Pessoa and her team . It dealFinally, the third contribution from Belo Horizonte assesses the epidemiological and toxicological profile of officially confirmed suicide victims in a 1 year period .We are delighted about the final format of this issue since it reflects the specificities of forensic anthropology in Brazil, a reality quite different from the one of other countries in Latin America. Yet, the continental dimension of the country, the severity of violence, and the shocking numbers of missing persons are finding a more solid answer from the discipline under question. There are now more forensic anthropology research centres, mainly located at the universities ; identified osteological collections are now systematized and organized, and capacitation/education on the field at the level of postgraduation is now happening and being sought. The Brazilian Association of Forensic Anthropology (ABRAF), which is currently commemorating 10 years, was also a big step forward in developing the discipline. Definitely, Brazil is now on the world map of Forensic Anthropology and its practice.National Institute of Legal Medicine and Forensic Sciences, Coimbra, Portugaleugenia.m.cunha@inmlcf.mj.ptBridget Algee-HewittCentre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, PortugalCCSRE Center for Comparative Studies in Race and Ethnicity (CCSRE), Stanford University, Stanford, CA, USAMelina CalmonGrupo de Pesquisa em Antropologia Forense e Identificação de Pessoas – ANP/CNPq, Lago Sul, BrazilEugénia Cunha


African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a case fatality rate approaching 100% in domestic pigs. The main objectives of this study were to describe the spatiotemporal analysis as well as to assess the potential risk factors along the pig value chain in Lao Cai province, Vietnam. A total of 925 outbreaks were reported from 2019 to 2020. The three clusters  were observed near the Chinese border. The most temporal clusters were detected between May and August during the study period. In addition, we evaluated the association between ASF outbreak locations to the nearest main roads and elevation. For ASF outbreak locations to the nearest main roads, compared with the reference , <1,000 m (10.22 times) and 1,000–2,000 m (1.98 times) were significantly higher occurrences of ASF. For elevation, compared to the reference , the farm locations with <500 m (55.31 times) showed a significantly increased risk of ASF outbreaks. Farmers perceived that the highest risk of ASF transmission may come from collectors and slaughterers, intermediaries inside and outside the commune, feed agents and maize agents in the commune, and pig retailers. Both commercial and household pig producers considered minimizing the number of people going in and out of pig stables and improving healthcare and husbandry procedures to be both very important and feasible. There is a need for compliance by all pig producers and other actors in the pig value chain to adopt biosecurity practices. Therefore, awareness, knowledge and understanding of infection and risks of ASF need to be improved. Veterinary officials at the provincial and district levels need to improve capacity and resources to perform laboratory analysis for ASF and need to coordinate with local actors on the control and prevention of ASF in the community. African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a case fatality rate approaching 100% in domestic pigs , 3. ASF Culex mosquito abundance using remote sensing data  for Lao Cai province, accounting for 25–36% of the total number of pigs and over 40% of the total number of poultry.Lao Cai is a highland province located in the northwest region of Vietnam, on the border with Yunnan province of China. There are estimated human and pig populations of 7,33,300 and 3,75,647 in 2019, respectively , 22. Thehttp://www.satscan.org), which is commonly used to detect space and space-time clusters in public health sectors  were created around roads  with local authorities  and pig producers. FGDs are most commonly used as a qualitative method to gain an in-depth understanding of current issues. In addition, the initial findings were validated in a stakeholder feedback workshop which was organized on 13 Jan 2021. Before the interviews, the interviewees signed a consent form after either reading or listening to the text. This research was approved by the Hanoi University of Public Health Review Board (No. 186/2020/YTCC-HD3), Vietnam. All relevant questionnaires are available .A total of 925 outbreaks were only reported in domestic pigs from 2019 to 2020. More outbreaks were reported in 2019 (708) than in 2020 (217). Using the spatial window set at 50%, a total of sixteen clusters were identified . The pri8/1,019) . The sec8/1,019) . For eleA total of three groups (7 people / group) took part in the discussion . In general, it took 2–3 h for the FGDs.In Lao Cai, the first outbreak was reported in May 2019 and then spread rapidly across the province. It was assumed that the main reasons were as follows:uncontrolled slaughter points in residential areasfarmers' use of swills from restaurants and kitchens as pig feedkeeping of livestock close to family kitchenspeople moving from households where pigs had ASF to other householdsthe officials' non-compliance with anti-epidemic regulations direct spread through natural mating service or trading of pig sementhe discarding of dead pig carcasses into the environmentsmall households' low adoption of biosecurity