To establish the link between structure and function of any large area of the neocortex, it is helpful to identify its principles of organisation. One way to establish such principles is to investigate how differences in whole-brain connectivity are structured across the area. Here, we use Laplacian eigenmaps on diffusion MRI tractography data to investigate the organisational principles of the human temporal association cortex. We identify three overlapping gradients of connectivity that are, for the most part, consistent across hemispheres. The first gradient reveals an inferior–superior organisation of predominantly longitudinal tracts and separates visual and auditory unimodal and multimodal cortices. The second gradient radiates outward from the posterior middle temporal cortex with the arcuate fascicle as a distinguishing feature; the third gradient is concentrated in the anterior temporal lobe and emanates towards its posterior end. We describe the functional relevance of each of these gradients through the meta-analysis of data from the neuroimaging literature. Together, these results unravel the overlapping dimensions of structural organization of the human temporal cortex and provide a framework underlying its functional multiplicity.The online version of this article (10.1007/s00429-020-02047-0) contains supplementary material, which is available to authorized users. Understanding the relationship between brain structure and function is one of the main challenges of neuroscience   were selected  was created on the cortical surface of each hemisphere by merging all individual temporal subregions  from the aparc segmentation  was estimated using maximum likelihood estimation (MLE) of intrinsic dimensionality  .To investigate how the connectopic topographies for a given grey matter area are related to connections with underlying white matter, we created tract projection images for each eigenvector. First, and for every subject, we created a projection skeleton in volume space, showing for each voxel in the target hemisphere how often a streamline from the seed had reached it. This projection skeleton was created by thresholding (such that only voxels visited by at least 1% of streamlines or a given seed vertex were considered) and binarizing the matrix resulting from FSL’s PROBTRACKX. Second, we populated each voxel of the projection skeleton with the weighted average connectopy value of the top three vertices whose streamlines hit that target voxel the most, thereby producing the projection images. Each connectopic map will have one projection image associated with it.Every projection image was multiplied with individualised thresholded white matter tract tractograms obtained using FSL’s XTract —a toolbox and library of standardised tractography protocols devised for the robust automated extraction of white matter tracts—following the tract definitions described in Mars et al.  to separk = 2 for both cross-subject and cross-session ICC. Both cross-subject and cross-session ICC were defined as the bootstrapped 95% confidence interval of the means of their respective definitions. The bootstrap was made with 10,000 samples.To assess cross-subject and cross-session reliability of the connectopic maps, the intra-class correlation coefficient (ICC) case 2.1 , the code to perform Laplacian eigenmapping and tract projection is part of the MR Comparative Anatomy Toolbox . Data are available as part of the Human Connectome Project (www.humanconnectome.org). Results files will be made available as part of a Data Sharing Collection on the Donders Repository (see link at www.neuroecologylab.org) and on https://github.com/gfreches/temporal-lobe-organisation. Neurovault deciles can be found in https://neurovault.org/collections/5575/ (for left hemisphere) and https://neurovault.org/collections/5594/ (for right hemisphere).The code to perform tractography is part of FSL  separated the AF from all remaining tracts. This suggest that the radial appearance of this gradient on the cortical surface is mostly driven by the unique connectivity profile of the AF. Nevertheless, there seems to exist groups of tracts that lie closer or further away from the AF in this axis. These groups constituted by the ILF and the MdLF (closer), the IFOF and UF (medium separation), and the AR (most separated).A plot showing the relative positions of the white matter tracts of the first and second connectivity modes is shown in Fig. x-axis) and third (y-axis) connectivity gradients. This visualization follows the tract organisation in physical MNI space to a large extent. As mentioned before, the first gradient resembles the ventral–dorsal organisation of the tracts. The third gradient resembles the anterior–posterior distribution, with the UF projecting to more anterior parts of temporal cortex compared to all other tracts. A noticeable larger variability of the tract projection values is noticeable on the y-axis  with multimodal functional roles  represented in intermediate areas.When analysing projection images such as Fig. In short, the second dominant connectivity mode can be described as ranging from auditory tasks on its lower end, visual tasks in its centre and comprehension tasks on its higher end. In the left hemisphere, these comprehension tasks are related to language  whereas in the right hemisphere they are related to comprehension of social cues .The third mode of connectivity Figs. a and 2 aThus, the third dominant gradient of connectivity distinguishes task specific from task general regions. In the left hemisphere, the task general (anterior end) corresponds to language functions, whereas in the right hemisphere, these correspond to social tasks such as theory of mind tasks. The posterior end of this gradient of connectivity is involved in specific tasks .When trying to understand the relationship between brain structure and brain function, it is helpful to understand the underlying principles of organisation of a part of the brain. Here, we attempted to find such principles of organisation for the association cortex of the temporal lobe. We found evidence for three overlapping fine-grained white-matter modes of organisation, formed by gradual shifts of connectivity driven by specific tracts, mapping on to distinct modes of function.The first gradient of connectivity is chiefly organised along an inferior–superior axis. It is driven by tracts belonging to the ventral visual stream, most prominently the inferior longitudinal fascicle, at its lower end, and the middle longitudinal fascicle and acoustic radiation at its top end. This structural dissociation is echoed in the clear functional dissociation in visual and auditory tasks associated with the two ends of the gradient. These results are in complete agreement with previous studies assigning key roles to the ILF  consistent with the dominance here of the arcuate fascicle that connects the pMTG to the inferior frontal gyrus (ITG). Additionally, the terms ‘semantic’, ‘visual word’ and ‘word form’ were highly correlated with the higher end of this connectivity gradient, possibly reflecting the role of the arcuate fascicle in mediating communication between the posterior temporal and inferior frontal cortex, particularly on the left hemisphere , each explaining some attribute of the connectivity fingerprint and related to a functional dimension. But, while we have described and discussed gradients of connectivity as representative of different organizational principles of the temporal lobe, there is an important orthogonality constraint, resulting from the algorithm choice (Laplacian eigenmaps) imposed on these gradients. In other words, while the first gradient is fully data driven, all subsequent gradients will explain less variance while being orthogonal (but not independent) to each other. The combination of these two factors  might explain the decrease in ICC Table  seen wheIn general, tract projections did not seem to indicate significant lateralisation effects. This finding agrees with that of Bajada and co-workers , who obsPrevious studies have also looked at gradients of connectivity in the temporal lobe, but our results differ in some important ways. Binney and colleagues  describeOur results are subject to certain limitations. First, there are shortcomings inherent to diffusion tractography, especially when trying to map precise end-to-end connectivity Below is the link to the electronic supplementary material.


The implications of the ongoing COVID-19 pandemic have stretched far beyond human health and wellbeing, causing serious setbacks for the achievement of the Sustainable Development Goals (SDGs). Although governments worldwide have implemented different fiscal stimulus measures to mitigate the implications of COVID-19, it is important to develop a precise understanding of their focus areas to ensure if the progress of SDGs is on track. For a specific case of Indonesia, this study establishes a thorough understanding of the COVID-19 implications on SDGs, and its fiscal stimulus package through a literature review and semi-formal interviews with the core stakeholders in Indonesia. The study results highlighted that COVID-19 has varyingly affected the progress of all SDGs in Indonesia. Amongst the four pillars of sustainable development in Indonesia, the SDGs on the social and economic development pillars are stated to be the most impacted. As for the fiscal stimulus, it is perceived that it can help maintain the SDGs’ attainment progress to a certain extent, although there are several concerns on its implementation. Deriving lessons from the conducted research, the study puts forward key suggestions for the effective implementation of SDGs in the post-COVID-19 era.  The first cases of coronavirus disease 2019 (COVID-19), a respiratory illness caused by the novel form of coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were reported in China towards the end of 2019. It was later declared a pandemic by the World Health Organization (WHO) on 11 March 2020, after the virus spread to more than 110 countries and there were 118,000 confirmed cases worldwide [Despite the fact that the COVID-19 pandemic is still unfolding, countries all over the world have distinctly executed a range of countermeasures to control and manage the spread of the virus, including the closure of educational, commercial, sports, and religious institutions . In thatInitially, 2020 was supposed to be the marking point of the Decade of Action to keep track on the 2030 Sustainable Development Agenda, and the 17 defined Sustainable Development Goals (SDGs) . Having Indonesia—like all other nations—is also struggling through the pandemic’s emergency consequences. Early in March 2020, the first confirmed case in Indonesia was recorded, and COVID-19 was then declared a national non-natural disaster by the Indonesian government on 13 April 2020 . By 5 MaReferring to the Sustainable Development Report in 2020 , some SDThe fiscal stimulus and/or state budget instruments are stated to be driving the economic improvements in handling the COVID-19 pandemic and the national economic recovery program . AlthougWhile extensive research studies have been conducted so far to study the implications of the COVID-19 pandemic, there have been very few empirical studies seeking to understand their consequences on the progress of SDGs and the relative role of fiscal stimulus measures. Furthermore, since COVID-19 economic recovery packages must be designed to not only mitigate the pandemic’s short-term repercussions, but also to “build back better” towards a more sustainable and resilient future ,19, it bWith an aim to bridge this gap, a qualitative research design  has been adopted in this study to methodically understand the impacts of COVID-19 on the progress of SDGs in Indonesia, as well as the role of fiscal measures for keeping SDGs on track. The three key objectives of this study are: (1) to establish a wider understanding about the impacts of the COVID-19 pandemic on the progress of SDGs in Indonesia; (2) to study the stakeholders’ perception about the impact of the COVID-19 pandemic and the impact of stimulus measures towards the SDGs’ progress; and (3) to suggest feasible directions for enhancing and strengthening the implementation of fiscal measures and SDGs in Indonesia. In doing so, the study addresses two key research questions: (1) How has the COVID-19 pandemic