Search

Abstract This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

ABSTRACT Background We evaluated the validity of surrogacy of progression‐free survival (PFS) or time‐to‐progression (TTP) and overall response rate (ORR) in phase II trials of pancreatic ductal adenocarcinoma (PDAC). In addition, we explored the impact of predictive variables on overall survival (OS) and developed an optimal OS model. Methods We analyzed 1867 clinical endpoint from 619 phase II PDAC trials with a systematic search from PubMed. Endpoint correlations were determined by Spearman's rank correlation. The assessed predictive factors included PFS/TTP, treatment size, therapy type, stage, and previous treatment. The relationship between predictors and OS was explored by a gamma generalized linear model (GLM) with a log‐link function and compared with linear models. Results The Spearman rank correlation coefficient between PFS/TTP and OS was 0.88 (95% confidence interval [CI] 0.85–0.89; p

Abstract Background We conducted a systematic review aimed to evaluate the effects of non-pharmaceutical interventions within non-healthcare workplaces and community-level workplace closures and lockdowns on COVID-19 morbidity and mortality, selected mental disorders, and employment outcomes in workers or the general population. Methods The inclusion criteria included randomized controlled trials and non-randomized studies of interventions. The exclusion criteria included modeling studies. Electronic searches were conducted using MEDLINE, Embase, and other databases from January 1, 2020, through May 11, 2021. Risk of bias was assessed using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analysis and sign tests were performed. Results A total of 60 observational studies met the inclusion criteria. There were 40 studies on COVID-19 outcomes, 15 on anxiety and depression symptoms, and five on unemployment and labor force participation. There was a paucity of studies on physical distancing, physical barriers, and symptom and temperature screening within workplaces. The sign test indicated that lockdown reduced COVID-19 incidence or case growth rate (23 studies, p

As part of a multiyear project that monitored illness-related school closures, we conducted systematic daily online searches during July 27, 2020–June 30, 2022, to identify public announcements of COVID-19–related school closures (COVID-SCs) in the United States lasting >1 day. We explored the temporospatial patterns of COVID-SCs and analyzed associations between COVID-SCs and national COVID-19 surveillance data. COVID-SCs reflected national surveillance data: correlation was highest between COVID-SCs and both new PCR test positivity (correlation coefficient [r] = 0.73, 95% CI 0.56–0.84) and new cases (r = 0.72, 95% CI 0.54–0.83) during 2020–21 and with hospitalization rates among all ages (r = 0.81, 95% CI 0.67–0.89) during 2021–22. The numbers of reactive COVID-SCs during 2020–21 and 2021–22 greatly exceeded previously observed numbers of illness-related reactive school closures in the United States, notably being nearly 5-fold greater than reactive closures observed during the 2009 influenza (H1N1) pandemic.

Both SARS-CoV-2 and influenza virus can be transmitted by asymptomatic, presymptomatic, or symptomatic infected persons. We assessed effects on work attendance while ill before and during the COVID-19 pandemic in the United States by analyzing data collected prospectively from persons with acute respiratory illnesses enrolled in a multistate study during 2018–2022. Persons with previous hybrid work experience were significantly less likely to work onsite on the day before through the first 3 days of illness than those without that experience, an effect more pronounced during the COVID-19 pandemic than during prepandemic influenza seasons. Persons with influenza or COVID-19 were significantly less likely to work onsite than persons with other acute respiratory illnesses. Among persons with positive COVID-19 test results available by the second or third day of illness, few worked onsite. Hybrid and remote work policies might reduce workplace exposures and help reduce spread of respiratory viruses.

The dataset explores the impact of waterlogging stress on sesame plants during the pre-flowering stage, recognizing its global impact on crop yield and the identification of tolerant genotypes using the MGIDI index. Carried out in Bangladesh, the research assesses the survival status, grain yield, and stress tolerance indices of 40 sesame genotypes, revealing that twelve of them demonstrated resilience under 72 h of waterlogging stress at the pre-flowering stage. There were variations in genotypic grain yield, and G15 exhibited the highest yields, recording 5.22 g/plant under normal conditions and 4.10 g/plant under waterlogging stress. The MGIDI index, evaluating waterlogging tolerance, identified G4 as the most favorable genotype, followed by G5 and G12. Factor analysis within the MGIDI index uncovered distinct tolerance and susceptibility indices, highlighting strengths and weaknesses in the selected genotypes. The selection gain percentages of these genotypes ranged from 12.9 to 37.4, indicating high broad-sense heritability (≥0.97). These results underscore the potential of genotype selection based on waterlogging stress indices, providing valuable insights for breeders addressing stress-related crop challenges in the face of changing climatic conditions.