practicesannouncement of ending ASF outbreak of some communes may have caused subjective psychology in disease prevention of some livestock farmers and local authorities.In 2020, in addition to the above reasons, a number of new issues contributed to the spread of ASF. First, pig producers killed infected pigs near roads or water sources in contravention of technical procedures by not wearing biologically protective clothing and burying pigs through middlemen. Second, producers only destroyed infected pigs but not pigs in the same cage, causing diseases to spread and prolonging the outbreak. Third, the official mechanisms participating in disease prevention and control at the district and communal levels were sometimes inconsistent and erratic. Lastly, some pig producers did not report cases of ASF on their farm to officials or delayed their reports, allowing the epidemic to spread widely and making it difficult to control.Two FGDs were held with farmers, each with different production scales. This Venn diagram  showed lThis research was the first attempt to assess the spatiotemporal analysis of ASF using local surveillance and remote sensing data, as well as to evaluate the risk factors in the pig value chain in Lao Cai, Vietnam. It was possible that ASF cases in the local surveillance system were likely to be underestimated because small-scale farms (accounting for 65−70% of the pig population in Vietnam) lacked knowledge about livestock diseases (including ASF) and were less reachable to veterinarians and animal health workers. Moreover, all pigs must be slaughtered when a new case is confirmed on the farm, which may have resulted in reluctance for reporting.The primary, secondary and fifth clusters were observed near the Chinese border, which might be highly associated with the illegal movements between Chinese borders as the first ASF case was reported in China  and rapiOur study found that the distance to the road and elevation were reversely associated with the ASF outbreaks, which was consistent with previous studies , 26. ActSince the first case of ASF was detected in northern Vietnam in February 2019, the number of affected provinces rapidly were increased within a short period of time (<4–5 months). Some potential risk factors have been proposed, such as illegal trading activities; low biosecurity; feeding of food scraps to pigs; not fully culling all pigs in infected farms, disposal of pig carcasses to public areas. Moreover, increased human and animal movements during the Tet holiday (Vietnamese New Year in late January or early February) may have played a significant role in spreading the ASF virus across the country.It has been well recognized that wild boars and soft tickets can play a significant role in the transmission of the virus –36. In AOur research had some limitations in that it was likely that ASF surveillance data were underreported due to lack of awareness, animal health professionals and laboratory facilities in rural and mountainous areas. Especially, farmers were reluctant to report to the authorities because of low compensation rates and complicated/prolonged administrator procedures. In addition, it was assumed that the pig population did not rapidly change during the research period, which is very important for a space-time permutation model. Therefore, it was possible that our detected clusters may have been affected by the pig population at risk if the background population dramatically increased or decreased in one area compared to another.During the stakeholder feedback workshop, participants discussed the risk factors for ASF transmission, especially the differences in risk levels between commercial farms and household farms. Although the risk of disease on any farm was tied to many factors, the human risk factors were lower on commercial farms because most of them applied high biosecurity procedures. In addition, they did not allow visitors, including traders, into the barn areas. However, for small-scale producers, the human movement was one of the most important risk factors as they applied fewer biosecurity practices and did not control their neighbors or traders who may move in and out of the production areas, which was consistent with a previous study . Mostly,According to the interviewed pig producers, the greatest difficulties for restocking pig herds after ASF outbreaks on the farms and in the region were concerns about re-infection with ASF, high cost of breeding pigs and lack of capital for both household and commercial farms. In addition, for household farms, access to breeding pigs with known origin was also difficult. Restocking of breeding sows was slow due to difficulties in purchasing quality breeds, especially exotic breeds. Restocking fatteners was also slow due to the high price of commercial piglets. ASF outbreak reoccurred in the province in February 2020 and continued to spread across the province. However, large numbers of households still did not have a deep understanding of diseases and biosecurity. Currently, the majority of people believe that any sickness or death among pigs is caused by ASF, causing farmers to be hesitant about restocking.Among the technical solutions discussed, both commercial and household pig producers considered minimizing the number of people going in and out of pig stables and improving healthcare and husbandry procedures to be both very important and feasible. The interviewees addressed the other most important practices were strengthening decontamination and