impacted the progress of SDGs? (2) What are the perceived direct and indirect impacts of fiscal policies in Indonesia towards the progress of SDGs’ implementation? For the study context, it is important to highlight that the “direct impacts” refer to the immediate impacts of the fiscal measures on the SDGs, while the “indirect impacts” refer to the relatively predictable effects of the stimulus on the SDGs that could arise later in time or in the cause-and-effect relationship chain.Overall, this paper is divided into seven sections, the first of which is the Introduction . SectionThis section is mainly intended to establish a foundational view of the study’s topic, based on the review of state-of-the-art scientific literature. It is divided into three sub-sections. The first sub-section provides an overview of the SDGs and their global progress. The second sub-section highlights the implications of the ongoing COVID-19 pandemic towards SDGs. The final sub-section discusses the fiscal stimulus programs implemented by governments around the world to overcome the repercussions of the COVID-19 pandemic.On 25 September 2015, the 2030 Development Agenda (titled “Transforming our world: the 2030 Agenda for Sustainable Development”) was adopted by 193 countries of the United Nations (UN) General Assembly, which serves as a framework for achieving peace and prosperity around the world. As the continuation of the Millennium Development Goals (MDGs), the 2030 Development Agenda outlines 17 SDGs and 169 associated targets, which frame the integrated plan of action in a global partnership for people, the planet, and prosperity ,20,21. THaving been implemented for five years, the global progress on achieving the SDGs appears to be lagging behind in fulfilling the targets ,23,24. BThe progress of SDGs worldwide was already behind the target, and the COVID-19 pandemic brings tremendous challenges in realizing all the goals ,27. The Based on the Sustainable Development Report in 2020 , the shoGovernments all over the world implemented fiscal stimulus initiatives to counteract multi-sector effects of the COVID-19 pandemic . Herein,It is evident that the components of fiscal stimulus (to mitigate the impacts of COVID-19) differed from country to country. The ASEAN member states’ fiscal stimulus packages were designed to assist people, households, and businesses in all sizes to survive the impacts of the pandemic and to improve the healthcare system . Other nThe International Monetary Fund (IMF)  stressedIndonesia is one of the world’s largest archipelagic countries with over 17,000 islands. It is located south of Malaysia and the Philippines, between the Asian and Australian continents, and between the Indian and Pacific oceans . The cenLike all nations worldwide, Indonesia has faced the widespread repercussions of COVID-19 not only in its public health but also in its economy. The COVID-19 pandemic has slowed the economic growth in Indonesia, as observed from the country’s Gross Domestic Product (GDP) growth contracting in the first and second quarters of 2020 ,46. In 2For the effective implementation of SDGs, Indonesia has taken considerable efforts to integrate the goals into its development strategies and policies, and to assure that these goals are implemented at the ground level. As a form of commitment to SDG implementation, the President of Indonesia signed the Presidential Regulation (Perpres) Number 59 Year 2017 (on July 4) on Achieving Sustainable Development Goals (SDGs) . The decAs the coordinator of SDG implementation in Indonesia, the Ministry of National Development Planning (BAPPENAS) has categorized the 17 SDGs into four development pillars , which aWhile the COVID-19 pandemic has caused significant disruptions to the progress of the 2030 Agenda, the deceleration is apparently concentrated in the social and economic development pillars of SDGs in Indonesia. The virus has taken a huge toll on public health services around the nation, and education has shifted from traditional classrooms to online platforms. As the poverty rate is expected to increase, there are also growing concerns about food insecurity. The pandemic is also likely to curtail the progress in achieving gender equality ,49. Under the cornerstone of economic development, the success of the SDGs has had significant ramifications. As explained earlier, Indonesia’s economic growth has plummeted after the onset of COVID-19, and so has inequality. The advancement of clean and affordable energy has slipped down the priority list . ReporteAltogether, the COVID-19 pandemic has had a wide-ranging effect on the people as well as the economy in Indonesia. To overcome the economic meltdown, the Indonesian government has taken several efforts and established fiscal policy measures under the National Economic Recovery (PEN) program. The economic recovery package of Indonesia (PEN) comprised of around 4.2% of the nation’s GDP, and its implementation is being performed in four phases , as illuIn order to fund the COVID-19 mitigation and the PEN, the Indonesian President has issued the Indonesian President Instruction (Inpres) No. 4 year 2020 on government programs’ refocusing, budget reallocating, and procurement of goods and services to accelerate COVID-19 mitigation . The InpTo study the impact of COVID-19 on SDGs in Indonesia, two steps of research were conducted in this study. Firstly, a methodical literature review was conducted to establish a basic understanding of the research subject. Relevant documents were identified from the selected research databases of Scopus, Science Direct, ResearchGate, and Springer by executing search queries for keywords such as “COVID-19”, “SDGs”, “COVID-19 fiscal stimulus” and “Indonesia”. While the research documents related to COVID-19 and SDGs are still very few, a total of 83 research documents were identified (as of December 2020) through keyword search queries in all defined research databases. These documents were then sorted by their title and abstract to meet the relevance for this study, after which 16 of these documents were taken into consideration. In addition, an online search was also conducted to find relevant policies, official reports, and statistics from the government, international organizations,  NGOs, media releases, and academic research. In the second step, semi-formal interviews (through online meeting platforms) were conducted with key stakeholders in Indonesia to substantiate the literature findings. The online mode was explicitly chosen with respect to the core need to maintain social distancing and to avoid any unintended consequences of this study. The key stakeholders were identified in consideration of their roles and responsibilities in SDG implementation and fiscal stimulus policy in Indonesia, which are mostly conducted by Indonesian government agencies. Thus, representatives from three different Indonesian government agencies were approached to be interviewed in this research: (1) SDGs Secretariat of BAPPENAS: the coordinating agency for SDGs Implementation in Indonesia; (2) Ministry of Finance: the agency that is responsible for fiscal policy; (3) Statistics Indonesia: the SDGs database coordinating agency. To further widen the range of stakeholders interviewed in this study, academic experts, NGOs, community groups, and the private sector were also identified based on their work on the field of SDG implementation in Indonesia. During November–December 2020, a total of 11 interviews were conducted with a range of stakeholders from Indonesia as follows: one expert form the SDGs Secretariat of BAPPENAS, two experts from the Ministry of Finance Indonesia, two experts from Statistics Indonesia, one expert from SDGs Research Centre, two academic experts, one expert from an NGO, one expert from a community group, and one expert from the private sector.Three broad research topics were prepared for guiding these interviews: (1) The impact of COVID-19 on SDG attainment in Indonesia; (2) The role of fiscal stimulus to keep SDG progress on track; (3) The impact of fiscal stimulus on SDG progress in Indonesia. Based on these topics, the authors prepared specific research questions to initiate the discussions during the semi-formal interviews as follows: (1) Describe the impact of the COVID-19 pandemic on SDG progress in Indonesia; (2) Which SDGs are the most affected by the pandemic?; (3) Do you think the fiscal stimulus will help to keep SDG progress on track?; (4) Which SDGs will be directly impacted by the fiscal stimulus?; (5) Which SDGs will be indirectly impacted by the fiscal stimulus? The average duration for each of the interviews was around 60 min and all the interviews were documented. In order to effectively keep track of all interview responses, the authors also prepared matrices to list down the direct and indirect impacts on all 17 SDGs and the fiscal stimulus. These matrices were later brought to Microsoft Excel for analysis and data display. Based on the responses from the interviews, a deeper understanding regarding the impact of the COVID-19 pandemic on SDG implementation in Indonesia and the impact of fiscal stimulus towards SDG progress was established, and the literature findings were accordingly substantiated.Insufficient SDG knowledge disseminationBased on the insights gained through the literature research and semi-formal interviews, it has been inferred that the progress of SDGs’ realization in Indonesia was lagging even before the pandemic. The interviewees even raised specific concerns on the progress of SDG attainment in Indonesia before COVID-19, which are explained as follows:As a new development concept adopted from an international agreement, SDG knowledge was first disseminated at the national government level, from where the knowledge was transferred to provincial and city/regional governments. However, some stakeholders argued that the knowledge transfer had not been well implemented. A familiarity gap persists between the national government and other tiers of governments below it. The knowledge on SDGs and their implementation is primarily possessed by those in the national government, whereas the other government levels have inadequate knowledge of SDGs, let alone the ability to translate them into their development programs.2.SDGs are not yet mainstreamed into the development programs in provincial and local government levelsAlthough it has been instructed by the national government of Indonesia to main-stream SDGs into development programs in all government levels, some stakeholders claimed that many provincial and local governments have not yet been able to do so. Through the interviews, it was discovered that certain government programs are not yet aligned with the SDGs, which hinders their progress. In addition, several programs are aligned with SDG attainment but are not yet synchronized on the programs’ report. Herein, the interviewees highlighted that the SDG roadmap set by BAPPENAS is yet to be translated into development programs by all government levels and institutions, particularly provincial and city/regional governments.The interviewees perceived that COVID-19 would delay Indonesia’s progress towards achieving the SDGs. Further, some SDGs are perceived to be more impacted than others, especially those under the social and economic development pillars. Derived through the stakeholder perception, In the wake of the COVID-19 pandemic, achieving SDG 1 (No Poverty) would be extremely challenging in Indonesia. The poverty rate is expected to rise as a result of large-scale social restriction policies which have been implemented by provincial, city, and regional governments all around Indonesia. Many people have lost their jobs and enterprises as a result of enforced social restrictions. The vulnerable groups of society are expected to fall into poverty, and those who are poor are expected to fall into deeper poverty. As per BAPPENAS , the povThe disruption of the food system due to COVID-19 restrictions has raised concerns for SDG 2 (Zero Hunger). BAPPENAS  underlinThe pandemic also has a major effect on SDG 3  according to the stakeholders interviewed. While the disruption in public health is apparent, the public healthcare services are presently more focused on controlling the pandemic, which is likely to disrupt the whole public service. The reallocation of the education budget for COVID-19 mitigation measures