Introduction COVID-19 has caused severe disruption to clinical services in Bangladesh but the extent of this, and the impact on healthcare professionals is unclear. We aimed to assess the perceived levels of anxiety, depression and burnout among doctors and nurses during COVID-19 pandemic.Methods We undertook an online survey using RedCap, directed at doctors and nurses across four institutions in Bangladesh (The Sheikh Russel Gastro Liver Institute & Hospital (SRNGIH), Dhaka Medical College Hospital (DMCH), Mugda Medical College Hospital (MMCH) and M Abdur Rahim Medical College (MARMC) Hospital). We collected information on demographics, awareness of well-being services, COVID-19-related workload, as well as anxiety, depression and burnout using two validated questionnaires: the Hospital Anxiety and Depression Scale (HADS) and the Maslach Burnout Inventory (MBI).Results Of the 3000 participants approached, we received responses from 2705 (90.2%). There was a statistically significant difference in anxiety, depression and burnout scores across institutions (p

Non-pharmaceutical interventions minimize social contacts, hence the spread of respiratory pathogens such as influenza and SARS-CoV-2. Globally, there is a paucity of social contact data from the workforce. In this study, we quantified two-day contact patterns among USA employees. Contacts were defined as face-to-face conversations, involving physical touch or proximity to another individual and were collected using electronic self-kept diaries. Data were collected over 4 rounds from 2020 to 2021 during the COVID-19 pandemic. Mean (standard deviation) contacts reported by 1456 participants were 2.5 (2.5), 8.2 (7.1), 9.2 (7.1) and 10.1 (9.5) across round 1 (April–June 2020), 2 (November 2020–January 2021), 3 (June–August 2021), and 4 (November–December 2021), respectively. Between round 1 and 2, we report a 3-fold increase in the mean number of contacts reported per participant with no major increases from round 2–4. We then modeled SARS-CoV-2 transmission at home, work, and community settings. The model revealed reduced relative transmission in all settings in round 1. Subsequently, transmission increased at home and in the community but remained exceptionally low in work settings. To accurately parameterize models of infection transmission and control, we need empirical social contact data that capture human mixing behavior across time.

This study assesses future climate change impacts on the hydrologic response of the Upper Meghna River Basin (UMRB), a major river system in Bangladesh. Separate SWAT (Soil and Water Assessment Tool) hydrologic models were developed for the three major sub-basins of the UMRB, i.e., Barak, Meghalaya, and Tripura, considering their unique geographical, hydrological, and land-use characteristics. To evaluate the efficiency of multi-site modeling in providing better model performance, the SWAT models were calibrated at both single and multiple locations. Those models were then simulated to estimate future flows using climate projection data from thirteen CMIP6 General Circulation Models (GCMs) under moderate and extreme emission scenarios, SSP2-4.5, and SSP5-8.5. The results revealed that the annual maximum flow will keep increasing gradually with time. The outlets of the Meghalaya sub-basin will experience a more significant rise in future flow in the upcoming decades compared to the Barak and Tripura sub-basins. Results showed that dry season flows with increases of up to 31–50% would be less affected compared to the wet periods, which could experience increases of up to 47–66%) across the sub-basins by the end of the 21st century under extreme emission projections. Besides an increasing trend in the mean flow, future flows at several outlets also exhibited an escalation in the intensity and frequency of extreme flood events.

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world’s raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models’ findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves.

