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PurposeThe purpose of this study is to the role of bonding and bridging social capital of women entrepreneurs in dealing with the business crisis induced by the COVID-19 pandemic.Design/methodology/approachA total of 24 women entrepreneurs from Deoria district from the state of Uttar Pradesh in India are selected through the snowball sampling technique. Out of these 24 female entrepreneurs, 14 belong to some network and 10 are not a part of any network. A qualitative methodology based upon in-depth personal interviews is used to get insights about the personal views of the female entrepreneurs on the role played by their bonding and bridging social capital to deal with the problems during the current business crisis.FindingsContrary to the general belief that women-owned businesses succumb easily to business crises and respond to reduce risks, the findings suggest that women business owners confidently faced the adverse business conditions. The bonding social capital of women entrepreneurs provided emotional support and motivation through internal interactions and responsibility sharing, and business networks (bridging capital) facilitated identification of new opportunities. The respondents emphatically mentioned the role of peer entrepreneurs as sources of information and resources.Originality/valueTo the best of the authors' knowledge, this is the first study in the Indian context that seeks to investigate the role of bonding and bridging social capital during the business crisis induced by the current pandemic using qualitative data generated through in-depth interviews.

The requirement for alternatives in roll-to-roll (R2R) processing to expand thin film inspection in wider substrates at lower costs and reduced dimensions, and the need to enable newer control feedback options for these types of processes, represents an opportunity to explore the applicability of newer reduced-size spectrometers sensors. This paper presents the hardware and software development of a novel low-cost spectroscopic reflectance system using two state-of-the-art sensors for thin film thickness measurements. The parameters to enable the thin film measurements using the proposed system are the light intensity for two LEDs, the microprocessor integration time for both sensors and the distance from the thin film standard to the device light channel slit for reflectance calculations. The proposed system can deliver better-fit errors compared with a HAL/DEUT light source using two methods: curve fitting and interference interval. By enabling the curve fitting method, the lowest root mean squared error (RMSE) obtained for the best combination of components was 0.022 and the lowest normalised mean squared error (MSE) was 0.054. The interference interval method showed an error of 0.09 when comparing the measured with the expected modelled value. The proof of concept in this research work enables the expansion of multi-sensor arrays for thin film thickness measurements and the potential application in moving environments.

Fake news dissemination is a critical issue in today’s fast-changing network environment. The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. This paper deals with the categorical cyber terrorism threats on social media and preventive approach to minimize their issues. Misleading or unreliable information in form of videos, posts, articles, URLs are extensively disseminated through popular social media platforms such as Facebook, Twitter, etc. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model, Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.

Background: Iron-deficiency anemia among school-aged children is widespread in India. The efficacy of micronutrient and iron fortified school-served meals in reducing iron deficiency anemia has been demonstrated in randomized controlled trials in other parts of the globe. The current study evaluates its effectiveness in real-world Indian settings. Methods: Mid-day-Meal (MDM) programme provides free lunch to students of grade 1 to 8 in all public-funded Indian schools. An implementation research project fortified MDM of all public schools of 4 out of 8 sub-districts (“blocks”) of Dhenkanal district of Odisha state with fortified rice kernel (FRK). All the schools of the other 4 blocks fortified with micronutrient powders (MNP)—both FRK and MNP containing equal amounts of supplementary iron and other micronutrients. Schools of 4 matched blocks of neighboring nonimplementing Angul district served as control. Cross-sectional representative samples of students were drawn from the 3 arms, before and after intervention (n = 1764 and n = 1640 respectively). Pre-post changes in anemia prevalence and hemoglobin levels were estimated in the sampled children using difference-in-difference analysis after controlling for inter-arm differences in socioeconomic status, and iron and deworming tablet consumptions. Results: Factoring in pre-post changes in control and adjusting for potential confounders, the proportion of children without anemia and mean hemoglobin improved by 1.93 (1.38, 2.24, P < .001) times and 0.24 (−0.03, 0.51, P = .083) g/dL in MNP; and 1.63 (1.18, 2.24, P = .002) times and 0.18 (−0.09, 0.45, P = .198) g/dL in FRK arms. Conclusions: Fortified MDM could effectively improve anemia status among Indian school-aged children under real-world conditions.