has impacted the national education program , as wellThe achievement of SDG 5  is also affected by COVID-19. Several reports have highlighted the rise in physical assault cases within the first six weeks of the large-scale social restriction in Indonesia. From March to April 2020, the number of domestic violence cases was reported to be doubled . The insSDG 7 (Affordable and Clean Energy) was perceived as hard to accomplish even before COVID-19. Since the pandemic, the progress has become much more hampered as the energy demands have plunged due to the closure of offices and shopping centers, and a drop in flight frequency. This has brought the fossil fuel price down significantly . As a reThe economic impact of the COVID-19 pandemic in Indonesia is highly prominent, making SDG 8 (Decent Work and Economic Growth) harder to attain. Indonesian economic growth contracted to minus 5.32% in the second quarter of 2020 . The couBAPPENAS  reportedWhile COVID-19 has widened the inequalities, the attainment of SDG 10  has become far more challenging. Inequality has risen primarily due to the disproportionate impacts of COVID-19 on different social groups, such as on the informal sector. While the urban society has been shown to be more vulnerable to poverty, they are also at a higher risk of virus exposure due to high population density, slum living, and limited access to health services. Furthermore, the rural economy is less affected by the COVID-19 pandemic, at least in the early stages of the pandemic. Recently, the economy in rural areas has returned to normal, and in some cases has even grown because of the new opening for tourism with COVID-19 prevention measures. While no scientific study has discussed the implication of COVID-19 on SDG 17 , it is perceived to be impacted based on the semi-formal interviews. Many multi-stakeholder initiatives have been put on hold due to COVID-19, which will hamper the progress toward this goal. Multinational collaborations on various development projects have been postponed due to the fact that all countries are presently focusing on themselves.SDG 6 (Clean Water and Sanitation) achievement was on track before COVID-19 ; howeverSDG 11 (Sustainable Cities and Communities) is also impacted by COVID-19 in varying ways. According to BAPPENAS , the useDuring the pandemic, the rise in medical waste and plastic packaging has been a major concern for the environment as people use disposable packaging to prevent the spread of the virus . WithoutIn relation to SDG 13 (Climate Action), the pandemic reduced the commitment to Climate Action . AccordiThe data availability for SDG 14 (Life Below Water) was limited even before the pandemic, and the pandemic has further exacerbated the situation, making it difficult to understand the impacts of COVID-19 on the attainment of this goal . RegardlFor Goal 15 of Life on Land, the extent of deforestation is found to be increasing during the pandemic . While tGoal 16  is affected by the pandemic as the restriction on public services on court processes disrupts the stability in the legal and judicial fields . FurtherHerein, the impacts of COVID-19 towards SDGs attainment in Indonesia have been presented, combining both literature review and stakeholder interviews as depicted in Based on the understanding obtained through the semi-formal interviews, fiscal measures under the national economic recovery of Indonesia were initiated as emergency measures to alleviate the impact of the COVID-19 pandemic. They were launched with the primarily goal of addressing the health fallout of the pandemic, while simultaneously mitigating social and economic impacts. Along the way, the Indonesian government has pledged to align the economic recovery measures with the SDG framework. The purpose was to expand the impact of fiscal stimulus measures to not only respond quickly to the repercussions of the COVID-19 pandemic but also to keep the 2030 sustainable development agenda on track . BroadlyThe possibility of misconduct in stimulus implementationAlthough it is perceived that fiscal measures may help to keep the attainment of Indonesian SDGs on track, several concerns were highlighted by the stakeholders interviewed in regard to the implementation of fiscal measures:The key interviewees argued that although the amount allocated for fiscal stimulus is not as large as expected, it will at least benefit vulnerable people, if well implemented without any misconduct such as corruption and fraud. However, the possibility of misconduct is rather high owing to the lack of accountability in the stimulus delivery process. Clean, transparent, and accountable tender mechanisms may be overlooked for the sake of ensuring a speedy stimulus implementation. This concern is also highlighted by The United Nations Office on Drugs and Crime (UNODC) , which s2.Unsynchronized database at all government institutionsThe semi-formal interviews revealed that the social protection fiscal measures may be beneficial only if they are delivered to the accurate beneficiaries. However, some interviewees highlighted that the database used for fiscal measure distribution is poorly maintained and updated. Each ministry has its own database, and so have the provincial and city/regional governments. These databases have not yet been integrated across government institutions and at all levels. As the accuracy of the beneficiary data is still inadequate, the distribution of social protection packages is often disrupted. For instance, during the ongoing COVID-19 crisis, some households received the social protection packages despite not meeting the eligibility criteria, while there have been cases where qualifying recipients were overlooked. 3.Lack of resources and capacity in fiscal measures’ implementationA large number of fiscal measures are distributed by the local governments in Indonesia. However, some interviewees argued that the local governments have not yet obtained sufficient capacity in managing a huge amount of emergency budget which must be distributed in a short period of time. This could lead to further implementation problems such as delayed delivery and a chaotic distribution process. The new work-from-home setting caused by COVID-19 has also hampered the fiscal measures’ implementation. The stakeholders perceived that the Indonesian government is not yet able to adopt a work-from-home setting both in terms of the manpower capacity and infrastructure.Noticeably, the goals within the “economic” and “social development” categories are perceived to be the most directly impacted goals by the fiscal measures. However, goals under the environmental and law and governance development categories do not experience as much direct impact. Most stakeholders regarded that SDG 8 (Decent Work and Economic Growth) will be mostly impacted directly by the stimulus, followed by SDG 3 , and SDG 9 , respectively. The reason is that the stimulus is largely allocated towards economic recovery, whether through SMEs financing, business incentives, sectoral and regional governments, and corporate financing.The stimulus on the sectoral and regional government category is recognized as being able to give a direct impact to all 17 SDGs, although the amount of the budget allocation is not as high as other economy related stimulus categories. It is because the stimulus aimed for the sectoral and regional government will be spent by ministries and local governments all over Indonesia for a variety of purposes. This stimulus enables the Indonesian government in all levels to execute a broad range of programs that may cover all SDGs, unlike other stimulus categories which are specifically addressing only those within their field.Evident through the research findings, the attainment of SDGs in Indonesia has been impacted by the pandemic  and SDG 3   to address the identified concerns on fiscal measures and SDG implementation in Indonesia (presented in As discussed in As highlighted in The allocation of the economy packages for social protection has brought forth concerns from the key stakeholder perception (explained in As the schemes are organized by different ministries, the beneficiaries’ database is not integrated across ministries and each ministry has its own database . This haAs the Indonesian government is committed to mainstream SDGs in its economic recovery program, it is imperative to ensure the mainstreaming of SDGs in all government institutions and across all government levels. The stakeholders interviewed in this study also highlighted that one of the key challenges of attaining SDGs in Indonesia is the gap of knowledge of the SDGs themselves existing within the government institutions, as discussed in While the wide-ranging implications of COVID-19 on SDGs worldwide are yet to be fully understood, this study presented a specific overview of COVID-19 implications on SDGs for the context of Indonesia. Building over the foundation of the existing literature, semi-formal interviews were conducted with the key stakeholders in Indonesia that are directly involved in the implementation of SDGs. In addition to identifying the most affected SDGs through specifically defined interview questions, this study has also derived the perceived direct and indirect impacts of the fiscal stimulus measures on different SDGs. In the backdrop of the limited research conducted so far on the implications of COVID-19, it is hoped that this research will provide useful insights and directions to the decision makers for enhancing the implementation of SDGs in the post COVID-19 era.The findings highlighted that COVID-19 impacts all 17 SDGs, although disproportionately. Based on the four pillars of sustainable development defined in Indonesia, the SDGs in the social and economic development pillars are seen to be most impacted by COVID-19, while the pillars of environmental and law and governance development are not as highly impacted. The semi-formal interviews also helped to identify the role of fiscal measures in maintaining SDG progress, wherein the fiscal stimulus is perceived to be able to help in maintaining the SDG progress to some extent, except in the law and governance development pillar. The direct and indirect impacts of the stimulus towards each SDG were also identified. Further, the semi-formal interviews, as the key strength of this study, have also brought forward the stakeholders’ concerns on the progress of SDG implementation and fiscal stimulus implementation.This study contributes to the existing knowledge of COVID-19, SDGs, and fiscal stimulus by providing a precise overview of COVID-19’s impacts on SDG attainment in the specific case of Indonesia. Building over the country’s governance profile and fiscal stimulus measures against the COVID-19 pandemic, the study methodically presented the research findings based on a literature review and stakeholder interviews. Through this study, specific policy recommendations were derived, in which they were established based on the stakeholders’ concerns on the COVID-19 fiscal measure implementation. Deriving specific lessons from the case of Indonesia, the study also highlighted the core need for aligning the COVID-19 fiscal stimulus with SDG attainment in order to keep the local level progress of SDGs on track, which is now is highlighted as necessary to be done. Towards the end, it is important to acknowledge the following limitations of this research. The study has been conducted in reference to the existing literature on COVID-19’s impacts on Indonesia and semi-formal interviews with the key stakeholders. However, the implications of the COVID-19 pandemic for SDGs may further transform, until the pandemic is fully controlled. As such, the future scope of this study necessitates a further detailed literature analysis and stakeholder perception study. It is also important to note that this research was mainly conducted through the online mode, but there is a further need for primary surveys in the field, and analysis of the secondary data. The future scope of this study also includes the mapping of the before and after COVID-19 progress of SDGs to determine the development gaps based on both primary and secondary data, and accordingly, to work for the timely attainment of SDGs. Another possible area of future research would be to investigate the impact of the COVID-19 pandemic on the environmental related goals with more data availability.