Persons with COVID-19–like illnesses are advised to stay home to reduce the spread of SARS-CoV-2. We assessed relationships between telework experience and COVID-19 illness with work attendance when ill. Adults experiencing fever, cough, or loss of taste or smell who sought healthcare or COVID-19 testing in the United States during March–November 2020 were enrolled. Adults with telework experience before illness were more likely to work at all (onsite or remotely) during illness (87.8%) than those with no telework experience (49.9%) (adjusted odds ratio 5.48, 95% CI 3.40–8.83). COVID-19 case-patients were less likely to work onsite (22.1%) than were persons with other acute respiratory illnesses (37.3%) (adjusted odds ratio 0.36, 95% CI 0.24–0.53). Among COVID-19 case-patients with telework experience, only 6.5% worked onsite during illness. Telework experience before illness gave mildly ill workers the option to work and improved compliance with public health recommendations to stay home during illness.

Abstract Background In early 2020, following the start of the coronavirus disease 2019 (COVID-19) pandemic, institutions of higher education (IHEs) across the United States rapidly pivoted to online learning to reduce the risk of on-campus virus transmission. We explored IHEs’ use of this and other nonpharmaceutical interventions (NPIs) during the subsequent pandemic-affected academic year 2020–2021. Methods From December 2020 to June 2021, we collected publicly available data from official webpages of 847 IHEs, including all public (n = 547) and a stratified random sample of private four-year institutions (n = 300). Abstracted data included NPIs deployed during the academic year such as changes to the calendar, learning environment, housing, common areas, and dining; COVID-19 testing; and facemask protocols. We performed weighted analysis to assess congruence with the October 29, 2020, US Centers for Disease Control and Prevention (CDC) guidance for IHEs. For IHEs offering ≥50% of courses in person, we used weighted multivariable linear regression to explore the association between IHE characteristics and the summated number of implemented NPIs. Results Overall, 20% of IHEs implemented all CDC-recommended NPIs. The most frequently utilized NPI was learning environment changes (91%), practiced as one or more of the following modalities: distance or hybrid learning opportunities (98%), 6-ft spacing (60%), and reduced class sizes (51%). Additionally, 88% of IHEs specified facemask protocols, 78% physically changed common areas, and 67% offered COVID-19 testing. Among the 33% of IHEs offering ≥50% of courses in person, having

Introduction: Fossil fuel and electricity-based irrigation practices contribute to greenhouse gases and add substantial costs to water access. Solar-powered irrigation is spreading globally, notably in developing countries, as a solution to the rising energy and climate concerns related to agriculture. This policy perspective devoted to examining the impact of the solar irrigation facilities (SIF) adoption on irrigation cost and return on investment (ROI) based on seven years of panel data seeks to contribute to the efforts to propel solar irrigation toward delivering on the myriad of promises.Methods: Panel logistic regression was employed to analyze adoption determinants, while adoption impact was evaluated through the propensity score matching with the difference-in-difference (PSM-DID) method. In addition, the time and panel fixed effect DID and doubly robust DID model was also used for robustness check.Results: The result reveals that SIF adoption significantly increased ROI by 20% to 30% and reduced irrigation costs by 21% to 30%.Conclusion: The findings call for further research and analysis on evidence-based best practices for solar irrigation solutions at the farm level so that the dissemination of this revolutionary technology, apart from contributing to the advancement of the energy sector, also plays a vital role in driving us towards establishing a more equitable and sustainable world.

Influenza causes significant mortality and morbidity in the United States (US). Employees are exposed to influenza at work and can spread it to others. The influenza vaccine is safe, effective, and prevents severe outcomes; however, coverage among US adults (50.2%) is below Healthy People 2030 target of 70%. These highlights need for more effective vaccination promotion interventions. Understanding predictors of vaccination acceptance could inform vaccine promotion messages, improve coverage, and reduce illness-related work absences. We aimed to identify factors influencing influenza vaccination among US non-healthcare workers. Using mixed-methods approach, we evaluated factors influencing influenza vaccination among employees in three US companies during April–June 2020. Survey questions were adapted from the WHO seasonal influenza survey. Most respondents (n = 454) were women (272, 59.9%), 20–39 years old (n = 250, 55.1%); white (n = 254, 56.0%); had a college degree (n = 431, 95.0%); and reported receiving influenza vaccine in preceding influenza season (n = 297, 65.4%). Logistic regression model was statistically significant, X (16, N = 450) = 31.6, p = .01. Education [(OR) = 0.3, 95%CI = 0.1–0.6)] and race (OR = 0.4, 95%CI = 0.2–0.8) were significant predictors of influenza vaccine acceptance among participants. The majority had favorable attitudes toward influenza vaccination and reported that physician recommendation would influence their vaccination decisions. Seven themes were identified in qualitative analysis: “Protecting others” (109, 24.0%), “Protecting self” (105, 23.1%), “Vaccine accessibility” (94, 20.7%), “Education/messaging” (71, 15.6%), “Policies/requirements” (15, 3.3%), “Reminders” (9, 2.0%), and “Incentives” (3, 0.7%). Our findings could facilitate the development of effective influenza vaccination promotion messages and programs for employers, and workplace vaccination programs for other diseases such as COVID-19, by public health authorities.