disinfection (using lime and hormones with higher frequency), disinfection of transport vehicles and killing of mice, flies and mosquitoes. These solutions were also considered highly feasible for adoption on the ground. As for appropriate policies to overcome ASF, household farms suggested that they would expect compensation or support when their pigs are depopulated due to ASF, as well as support for purchasing disinfectants. In addition, the commercial farms expected support for purchasing disinfectants, farm materials and equipment. The priority for training needs in the context of ASF is quite similar between households and commercial farms. These include the detection and recognition of ASF and emerging diseases; understanding the risks of ASF infection and how to prevent it; training on breeds and controlled breeding practices; safe artificial insemination practices; and technical knowledge of sanitary and disease-free pig housing.There was a high risk of ASF transmission from traders in the pig value chain, including collectors, slaughterers and retailers at the provincial, district and commune levels. These actors all participated in the sale and purchase of pigs that could be infected. Furthermore, live pigs and pork products were not subject to quarantine within the province, and their movement was not controlled. In addition, when an epidemic occurred, it took a long time to identify the disease due to the lack of resources and capacity among local animal husbandry actors and the inability to analyse samples locally for detecting the ASF virus. This allowed time for pathogens to spread through transportation and sales of sick pigs. Every year, Sub- DAH officers have been trained in disease prevention, control and surveillance. However, in-depth professional knowledge was still lacking. At the district level, some stations lack livestock and animal health specialized staff. In some districts, the specialized veterinary staff they have were sent to work other jobs. The veterinary officers at the provincial and district levels have limited resources and capacity for monitoring and surveillance of ASF. There is a need for compliance by all pig producers and other actors in the pig value chain to adopt biosecurity practices. Therefore, awareness, knowledge and understanding of infection and risks of ASF need to be improved. Veterinary officials at the provincial and district levels need to improve capacity and resources to perform laboratory analysis for ASF and need to coordinate with local actors on the control and prevention of ASF in the community.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Hanoi University of Public Health. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Hanoi University of Public Health.HL designed and ran the statistical analyses, wrote the draft of the manuscript, and prepared all the figures and tables presented. TD and LH collected the data. All authors contributed to the conception and design of this study and editing and comprehensive revision of this manuscript.This study was funded by the CGIAR Research Program on Livestock.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Recently proposed concepts regarding the nature and assessment of the thyroid state have provided a model more consistent with empiric evidence. It now appears likely that there are no such entities as thyroid set points and individual euthyroidism. Rather than there being discrete thyroid states, peripheral organ parameters are associated with thyroid function in a continuous manner. Thyroid hormone levels and, in particular, levels of free thyroxine now appear to be superior to thyrotropin levels as indicators of the thyroid state. Complicating the assessment of the correlations of the thyroid state with pregnancy outcomes are the contribution of the placenta to maternal thyroid function, fetal thyroid development, the multiple potential pathways to any particular outcome, the likely presence of small critical periods of time, the differing genetics of fetal and maternal tissues, and the unreliability of thyroid hormone assays. Nevertheless, there is no apparent reason for there to be a change in pregnancy to the basic principles of thyroid hormone action. The relationships between mild abnormalities of the thyroid state and pregnancy outcomes and the value of treating such mild abnormalities remain uncertain and controversial. The evidence suggests that further investigation of these clinical questions might better be based on thyroid hormone, particularly free thyroxine, levels. In the investigation of borderline low thyroid states, the categories of subclinical hypothyroidism and isolated hypothyroxinemia might both be abandoned with attention being directed to low free thyroxine levels regardless of the thyroid-stimulating hormone (TSH) levels. For these changes to occur, there would ideally be improvements in the assays for free thyroxine in pregnancy. The evidence suggests that, just as in the non-pregnant situation, pregnancy guidelines based on thyrotropin levels may need revision. Recent clinical evidence –3 has inMost of the research underpinning the above concept has focused on the non-pregnant state. Nevertheless, the meta-analysis of the associations of clinical parameters with the different thyroid function tests did include pregnancy outcomes, and the relevant results were identical to other clinical parameters in that FT4 levels, as compared with TSH levels, were more often associated with outcomes .Though