In this paper, Rayleigh backscattering sensors (RBS) are used to realize shape sensing of beam-like structures. Compared to conventional shape sensing systems based on fibre Bragg grating (FBG) sensors, RBS are capable of continuous lateral sensing. Compared to other types of distributed fibre optic sensors (FOS), RBS have a higher spatial resolution. First, the RBS’s strain sensing accuracy is validated by an experiment comparing it with strain gauge response. After that, two shape sensing algorithms (the coordinate transformation method (CTM) and the strain-deflection equation method (SDEM)) based on the distributed FOS’ input strain data are derived. The algorithms are then optimized according to the distributed FOS’ features, to make it applicable to complex and/or combine loading situations while maintaining high reliability in case of sensing part malfunction. Numerical simulations are carried out to validate the algorithms’ accuracy and compare their accuracy. The simulation shows that compared to the FBG-based system, the RBS system has a better performance in configuring the shape when the structure is under complex loading. Finally, a validation experiment is conducted in which the RBS-based shape sensing system is used to configure the shape of a composite cantilever-beam-like specimen under concentrated loading. The result is then compared with the optical camera-measured shape. The experimental results show that both shape sensing algorithms predict the shape with high accuracy comparable with the optical camera result.  The usage of composites in air-foil and wind turbine blades has dramatically reduced the weight of these structures and, meanwhile, increased their deflections largely. The extreme deflection of the A350XWB wing, whose span is 64.75 m, is about 5.2 m, while Boeing 787’s wing deflection can go up to 7.9 m for a span of 60.1 m. An in-flight break-up accident of NASA’s Helios wing in 2003 and the awareness that part of the failure was due to excessive wing deformations has led to an urgency to develop a robust real-time shape sensing methodology  s. The size of the specimen is 250 × 35 × 3 mmThe strain measurement results from both RBS and strain gauges are shown in As can be seen, the strain measured by both RBS and strain gauges match perfectly. The difference between strain measured by strain gauge and RBS are within 5%, which validates that RBS can be used in strain measurement. In the next section, the strain-shape algorithm will be introduced so that the measured strain can then be used to reconfigure the deformed shape of the structure.The strain-deflection equation method (SDEM), alternatively called the “fibre optical sensing system method (FOSS)”, is the most popular method used in air-foil shape sensing. This algorithm relies on the classical beam theory to derive the displacement transfer functions in segments of the structure; details can be found in ,12,26. FThe derivation starts from Equation (1) which relates the vertical displacement W and the strain in optic fibre along the axial direction.Therefore, the deflection in each element can then be calculated as:T is the thickness of the structure. The calculated rotation in each sensing point is then divided equally and added to the rotation of the two adjacent elements. In a real situation, external loading is more likely to be a non-uniform distributed loading with concentrated force added on any place along the structure. In the next section, the SDEM and CTM in form of RBS-based shape sensing system is derived. Compared to the previous formula, no extra strain distribution assumption between two sensors are required.Assuming a linear distribution for the strain in each sensing part translates that the structure is under a load distribution with concentrated force is applied at FBG’s location. This means that the external loading is concentrated forces located on the FBG’s measurement points. In the numerical simulation used in the patent  and in tIn this section, the algorithm for continuous strain sensing is introduced. Two methods (SDEM and CTM) for continuous lateral sensing is derived first and their performance for shape sensing is then compared and validated under various load distributions . In discrete sensing, FBG-measured data are regarded as the nodal strain in each element, while distributed FOS-measured data are the average strain within each part shown in . That mex-axis is the tangent line at the mth element’s left end. For example, The relation between the local strain and local curvature is given by Equation (4).The recursion formulas to calculate the shape in the CTM are the following:After the derivation of the CTM algorithm, a numerical simulation is carried out in which the CTM with different spatial resolutions is considered and compared with theoretical results to see how much influence the spatial resolution will have on the accuracy of the shape sensing. A cantilever beam of 250 mm length is considered, where the strain variation starting from its fixed end is 2000–0 terature . Figure terature .The results of shape sensing with the CTM with distributed FOS on one side of the beam was derived and validated in this section. In the next section, the SDEM for distributed FOS is derived.The element map of the SDEM for distributed FOS is shown in By inputting the measured strain into Equations (7)–(12), the shape of the structure can then be configured. It starts from the first node’s rotation and deflection, which is Similar to the CTM, the SDEM with different sensing spatial resolutions is applied in a simulated example presented previously to investigate the influence of the spatial resolution in the shape sensing accuracy. The simulation results in It is worth mentioning that both of the derived algorithms have calculated the intermediate variable angle of each element The algorithm for shape sensing with distributed FOS was derived in the last section. However, using the uniform average strain as the strain distribution within each element part will cause an error, since in real situations, external loading is more likely to be distributed and non-uniform. Furthermore, for FBG and BBS, a malfunction is more likely to be an error in the measured spectrum, since the distortion of the spectrum will lead to the difficulties in the centre wavelength configuration. Therefore, Nishio  proposedIn addition, for shape sensing of large structures such as aircraft wings or turbine blades, the self-weight of the structure will cause a noticeable strain in both axial and bending directions. In this scenario, the axial displacement should be taken into consideration. In the next section, modified algorithms are proposed which enable the RBS-based shape sensing system to have a higher accuracy under complex or combine loading (tension and bending) and a higher robustness in case of sensor failure.To increase the accuracy of the shape sensing as well as increasing the robustness of the sensing system, the interpolation method with spline interpolation of order three is proposed. The third order interpolation is more suitable for non-uniform external loading distribution. Furthermore, if one or more sensors within the optic fiber fails, the error caused by the malfunction is minimized as well.Similar to the previous sections, a numerical simulation is carried out to validate the shape sensing system’s performance under a more realistic air-foil loading, where a combination of distributed loads and point loads are applied, see When the structure is under combined loading, which involves both axial (tension or compression) and transversal (bending) loading, taking the axial displacement into consideration can improve the accuracy of the shape sensing. In that case, the rotation–deflection relationship By modifying the algorithm, the axial strain is taken into consideration. The numerical simulation result for considering an axial strain with a magnitude of 2000 The results show that when considering the axial displacement, the error in the deflection direction can be reduced by maximum 4%. This is not a noticeable improvement.In this section, an improved algorithm when under more realistic air-foil loading or combine loading is proposed. The simulated results show that the optimized distributed (RBS-based) shape sensing system has a higher performance under realistic loading air-foil loading compared to point sensing (FBG-based) system. In next section, an experiment is carried out and reported to validate and verify the RBS-based shape sensing system.To validate the application of RBS in shape sensing, an experiment is carried out. The RBS was bonded onto the surface of a composite cantilever specimen to measure the strain distribution along the length of the beam. The shape of the specimen was then configured from the RBS-measured strain. The configured shape was compared to the deformed shape measured by an optical camera for validation and verification.S. The size of the specimen is The CFRP specimen was manufactured from thermoset prepreg (hexcel 914C-TS-5) as the host structure with the composite layup of: [0/45/−45/90/90/−45/45/0]The setup of the experiment is shown in The shape configuration resulting from the RBS strain distributions with both CTM and SDEM algorithms are shown in The comparison between distributed the FOS-based shape sensing and optical camera results shows that both the strain-shape algorithms predicted the deformed shapes with high accuracy, and they match well with the optical camera results, which validate the RBS-based shape sensing system. It is worth noting that the load profile in this example is a constant point load. However, in the previous sections it was demonstrated that the complexity of the loading  will change the accuracy of the prediction on the basis of the resolution of the RBS sensors. The objective of this research was also to investigate this effect experimentally. However, the current experimental set up was not able to apply combined loading simultaneously. This will be considered in the next step of the research to improve the reliability and robustness of the algorithm.By comparing the spatial resolution of different distributed FOS, the most suitable distributed FOS, RBS, was selected which has the advantage of both continuous lateral shape sensing and high spatial resolution. RBS is then applied in shape sensing to improve current FOS-based shape sensing system’s performance. The RBS’s strain sensing accuracy was validated by an experiment comparing it with strain gauges. After that, two shape sensing algorithms (CTM and SDEM) based on the distributed FOS’ input strain data were derived and tested. A comparison between the two algorithms were made, which showed that the error between the two algorithms for small deformation is negligible. The algorithm was then optimized according to the distributed FOS’s feature, to make it applicable to complex loading situations and to improve its accuracy in case of sensor failures. After that, numerical simulations were carried out to validate the algorithms’ accuracy. The simulations showed that compared to the FBG-based system, the RBS-based system has a better performance in configuring the shape when the structure is under complex loading or combine loading.Finally, an experiment was carried out, in which the RBS sensors were used to configure the shape of a composite cantilever-beam-like specimen under concentrated loading. The configured shape was then compared with the deformed shape measured by an optical camera. The experimental results show that both shape sensing algorithms predict the shape comparably well with the optical camera measured result.