Background: The recently identified SARS-CoV-2 coronavirus has resulted in the Covid-19 pandemic with severe morbidity and high mortality, particularly in certain sections of the population. The co-morbidity patterns associated with adverse outcomes are multiple and complex and there is emerging epidemiological, nutritional and molecular biological evidence that an inadequate vitamin D status is a contributing factor. Objective: The aim was to review the role of vitamin D in immune function with particular reference to the mechanisms whereby it supports immune efficiency, host protection and immune modulation. The evidence for the possible benefit of vitamin D supplementation to ameliorate the severity of respiratory infection by SARS-CoV-2 and other pathogens was also reviewed with a view to making a recommendation. Methods: PubMed, MEDLINE and Google Scholar were searched using the terms: Covid-19, coronavirus, SARS-CoV-2, vitamin D, calcitriol, deficiency, adaptive immunity, innate immunity, ventilation, critical care, intensive care, acute respiratory distress syndrome, cytokine storm, respiratory viruses, respiratory tract infection, respiratory syncytial virus, influenza, supplementation. Papers for inclusion were selected on the basis of relevance and quality. Findings: Vitamin D insufficiency is widespread in many parts of the world. Vitamin D is needed for normal protective and surveillance immune function and there is evidence that deficiency increases the risk of some respiratory infections, probably including Covid-19. By binding with dedicated receptors on immune cells vitamin D influences several strands of immune function, including the production of anti-microbial peptides and several cytokines that promote an appropriate immune response. Vitamin D supplementation probably reduces the risk of respiratory infection, with persuasive biological, epidemiological and observational evidence for possible benefit against Covid-19. Conclusion: Despite the lack of direct evidence specific to Covid-19 a cogent theoretical case can be made for giving adults from selected groups, and arguably all adults, routine supplementation with vitamin D to improve immune efficiency and reduce the incidence and severity of respiratory infections. This could be particularly important in sections of the population with a high prevalence of vitamin D insufficiency. Targeted research is required to provide firm evidence to guide practice.

Additive manufacturing (AM) is emerging within many industrial applications due to inherent advantages such as rapid prototyping and produc- tion. However, the correlation of process parameters across modules and their impacts on product quality are not yet fully understood. This article presents a system built out of Internet of Things (IoT) and edge computing technologies to collect and analyze AM process in-situ. An IoT thermal camera platform was developed, and integrated within an Laser Based Additive Manufacturing (LBAM) system to collect information that could be used to characterize the thermal distribution surrounding the melt pool. Machine learning techniques were utilised to identify the occurrence of defects using the collected low-resolution thermal images.

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.

Caste, a stratifying axis of the Indian society, is associated with wealth and health. However, to what extent caste-based health inequality is explained by wealth disparities, is not clear. Therefore, we aimed to examine the caste-based differences in anaemia (haemoglobin < 11 gm/dl) and self-reported sickness absenteeism in schoolchildren and the mediating role of economic disparity. Students (n = 1764) were surveyed from 54 government schools of Dhenkanal and Angul, Odisha state. Socioeconomic data, anaemia and absenteeism were recorded. The relative risks of anaemia among Scheduled Tribe (least advantaged) and Scheduled Caste (second least advantaged) students were 1.19 (95% CI: 1.08, 1.26) and 1.13 (1.03, 1.20), respectively, as compared to students of the most advantaged caste and that for sickness absenteeism were 2.78 (2.03, 3.82) and 2.84 (2.13, 3.78); p < 0.05, with marginal attenuation when controlled for inter-caste economic disparities. Caste had an independent effect on anaemia and sickness absenteeism in school children, unexplained by inter-caste economic disparities.

A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy.

Treatment of tuberculosis (TB) involves the administration of anti-mycobacterial drugs for several months. The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb, the causative agent) together with increased disease severity in people with co-morbidities such as diabetes mellitus and HIV have hampered efforts to reduce case fatality. In severe disease, TB pathology is largely attributable to over-exuberant host immune responses targeted at controlling bacterial replication. Non-resolving inflammation driven by host pro-inflammatory mediators in response to high bacterial load leads to pulmonary pathology including cavitation and fibrosis. The need to improve clinical outcomes and reduce treatment times has led to a two-pronged approach involving the development of novel antimicrobials as well as host-directed therapies (HDT) that favourably modulate immune responses to Mtb. HDT strategies incorporate aspects of immune modulation aimed at downregulating non-productive inflammatory responses and augmenting antimicrobial effector mechanisms to minimise pulmonary pathology and accelerate symptom resolution. HDT in combination with existing antimycobacterial agents offers a potentially promising strategy to improve the long-term outcome for TB patients. In this review, we describe components of the host immune response that contribute to inflammation and tissue damage in pulmonary TB, including cytokines, matrix metalloproteinases, lipid mediators, and neutrophil extracellular traps. We then proceed to review HDT directed at these pathways.

Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

Electrical machines have recently received a lot of attention due to a variety of applications in several industries. Although advances in digital technologies have enabled more efficient production of electrical machines, faults are still identified at the end of the line tests. In order to avoid accumulation of defects during the production chain, it is desirable to identify faults early in the process. This can be achieved by identifying how critical process parameters and the interdependencies between them influence the occurrence of faults. This poses a challenge in electrical machine manufacturing because of the complexity involved in various manufacturing steps involving deformable material, an example is coil winding. This paper proposes a computational framework to model interdependencies in a complex electrical machine manufacturing process involving deformable material. A Discrete Event Simulation model representing the coil winding process demonstrated that input parameters such as wire tension and winding speed influence physical and electrical properties of the coil (enamelled copper wire) leading to generation of defects in the final product.

The market for electric motors is experiencing a step-growth due to their adoption across a range of industrial sectors. This increased demand also highlights the importance of end-of-life management of electric motors and a requirement for appropriate strategies for the high value materials embedded in them. This paper aims to offer a holistic view on the circular economy research for electric motors and the role of Industry 4.0 technologies by presenting the state-of-the-art available in literature and comparing it with the industrial perspective. The literature review revealed the absence of a methodology for selecting the best end-of-life scenario for industrial electric motors. Recycling, which is an end-of-product-life strategy, was found to be the key focus area of research. Reuse, which is a better strategy in terms of waste hierarchy, was the least researched area due to lack of information about the condition and availability of returned products. In order to capture the current landscape within the UK for the repair, remanufacture and recycling of electrical machines, a structured survey of UK based companies was conducted. The survey revealed that nearly half of the companies do not undertake any repair strategies for electrical machine components, however, there was an aspiration from the respondents to migrate their companies towards more sustainable activities. The industry survey and the review of existing literature led to the identification of research trends, challenges and recommendations for future research.

Studying latent Mycobacterium tuberculosis (Mtb) infection has been limited by the lack of a suitable mouse model. We discovered that transient depletion of biotin protein ligase (BPL) and thioredoxin reductase (TrxB2) results in latent infections during which Mtb cannot be detected but that relapse in a subset of mice. The immune requirements for Mtb control during latency, and the frequency of relapse, were strikingly different depending on how latency was established. TrxB2 depletion resulted in a latent infection that required adaptive immunity for control and reactivated with high frequency, whereas latent infection after BPL depletion was independent of adaptive immunity and rarely reactivated. We identified immune signatures of T cells indicative of relapse and demonstrated that BCG vaccination failed to protect mice from TB relapse. These reproducible genetic latency models allow investigation of the host immunological determinants that control the latent state and offer opportunities to evaluate therapeutic strategies in settings that mimic aspects of latency and TB relapse in humans.

Organic mulches are easily available and cheap in North Bihar region. Therefore, an experiment was conducted to find out the suitability of different types of organic mulches in tomato cultivation. Among the vegetables tomato cultivation faces challenges in producing crop without chemicals for effective weed control. Use of several kinds of mulches gives the opportunity to control weeds effectively, regulate soil temperature and also lower down evaporation from the soil. The experiment was conducted in Krishi Vigyan Kendra, Jalalgarh, Purnea during 2015 and 2016. The treatments were designed by using different organic materials as mulch i.e. pea straw, dry neem leaves, paddy straw and one control without mulch. The observations were recorded on plant height (cm), number of branches per plant, days to 50% flowering, average fruit weight (gm) and total fruit yield (q/ha). The mulches were spread after twenty days of the transplanting of tomato seedlings in 10 cm thick layer. The result of the experiment indicates that organic mulches reduced soil temperature and weed infestation. All these enhanced the growth and fruit yield of tomato as well as weed control, soil moisture conservation and temperature modification resulted good crop growth with more quality fruit yield.

Organic agriculture seeks to augment ecological process that foster plant nutrition while conserving soil and water resources. Organic systems eliminate agrichemicals and reduce other external inputs to improve the environment as well as farm economics. It is a production system which favours maximum use of organic materials like crop residues, FYM, compost, green manures, oil cakes, biodynamic preparations and bio fertilizers etc. to enhance crop production, carbon sequestration and improve soil health. Organic production system is based on specific and precise standards of production which aim to achieving agro-ecosystems which are socially and ecologically sustainable. As demand for organically grown food has been growing rapidly and significant proportion of consumers believe that organic food is qualitatively better than non organic, the present study showed not only the quality of the produce but also quantitatively higher yield that touched the world record production (660 q/ha) in Nalanda Bihar.