there is evidence to suggest that some of the associations between mild thyroid dysfunction and clinical outcomes are on the basis of causation, there is no convincing evidence that treatment of mild thyroid dysfunction is effective , 10. ThiThe approach to thyroid dysfunction in pregnancy may even be more controversial than in the non-pregnant state. For example, much thyroid/pregnancy research has been devoted to the associations of subclinical thyroid dysfunction with hypertensive disorders of pregnancy and (pre)eclampsia , 16, butThough our meta-analysis  did suggIn the non-pregnant state, the outline of physiology is relatively simple in that the thyroid gland secretes thyroid hormones in response to TSH and these hormones feedback negatively on pituitary TSH secretion. Subsequently, clinical parameters may or may not be influenced by the levels of the thyroid hormones. The details of this system, e.g., receptor and deiodination physiology modulating local actions of thyroid hormones , add a lvia the secretion of human chorionic gonadotropin (hCG)   and probin (hCG) . Althougin (hCG) . Later iin (hCG) . The risin (hCG) . The prein (hCG) , 36, 37.in (hCG) . Furtherin (hCG) , 39, morvice versa. Perhaps the initial influence is exerted by the preconception thyroid state  , 57. In rimester , 58 poterimester , 58. Therimester , 59. Thirimester .The thyroid status of the placenta may not be a simple concept, with there being evidence that placental exposure to thyroid hormones may not be uniform with the maternal placenta being exposed to greater levels than the fetal placenta .Thus, though maternal tissues, the maternal placenta, the fetal placenta, and the fetus are likely to have intracellular thyroid hormone levels related to the maternal circulating levels of thyroid hormones, this relationship is more complicated than in the circumstances of considering outcomes in non-pregnant individuals.The complexity of the causation of the outcomes in pregnancy, and the multiplicity of tissue thyroid states to consider, may contribute to the conflicting results of associations of pregnancy outcomes with hormonal status while obscuring any potential intervention targets.We have indicated that, in general, FT4 is the better indicator of the thyroid state of peripheral tissues . In our At the most fundamental level, the physiology of thyroid hormone levels does not change with pregnancy. Thyroid hormone and TSH levels are determined by the thyroid hormone /TSH feedback loop. The evidence indicates that the levels represent balance points of the physiological processes at play rather than the previous concept that the levels are genetically set and physiological processes act to defend these levels . The chaIt has also been previously thought that the genetically determined “set-point” of thyroid hormones of each individual represented an optimum level for the individual . It follT4 is found in coelomic fluid in early pregnancy in proportion to maternal levels  and the The recent meta-analysis by Toloza et al.  suggesteIn pregnancy, therefore, we have conflicting studies both in terms of outcomes and in terms of the relative association with TSH or FT4 with these outcomes in the context of a lack of strong evidence as to whether the nature of any association is causal due to reverse causation or due to another mechanism.On theoretical physiological grounds, TSH was previously thought to be the best indicator of the thyroid state. We have indicated above that now the physiological evidence would favor FT4.On the other hand, there are problems with most if not all FT4 assays in pregnancy , 36, 37.via the stimulation of TSH. In these circumstances, on account of the initial causation, the parameter would initially be associated with low FT4 levels and high TSH levels, the association with the high TSH levels being on account of the inverse correlation in the population between FT4 levels and TSH levels  stimulated thyroid function H levels . The revH levels . The stuH levels  is potenAny relationship of an outcome with TSH might also potentially result from a relationship between TSH and any other parameter, e.g., hCG , 40, 41 Finally, a stronger association with TSH might be seen should TSH directly act on placental or fetal tissues. In the non-pregnant state, this phenomenon has been recognized. TSH has been reported to have effects on the heart  and on bDespite the problems with FT4 assays and the possible causes of spurious TSH relationships, there are studies that report superior relationships with FT4 –71. Thervia the production of estrogen  with pregnancy outcomes. The physiological models and empiric evidence are not mutually coherent. These deficiencies may originate in part from the fundamental emphasis on TSH levels in the assessment of the peripheral thyroid state. We found no reasons to consider that in pregnancy, the conceptual changes regarding the nature of, and the general assessment of, the thyroid state do not apply. By extension, the clinical and research implications for pregnancy might parallel the general implications  and inclIn pregnancy, the complex physiology/pathophysiology and the difficulties of obtaining an accurate measurement of FT4 complicate the application of these implications.STF Conceived paper, wrote initial draft. NB provided mathematical support, revised the draft. SAF collaborated with review of obstetric physiology/fetal development. HF revised draft substantially. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.