In unshrouded turbine rotors, the tip leakage vortices develop and interact with the passage vortices. Such complex leakage flow causes the major loss in the turbine stage. Due to the complex turbulence characteristics of the tip leakage flow, the widely used Reynolds Averaged Navier–Stokes (RANS) approach may fail to accurately predict the multi-scale turbulent flow and the related loss. In order to effectively improve the turbine efficiency, more insights into the loss mechanism are required. In this work, a Delayed Detached Eddy Simulation (DDES) study is conducted to simulate the flow inside a high pressure turbine blade, with emphasis on the tip region. DDES results are in good agreement with the experiment, and the comparison with RANS results verifies the advantages of DDES in resolving detailed flow structures of leakage flow, and also in capturing the complex turbulence characteristics. The snapshot Proper Orthogonal Decomposition (POD) method is used to extract the dominant flow features. The flow structures and the distribution of turbulent kinetic energy reveal the development of leakage flow and its interaction with the secondary flow. Meanwhile, it is found that the separation bubble (SB) is formed in tip clearance. The strong interactions between tip leakage vortex (TLV) and the up passage vortex (UPV) are the main source of unsteady effects which significantly enhance the turbulence intensity. Based on the DDES results, loss analysis of tip leakage flow is conducted based on entropy generation rates. It is found that the viscous dissipation loss is much stronger than heat transfer loss. The largest local loss occurs in the tip clearance, and the interaction between the leakage vortex and up passage vortex promotes the loss generation. The tip leakage flow vortex weakens the strength of up passage vortex, and loss of up passage flow is reduced. Comparing steady and unsteady effects to flow field, we found that unsteady effects of tip leakage flow have a large influence on flow loss distribution which cannot be ignored. To sum up, the current DDES study about the tip leakage flow provides helpful information about the loss generation mechanism and may guide the design of low-loss blade tip. In a shroudless turbine blade, the pressure difference from the pressure side to the suction side causes a part of the flow from the pressure side passing through the tip clearance to the suction side, thereby the tip leakage flow forms. Because of the large lateral velocity gradient of the leakage flow, it mixes with the secondary flow and causes large loss. The tip leakage flow could account for about 30% of the overall loss in turbine stage . MeanwhiRegarding the study of the tip leakage aerothermal performance, there are plenty of mechanism studies of tip leakage flow. Moore and Tilton  developeSince aerodynamics and heat transfer are mutually influenced by each other, there is some research about optimization of blade tips by combining aerodynamics and heat transfer. Caloni and Shahpar  applied The mechanism study of tip leakage flow is the key point and foundation to understand the flow physics and enhance turbine performance as well as cooling effects. For the unsteadiness of tip leakage flow, there are still many aspects which we need to study further. In order to obtain more insights into turbulence characteristics and the loss mechanism in tip leakage flow, the flow field must be analyzed in more detail. The RANS method is less expensive and serves as the current main simulation method. Du et al.  simulateIn order to further analyze the flow physics, the flow field needs to be decomposed. The POD method is one of the model reduction methods to extract dominant flow structures. The POD method has been widely used in science and engineering, including image processing, data compression, signal analysis, modeling and control of chemical reaction system, turbulence models, and coherent structures. Since Lumley  introducAt present, the research on the tip leakage flow mostly adopts RANS or URANS approach, and the in-depth study of tip leakage flow using a high fidelity numerical method is seldom reported. In addition, existing analysis about tip leakage flow focus on the distributions of pressure, temperature and other parameters, while novel analysis methods like POD should provide new perspective about the flow physics. This paper uses the DDES, a hybrid RANS/LES approach to investigate the mechanism of tip leakage flow inside a high pressure turbine blade with a tip gap of 1% height, which has been proved to be superior in capturing a high accuracy and detailed leakage flow. With the POD method, dominant flow modes governing the unsteady evolution in the tip region are successfully obtained. The use of DDES approach as well as POD analysis brings an interesting view of what can be done to better understand the leakage flow with modern methods for computation and post processing. The time-averaged entropy generation rate reveals the overall loss of tip leakage flow, and the instantaneous entropy generation rate shows the local loss. In addition, the unsteady loss obtained by decomposing the loss reveals that the unsteady effects caused by tip leakage flow cannot be neglected.This paper is organized as follows: firstly, the numerical method and modeling are introduced as follows; secondly, an overview of the computational domain and mesh resolution are given; in the following parts, the detailed numerical results are analyzed; the last section concludes this work.The three-dimensional Navier–Stokes equation is expressed as follows:H is the total enthalpy, u, v and w represent the velocity components in the Cartesian coordinate system, respectively, and E represents the total internal energy  method, Spalart et al.  updated  is 0.65 , and Δ iey area”  between An in-house CFD code, which is based on the Message Passing Interface (MPI)-parallel multi-block structured finite volume method  is used The first term on the left side of the equation represents the pseudo time term. In the time marching solution, a three-stage Runge–Kutta/implicit scheme with a multigrid method is used. The second term represents the physical time term, which controls the unsteady evolution. In unsteady calculation, dual-time-step of an optimized, second-order, backward difference temporal scheme (BDF2opt)  method iDetailed validation about the DDES method in this code has been conducted by Lin et al.  and the In this paper, the rotor blade in a single stage axial turbine is studied. The relevant parameters of the blade are shown in Commercial software Numeca AutoGrid5 8.9-1 is used to generate multi-block structured mesh. H-Grid topology is applied in general. HO-Grid topology is applied for tip gap. The number of overall mesh points is about 9.11 million, with 53 layers of mesh in the tip clearance and 113 layers spanwise. The minimum orthogonality among all mesh block is 62.1 degrees. The maximum dictions . In ordedictions  is adoptCIn the DDES method, sub-grid scale modeling is used away from the wall. The mesh convergence study of DDES is difficult compared to RANS because the modeled part of the turbulence is closely related to the length scale of the mesh element. As a result, refining the off-wall mesh would change the contribution from the subgrid scale (SGS) modeling. Besides checking the distributions of c energy :(10)IQ=Ed C≈ 100 , which ec energy , so thatIn the unsteady simulation, the physical time step is taken as 1.2 Both methods capture the tip leakage vortex and up passage vortex. However, the vortex structure of the tip leakage flow and the secondary flow captured by URANS method is basically steady, and the interaction of the two vortex structures is not observed. The DDES method captures the development and interaction process of the tip leakage flow and the secondary flow more precisely, which is very helpful for the analysis of tip leakage flow mechanism in the flow field. It can be seen that the RANS is not suitable for the tip leakage flow, mainly because its failure to predict the interaction between the tip leakage flow and the main flow, which can be traced back to the isotropic and equilibrium assumptions in the currently used linear eddy-viscosity RANS model. In the DDES model, RANS can be considered as the wall-model for the LES simulation. The RANS region is restricted in the boundary layer and, in the current case, the boundary layer is free of separation. As a result, a RANS model like Spallart–Allmaras or SST behaves very well in the boundary layer and performs better than using LES in the whole domain. Thus, the RANS approach is not suitable for the tip leakage flow analysis in the off-wall region.Instantaneous Cp0 contours are shown in That is because there is no strong interaction between tip leakage vortex and up passage vortex initially. However, as the tip leakage vortex and the up passage vortex move downstream, the instantaneous results of two models show a clear discrepancy in tip leakage flow. It can be seen that the Cp0 distributions of URANS appear spatially smoothly and nearly no interaction between tip leakage vortex and passage vortex. On the contrary, the DDES method predicts small scale flow structures in tip leakage flow. Since the URANS method does not capture the interaction between tip leakage vortex and up passage vortex, the influence of such interaction on the flow field is neglected. From The vortex structures obtained with DDES method are represented by Q criterion and rendered by Mach number, as shown in Different slices are taken along the spanwise direction, and the isentropic Mach number distribution on the blade surface is obtained, as shown in The distributions of turbulent kinetic energy at several slices along the axial direction are given in POD method, also known as Karhunen–Loeve decomposition or Principle Component Analysis (PCA), is a mathematical tool for analyzing multidimensional data. The employment of POD method in flow field can help identify the main structures and flow characteristics and also obtain modes’ information with different orders . The keyIt is assumed that there are k linearly independent K in Equation Conducting POD analysis for the whole flow field would consume too much memory to store the required snapshots, so in the current work the key area of the flow field is extracted which rep the upper half blade span. 80 instantaneous flow fields are collected, which contain about 14 vortex shedding cycles. Eighty modes and its eigenvalues As can be seen, the energy at time averaged mode 0 is the highest, which indicates that most of the flows in the flow field are steady. From By using POD, the main unsteady flow structures are obtained, which are tip leakage vortex, up passage vortex, separation bubble, and the vortex generated by interaction of tip leakage and up passage vortex, respectively. Among them, the strongest unsteady characteristic is the tip leakage vortex, which indicates that the tip leakage vortex is the main source of the unsteady effect in flow field.Loss coefficients are generally used to evaluate the flow loss in turbine design, such as energy loss coefficient, enthalpy loss coefficient, and total pressure loss coefficient. Denton  studied From the thermodynamics, the irreversibility in the flow field is the source of loss. There are two main types of irreversibility in the flow field: caused by fluid viscosity and heat transfer. The equation is as follows:The left side of the equation is the total entropy generation rate (EGR). The first term on the right side is the irreversible loss caused by heat transfer, defined as Sthe. The second term is the irreversible loss caused by viscous dissipation, defined as Svis.The loss caused by viscous dissipation in this case is much larger than the loss caused by heat transfer, where the Svis is more than 99%, as shown in It can be seen that the entropy is highest in the tip clearance because of the flow friction dissipation caused by large shear stress of fluid inside the gap. The loss of the tip leakage vortex is greatest at the 50% Ca slice, and then the loss is gradually reduced. The loss in tip leakage vortex core is high due to the strong intensity and high initial energy. As the tip leakage vortex interact with the up passage vortex during the downstream transportation, the tip leakage vortex intensity becomes lower, and the energy in the vortex core is also smaller. Meanwhile, the loss caused by tip leakage flow is lower in the area where the tip leakage vortex and up passage vortex interact with each other.The mass-flow-averaged entropy generation rate is given at 110% Ca, which is along the upper half span as shown in There are three large losses in flow field, namely endwall loss, tip leakage flow loss and up passage flow loss. It can be seen that the up passage flow loss is lower than tip leakage flow loss both in both time-averaged and instantaneous results. The difference of up passage flow loss is quite small between time-averaged and instantaneous results. However, the difference of tip leakage flow is very large, which means that the unsteady loss of tip leakage flow is larger than passage flow loss. The time-averaged results of tip leakage flow are larger than instantaneous results; it indicates the turbulence characteristics in tip leakage flow is reduced by interacting with up passage flow and dissipating a large amount of energy in the transportation process. Therefore, if losses are only analyzed by time-averaged flow, the loss evaluation is over-predicted.In addition to classifying losses from physical sources, losses caused by the actual flow process can also be composed of two parts: losses due to steady effects (contribution from the time-averaged flow field, It can be seen from the steady and unsteady losses distribution in the flow field that the tip leakage flow has strong unsteady characteristics along flow and spanwise direction. It indicates that the tip leakage flow has large-scale turbulent and three-dimensional flow characteristics. Unsteady losses in tip leakage flow cannot be ignored.This work aims to investigate the flow phenomena and loss mechanism of tip leakage flow. The rich and detailed flow fields show the generation and development of the tip leakage flow. In addition, a schematic diagram comes up for explaining the flow field clearly. The flow field is decomposed by the POD method, which helps to resolve the main unsteady structures. The loss obtained by entropy generation rate is helpful to understand the loss caused by tip leakage flow and its interaction.POD is used to identify the dominant energy containing modes in flow, and it is found that the main flow structures near the shroud are tip leakage vortex and passage vortex in time-averaged flow. Apart from these structures, there is the separation bubble in the unsteady flow field. The strongest unsteady characteristic occurs on the 110% of axial chord slice, where the tip leakage vortex interacts with the up passage vortex. This is the main source of unsteady losses in flow field.In tip leakage flow, the irreversible loss caused by viscous dissipation is the dominant factor. Meanwhile, the main loss generated in flow field is due to a separation bubble in tip clearance, tip leakage vortex and the end wall vortex formed by up passage vortex. The friction dissipation caused by separation bubble in tip clearance causes the local maximum loss. The loss of the tip leakage vortex has the strongest influence at 50% Ca. As the up passage vortex interacts with the tip leakage vortex, the loss caused by tip leakage flow is reduced in the flow field. In addition, the unsteady effects of tip leakage flow have a large influence on flow losses distribution, which cannot be ignored.


Nanoparticles significantly alter the rheological properties of a polymer or monomeric resin with major effect on the further processing of the materials. In this matter, especially the influence of particle material and disperse properties on the viscosity is not yet understood fully, but can only be modelled to some extent empirically after extensive experimental effort. In this paper, a numerical study on an uncured monomeric epoxy resin, which is filled with boehmite nanoparticles, is presented to elucidate the working principles, which govern the rheological behavior of nanoparticulate suspensions and to simulate the suspension viscosity based on assessable material and system properties. To account for the effect of particle surface forces and hydrodynamic interactions on the rheological behavior, a resolved CFD is coupled with DEM. It can be shown that the particle interactions caused by surface forces induce velocity differences between the particles and their surrounding fluid, which result in increased drag forces and cause the additional energy dissipation during shearing. The paper points out the limits of the used simulation method and presents a correction technique with respect to the Péclet number, which broadens the range of applicability. Valuable information is gained for a future mechanistic modelling of nanoparticulate suspension viscosity by elucidating the interdependency between surface forces, shear rate and resulting drag forces on the particles. Numerous reports exist on the strong effect of nanoparticles on the rheological behavior of nanoparticulate suspensions . SurfaceThis work applies coupled CFD-DEM simulations to gain insight on the working principles causing the rheological behavior of nanoparticulate suspensions. Thus, the present work seeks to open up the prospect of being able to mechanistically model the rheological properties of nanoparticulate suspensions in a broader range than possible today.Independent from the specific method, numerical simulation of rheological properties focusses on the combined depiction of solid-fluid interactions (hydrodynamic interactions) and the interaction between particles. The first methods to be employed for this purpose were Stokesian Dynamics  and MoleWoodcock  applied r et al.  were ablr et al.  concernir et al.  influencMD simulations excel at elucidating effects on molecular level, but are quite costly. Disspative Particle Dynamics (DPD) represent fluid elements with discrete spheres, which mimic the behavior of a fluid. This reduces the computational effort significantly. With this method, Koelman et al.  were ablA further step in abstracting the fluid phase of a suspension is taken, when exclusively employing the Discrete Element Method (DEM) to investigate the rheology of suspensions. In recent years, this method proved to be very capable of illuminating rheological phenomena in suspensions with a high solids content of micron scale particles ,23. Nessto model . Lin et to model  employedto model  regardedIt is noticeable that DEM can only be used alone, if the suspensions have a high solids content of coarse particles with pronounced inertia, since friction-, contact-, and lubrication forces were found to dominate in this region. In contrast, nanoparticulate suspensions already exhibit strong viscosity increases at low solids contents. Consequently, friction- and contact forces cannot be expected to have a strong effect. Instead, inter-particle surface forces and particle-fluid interactions must be evaluated with great care.Computational Fluid Dynamics (CFD) is a very common method to solve fluid flow. A coupling of CFD with DEM can therefore yield the required information on the interaction of fluid and solid phase. Yoshida et al.  studied In a rotatory viscometer, the shear viscosity of a fluid or a suspension is measured by shearing a fluid sample in a defined gap and measuring the shear stress acting on the wall of the measurement cell by monitoring the torque acting on the rotating geometry. Most of the simulations mentioned above were performed with periodic boundary conditions, which prevents any estimation of a shear stress on a wall, since no walls exist. Independent from the specific simulational method, in most studies the viscosity of the suspension is estimated by quantifying the stress acting in the system. The procedure was developed by Batchelor , who modEquation .(1)τxy=V, divided by the volume as presented in Equation τxyThis procedure allows the individual assessment of each phase in the system Equation , wherebySmuts  chose a Equation ). AdditiFor this numerical study, coupled CFD-DEM simulations were conducted. The simulation domain consisted of a cubic representative volume element. In a preliminary convergence study, a side length of 1.44 The coupling of CFD and DEM was performed with the immersed boundary method , which ar and the distance between the surfaces is denoted by b.To account for the inter-particle interactions a Hertzian contact model was modified to include van der Waals type interactions before overlap of the particles. quations  and 5)))FvdW wheH.The effect of the interstitial fluid phase on the particle-particle interaction is regarded by applying Equation  for the The calculation of the Hamaker constant for the respective materials requires numerous material parameters, which were either measured or taken from literature. A detailed list of the input parameters and their origin is stated in Equation  38].ηsusThe contribution of the fluid viscosity is weighted with the fluid volume content atchelor , presentEquation .(9)ηparThe stress tensor Equation , accountThe respective stresslet were calculated as presented in Equations –13). Th. Th13). The method of Batchelor  can be aFor validation of the simulation results, an epoxy-boehmite nano-composite material was chosen. The suspension consisted of uncured Araldyte LY 556 epoxy resin (HUNTSMAN) and boehmite nanoparticles HP14 (SASOL), dispersed to a median particle size x et al. .The rheological characterization of the sample material was conducted in a rheometer  with a plate-plate geometry. Rheological measurements were performed in a temperature range of 30–80 pendency . Non-NewTo test the applicability of the simulation set up for the simulation of the suspension viscosities, the solids content in the suspension was varied and compared to experimental values. The results are given in in model ,43 prediin model  is exceeThe error bars of the simulational results with surface forces indicate increasing scattering with increasing solids content. In addition, slight deviations between experimental and simulational values arise at high solids contents, as the simulation underestimates the viscosity of such suspensions. This phenomenon will be addressed later.In The question arises, why the temperature appears to limit the applicability of the chosen simulation method and high shear rates contribute to the inaccuracies. The viscosity of the resin under investigation shows a strong temperature dependency and increases quickly as temperature decreases. Hydrodynamic forces are known to gain increasing relevance in the rheological behavior of suspensions, when the fluid viscosity, shear rate and solids content are high. The impact of hydrodynamic forces is characterized by the dimensionless Péclet number, which is calculated from the ratio of hydrodynamic and diffusive forces, as given in Equation .(14)Pe=Brownian movement allows a system of particles to relax to an energetically favorable condition, in which the surface interaction potentials between the particles are minimized. Hydrodynamic forces disturb this equilibrium state, as they enforce relative movement between the particles. Therefore, the Péclet number describes to what extent hydrodynamic forces change the state from equilibrium conditions.A clear trend of increasing deviation with increasing Péclet number is visible from c, m, and e are −21.22, 22.02 and 0.28, respectively.The identified relationship between the deviations and the Péclet number cannot only be used to explain the observed deviations, but also allows a correction of the simulated values as well. The approximated power function, which is displayed as a dashed line in From it, Equation  can be dThe second objective of this study is to identify relationships between the acting forces in the system to identify working principles causing the observable rheological behavior. Identifying mechanistic relationships is crucial for robust viscosity models and so far, such relationships have not been established for nanoparticulate suspensions .Based on the stresslet method, contact-, drag- and van der Waals forces contribute to the stress state in the suspension. The effect of contact forces is essentially zero. Because of the low solid contents, the particle insertion at 0.4 nm distance and the short-simulated time spans, particles do not have time to travel long enough to touch. Even if particles were given the time, the inertia of the small particles would not suffice to squeeze the highly viscous fluid out of the gap between the particles . ConsequDuring the simulation, a network of surface forces is established between the particles and drag forces act on each particle. The magnitude of these forces depends on the particle size, the proximity to the next particle and the relative velocity between particle and fluid. The values of the forces span multiple orders of magnitude, much like the stresslet distribution depicted in If two adjacent particles are considered, which are in different layers of a laminar flow travelling at different speeds, an interaction force occurs between them. In the case of van der Waals interactions this force is attractive. The faster flowing particle thus accelerates the neighboring particle, while the particle itself is decelerated. This creates a velocity difference between the particles and the respective surrounding laminars of the fluid. This velocity difference generates the observed drag forces and causes the increased energy dissipation during the flow and therefore, a higher viscosity of the suspension. It is plausible that the flow resistance force is as strong as the surface interaction force, which caused the flow resistance force.In This proves that even when surface forces dominate the rheological behavior of nanoparticulate suspensions, hydrodynamic forces contribute significantly as well. The strong effect on the viscosity at comparatively low solids contents must be attributed to the imposed drag forces.Based on these findings, a more mechanistic model for the viscosity of nanoparticulate suspensions is proposed, which will be developed further and investigated in future research work. A first approach is given by Equation , which cThe first term in Equation  accountsEquation ). The seCoupled CFD-DEM simulations using the immersed boundary method proved capable of depicting the rheological behavior of nanoparticulate suspensions at high temperatures. They correctly depicted the dependency of the suspension viscosity in a broad range of solids contents and shear rates. The viscosity was calculated based on the particle-particle and fluid-particle interactions. It was shown that surface forces indeed dominate the rheological behavior of nanoparticulate suspensions, as simulations without surface forces showed almost Einstein-like behavior.Limits arise due to disregarding particle-fluid interaction caused by particle rotation. The resulting deviations between simulation and experiment could be corrected based on a correction term, which depends on the Péclet number. In future, CFD-DEM coupling solvers, which provide rotatory coupling, such as the one used by Aycock  should bDetailed examination on the relationship between the drag force and influencing parameters like van der Waals interaction and the shear rate helped identifying working principles of the rheological behavior of nanoparticulate suspensions.A mechanism by which surface interaction forces influence the viscosity of the suspension was proposed, based on the finding that surface forces and resulting drag forces on each particle do always match in magnitude. Attractive forces between neighboring particles alter the velocity of both particles and cause the rise of drag forces, which increases the stress state within the suspension.This model representation of the fundamental working principle as well as the identified mathematical relationships between the active forces, allowed developing an equation, which can be used as a framework for a viscosity model for nanoparticulate suspensions. It holds the perspective of being refined to a mechanistic model for the viscosity of these materials in future.


Breastfeeding is beneficial to both the newborn and the mother. During the COVID-19 pandemic, concerns have been raised on whether the SARS-CoV-2 virus could be transmitted from COVID-19 positive mother to the newborn through breastmilk. The purpose of this review is to examine the available evidence on the risks of transmission of infection from COVID-19 mothers to their newborns through breastfeeding.Data is very limited in this regard, with only a few smaller case series, and case reports have been published so far. In most of the studies, breastmilk samples from COVID-19 mothers tested negative for the virus. In the case reports where the virus was detected in breastmilk and the infants were diagnosed with COVID-19, it remained unclear whether the disease was transmitted through breastmilk or direct contact or through delivery. Another hypothesis is that the viral antibodies could pass to the newborn passively through breastmilk of COVID-19 positive mothers and give immunity to the child, but data is minimal.Based on the currently available limited evidence and recognizing the benefits of breastfeeding, it may be concluded that if the health of the mother and her newborn allows, direct breastfeeding or extracted breastmilk should be encouraged by the healthcare providers after a careful discussion of the risks of vertical transmission to the mother and her family. Preventive measures should be taken by COVID-19 mothers to prevent droplet transmission of infection to the infants while breastfeeding. Coronavirus disease-2019 (COVID-19), declared a pandemic on March 11, 2020, by the World Health Organization (WHO), started as an outbreak of pneumonia in Wuhan, China, in December 2019. Later, it was discovered that the disease was caused by a single-stranded RNA virus, which was named SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2). The virus is known to be transmitted by droplets of an infected individual and leads to a variety of symptoms affecting respiratory and other systems in the body [Pregnant women and newborns can be considered a high-risk population during the COVID-19 pandemic because their vulnerability to acquire any infection is higher in general due to lowered immunity during this period. Even though initial data from smaller studies have suggested that the pregnancy does not increase the risk of getting COVID-19 infection and the severity of illness is not different from the general population, caution still must be exercised by both the pregnant women and their clinicians as there is a lot that we are not aware of this disease .Breastfeeding is beneficial to both the mother and the baby. Breastmilk can serve as the source of many required nutrients to the breastfed newborns during the first few months of life and provide the much-needed immunity as well. Breastfeeding protects the mother from the development of breast and ovarian cancer and helps her recover during the postpartum period . WHO hasThe following represents a thorough review of the literature, including the research and recommendations published so far related to breastfeeding during the COVID-19 pandemic. The comprehensive review focuses on the available evidence on the transmission of SARS-CoV-2 through breastmilk and recommendations for nursing mothers who are COVID positive on safe and protective breastfeeding.The biggest fear of breastfeeding newborns during the COVID-19 period is the fear of transmitting the virus through breastmilk. There is still not a substantiate amount of evidence available regarding the transmission of SARS-CoV-2 in breastmilk. Much of our knowledge comes from the data obtained from smaller studies and case reports  could not be determined ••, 19. IHowever, in addition to being smaller studies, there are several limitations associated with these reported studies. In most of the studies where the virus was detected in breastmilk, no precise information was available on sample collecting techniques. Also, only few of these studies tested breastmilk samples for the virus for several consecutive days. Groß R et al. reported that the breastmilk samples from one of the mothers in the study tested positive for four consecutive days before it tested negative •. ChambeIt is well-known that breastmilk protects the infant by providing antibodies during the first few months of life while the infant’s immune system is still developing. Whether the same is true in the case of COVID-19 is still not entirely known. There have been few reports suggesting the presence of IgG and IgA (immunoglobulins G and A) antibodies in breastmilk, which could offer immunity to the newborn from COVID-19 , 23. AnoDuring the earlier months of COVID-19, there appeared to be a lack of consensus regarding breastfeeding for COVID-19 positive mothers. In February 2020, Chinese experts recommended against breastfeeding for mothers with suspected or confirmed COVID-19. They advised initiating breastfeeding only if the mother and her breast milk samples tested negative .Based on the currently available evidence, WHO has recommended that the mothers with confirmed or suspected COVID-19 should continue to breastfeed since the benefits of breastfeeding to both the mothers and newborns significantly outweigh the risk of transmission of COVID-19 to the newborns . These gAfter examining the limited data published so far, we can conclude that our knowledge of the transmission of the SARS-CoV-2 virus through breastmilk is quite insufficient. At the same time, we cannot ignore the evidence that proves the benefits of breastfeeding to both the mother and the newborn. Even though, based on the limited set of literature, it can be inferred that if the health of both the mother and her newborn permits, continuation of breastfeeding should be advocated to the mothers with COVID-19; healthcare providers should still cautiously discuss the risks of vertical transmission with the patients to help them make a well-informed decision. Providers should also educate the mothers about the disease process as well as the precautions required to prevent contact transmission of COVID-19 to the newborns. When direct breastfeeding is not feasible due to health concerns, giving extracted breastmilk to the newborns can be considered. An in-depth analysis of virus pathophysiology and its transmission through breastmilk will be possible once more extensive studies are conducted to examine this correlation.


Previous studies suggest that health intervention designed to increase cervical cancer screening has been effective to reduce cervical cancer incidence and mortality. The aim of this study is to determine the effect of a home-based health education intervention for increasing cervical cancer screening uptake delivered by trained female community health volunteers (FCHVs), a category of community health worker in Nepal.A community-based, open-label, two-armed, cluster-randomized trial . The participants are recruited from a population-based survey with a sample size of 884. Based on population proportion size, 277 women will be recruited for the intervention group and 413 women recruited for the control group. A 12-month community-based health education intervention will be administered mobilizing the FCHVs, based on the Health Belief Model. The primary outcome measure of the study will be the difference in percentage of cervical cancer screening uptake between the two study arms. The primary outcomes will be modeled by using mixed-effect logistic regression analysis.COBIN-C is the first study investigating the effect of a community-based health education intervention by FCHVs on increasing cervical cancer screening uptake among women in Nepal. The purpose of this study is to develop and implement a home-based, culturally sensitive program to increase cervical cancer screening coverage at the community level.NCT03808064. Registered on January 14, 2019.ClinicalTrials.gov The online version contains supplementary material available at 10.1186/s13063-021-05049-3. Cervical cancer is a common cancer among women in most low- and middle-income countries (LMICs) in Asia, Africa, and Latin America . It is tStudies suggest that mobilizing lay health workers and empowering them with integrated training could contribute in expansion of the knowledge base on non-communicable diseases (NCDs) at the community level and harmonize the community-based interventions –13. In NMobilizing FCHVs to educate women and encourage them to participate in cervical cancer screening, and thus strengthening grass root level primary health provision, could be an acceptable and culturally appropriate intervention, which needs to be tested. Furthermore, it may contribute in the implementation of the National Cervical Cancer Screening and Prevention policy and ultimately help to reduce cervical cancer mortality in Nepal. The health promotion package will be a simple, feasible and effective approach.In our study, we will conduct a community based cluster-randomized controlled trial (cRCT) to explore the roles of FCHVs in educating and empowering women through home visits to increase the uptake of VIA screening. Therefore, the primary outcome of the study is to increase the cervical cancer-screening uptake in the study area.This manuscript adheres to the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement: extension to cRCT .The proposed trial is a community-based, 12-month, open label, two-arm cRCT with an equal allocation of clusters  between intervention and control arms in a semi-urban area of Pokhara Metropolitan city . It is one of the two areas where the Cervical Cancer Screening and Prevention (CCSP) pilot program was first initiated in Nepal. The total population of Lekhnath municipality is 59,498  with the total number of households being 14,958 as per the 2011 census . Each waThe populations targeted in our study area are women aged 30–60 years , listed in the Community-Based Intervention for Non-communicable diseases study for hypertension (COBIN-H) , 16. We A sampling framework was adopted from the COBIN-H trial that has taken a population framework of all eligible participants from the voter list prepared by the Election Commission in 2017 . As of 3Clusters will be randomly assigned to intervention and control areas using a 1:1 allocation ratio. Randomization will be done before the participants are recruited. Research teams responsible for identifying potential participants, obtaining consent, and recruiting trial participants will be blinded to the participants’ allocation status. The following steps will be taken to avoid selection bias during random allocation: (a) clusters will be randomized only after the baseline survey, (b) clusters will not be withdrawn from or added to the study, and (c) a statistician from abroad who is not familiar to the study will randomize the clusters.Women aged 30–60 years who are listed in COBIN-H and who indicated they planned to reside in a cluster in the study area for at least the next 12 months are eligible for inclusion in the baseline survey and for the invitation to participate in the study. Women who are severely ill, pregnant, less than 6 weeks after delivery, who are already diagnosed with cervical pre-cancer and cancer, with a history of total hysterectomy, as well as those who decline to participate in the study or unable to complete the interview, will be excluded from the study.The primary outcome of the study is percentage change in cervical cancer screening uptake from baseline to follow-up in the intervention group compared to the control group in a 12-month home-based health education intervention. The baseline survey will be conducted before the cluster randomization, and a follow-up survey of similar method will be conducted after 12-month intervention. The response of each participant for cervical cancer screening in the baseline survey and the follow-up survey will be compared to evaluate the percentage. In addition, FCHV will provide a referral slip to the participants in the intervention group  during the home visit, which they take with them and submit to the health facility for VIA screening. The referral slip contains study code, name, age, address, and signature of both FCHV and the participant. FCHV will inform the participants about the health facility where the services are freely available. The key health personnel providing the VIA screening services are oriented about the study. Furthermore, confidentiality about the participants’ information will be maintained throughout the intervention. The field supervisor will collect the referral slip to confirm that the participants have received VIA screening and results from all the health facilities of the intervention ward as a reference to the participants’ response.The secondary outcomes of the study are change in the level of knowledge and awareness among women on cervical cancer screening and prevention. The response of each participant for knowledge and awareness on cervical cancer screening in the baseline survey and the follow-up survey will be compared to evaluate the change using the validated questionnaire , 20.The sample size for the baseline survey is calculated based on the method suggested by the WHO STEPWISE Approach : for ourAfter randomization, the FCHVs in intervention wards will receive training coordinated by the research team in collaboration and close supervision of the local health authority. The training manual is designed under the guidance of experts in cervical cancer and public health from the national and international organizations. The training manual is based on the following: training of community health workers, WHO, 2017 training manual  and CervAt the end of the training, FCHVs will assign the number of households that have to be visited based on the baseline survey. Participants in the intervention clusters will receive a 12-month, home-based, health education package administered by FCHVs in a household setting. FCHVs will visit each household, meet the selected participant, receive approval, and provide the health education. On average, one FCHV will meet 18 participants three times a year. FCHV will complete the three visits to provide health education even if the participants have already received VIA screening. During the visit, FCHVs will deliver the health education intervention, to encourage and empower women for cervical cancer screening uptake available free of cost at their local health facility. They will also conduct two reinforcement visits every 4 months from the first visit (three home visits in a year).Health workers conducting cervical cancer screening at the local health facility in the intervention group will receive orientation about the study to make sure that the women attending the health facility receive screening. They will ensure that the equipment is functional and reagents are available, record information, provide psychosocial counseling to the case positives, and refer for further confirmation and treatment. The recorded information at the health facility will be tallied using the referral slip to confirm women’s participation for cervical cancer screening. The collected data will be kept strictly confidential and will only be accessed by the members of the trial team.Each participant is assigned an individual trial identification number, which is securely stored. The principal investigator reserves the right to the data set and could share upon reasonable request.There will be no special criteria for discontinuing or modifying allocated interventions. Furthermore, the implementing community-based intervention for cervical cancer screening uptake will not require alteration to usual care pathways (including use of any medication) and will continue for both the trial arms.A field supervisor will routinely visit each FCHV in order to maintain motivation and collection of records from the health facilities. The supervisor also provides ad hoc help to the FCHVs when needed during the intervention period. We will also use a supervision checklist to track and update the knowledge and skill levels of FCHVs as well as to ensure fidelity to the study protocol.In the control clusters, participants will receive “usual care.” The usual care means the current health education and promotion practices for cervical cancer screening at the community level provided by the government health system. They will not receive further contact, information, or educational materials from FCHVs until the 12-month assessment.The risk and level of contamination will be monitored at the levels of FCHVs and participants. FCHVs will be instructed not to share information about the study and not to provide any support to people from other clusters in the community, other than the ones who are assigned. The risk of contamination will also be minimized by the cluster design, whereby the intervention and control clusters will be geographically separated and the chance of intervention cluster participants regularly meeting control cluster participants will be negligible. All possible means of contamination between intervention and control participants will be collected during the follow-up survey and adjustments will be made for estimating the effect.The intervention is designed under the guidance of experts in cervical cancer and public health from the national and international organizations. The subject experts and major stakeholders reviewed and validated the training materials. The education session were delivered under the close supervision of the local level health authority and the research team, who will monitor the intervention. The Data Safety and Monitoring Board (DSMB) along with a member from the Nepal Health Research Council will evaluate the progress of the trial, including periodic assessments of data quality and timeliness, participant recruitment, accrual and retention, participant risk versus benefit, performance of the trial site, and other factors that can affect study outcome. Each FCHV in the intervention cluster will be advised of the importance of following the intervention steps and content during the training session. The field supervisor will monitor FCHVs tasks. FCHVs will deliver the services guided by a register that keeps them reminded of stages of the intervention and will report every 4 months.STATA version 15 software  will be used for quantitative analysis following the Consolidated Standards of Reporting Trials (CONSORT) guidelines for cluster-randomized trials. Flowcharts . Double entry for 10% of the data will qualify potential errors in data entry quality. The research officer will crosscheck the data quality on the spot and in our field office for any incomplete, inconsistent, and invalid data. Any deviation in the data quality will require recollecting in the field.The study carries minimal risk to the study participants, which is why we have no compensation or insurance plan for the participants. However, we will provide medical insurance and US$5 per home visit to FCHVs for transportation expenses.The results of the trial will be published in a peer-reviewed journal.The COBIN-C study is the first investigation of a community-based intervention to increase cervical cancer screening uptake in Nepal. We expect that a community-based, culturally tailored education intervention delivered by community health workers such as FCHVs will play a key role in increasing cervical cancer screening uptake in the study area. Indeed, community health workers can play an important role in understanding the cultural barriers including community health practices, cultural identity, and coping practices that can promote positive health outcomes .Furthermore, studies from Iran, Kenya, and Nigeria have reported health education interventions and behavior change frameworks provide an effective base for cervical cancer prevention , 28–30. The health promotion package is a simple, feasible, and potentially effective approach. It may contribute in the implementation of the National Cervical Cancer Screening and Prevention policy and help reduce cervical cancer mortality in the long term in Nepal and if successful may be adapted to similar settings elsewhere.The trial is now closed to participant accrual, but the trial is ongoing. The endpoint assessment of all the participants will be completed by the end of August 2021. The schedule of enrollment, interventions, and assessments is presented in Table The protocol version number and date—V1, 28 December 2020.Additional file 1. SPIRIT 2013 Checklist: COBIN-C.Additional file 2. Questionnaire: COBIN-C.