IntroductionBangladesh's quest to achieve sustainable development goals has highlighted the need to enhance resilience against the challenges that interconnected food, energy, and water (FEW) nexus systems faces to support human well-being. The government has decided to promote the adoption of competent and cutting-edge technologies that can significantly contribute to balancing energy and water resource utilization in achieving a more sustainable and climate-smart food production system. Hence, scaled-up adoption of solar-powered irrigation systems and recommended fertilizer dose (SIRFD) applications were proposed. This study, to provide practical policy implications, attempts to identify the determinants and impact of SIRFD adoption in water-scarce areas of Bangladesh.MethodsDeterminants of adoption were analyzed using multinomial logistic regression, and the adoption impact was analyzed using treatment effect models.ResultsThe results revealed that land typology, soil fertility perception, soil water retention, knowledge, environmental awareness, secondary income, close acquaintance adoption, and cash availability significantly influenced adoption decisions. The treatment effect model result indicated that farmers who adopted both technologies could reduce production costs by 1.36% and obtain an 8.92% higher ROI than non-adopters.ConclusionThe study findings suggest that policy interventions on scaling up SIRFD adoption require focusing on knowledge development village-based demonstration activities, group farming models backed by micro-finance, and avoiding launching conflicting schemes.

Abstract Background and Aim As the interrelationship between Helicobacter pylori, metabolic syndrome (MetS), and non‐alcoholic fatty liver disease (NAFLD) is controversial, we undertook a community‐based study with the aim to find the seroprevalence of H. pylori and its relationship with MetS and NAFLD. Methods This door‐to‐door survey was conducted among the adult subjects (≥18 years) of two villages (Charcharia of Dhaka district and Kharrah of Munshiganj district) of Bangladesh. Interviews using a structured questionnaire, clinical examination, anthropometric measurements, ultrasonogram of the liver, and biochemical tests were performed. Results Of 1021 subjects, 781 responded (76.49%), and 767 were included in the final analysis (mean age 40.35 ± 15.56 years; female 63.5%). Anti‐H. pylori antibodies were found in 418 of 767 (54.5%). There were no H. pylori serostatus association with MetS and diabetes mellitus (DM) in univariate or multivariate analysis (all P > 0.05). However, H. pylori seropositive subjects had lower systolic blood pressure (odds ratio [OR] = −2.95 [−5.58, −0.32]) and low density lipoprotein ‐cholesterol (OR −7.79 [−15, −0.57]) compared to seronegative subjects in the linear regression model. Seronegativity of H. pylori was associated with NALFD in univariate (P = 0.007) but not multivariate analysis (P = 0.086). There were no differences in the frequency of H. pylori seropositivity among the participants with nonobese compared to obese NAFLD (19/42 [45%] vs 43/99 [43.4%], P = 0.844). Conclusion In a rural community of Bangladesh, about 55% of the population were H. pylori seropositive, which was more common among the underweight than normal or obese subjects. However, there was no relationship observed between H. pylori seroprevalence and MetS, DM, or NAFLD.

Background: In the present study Xbal gene polymorphism was exposed with the use of PCR and its co-relation was established with the level of vitamin D in the pre-menopausal and post-menopausal women of north Indian population. There are several pre and post-menopausal women in this region are suffering from vitamin D deficiency. Objective: The main objective of this research was to find out Xbal gene polymorphism and % of its genotypes in respect to vitamin D in the pre-menopausal and post-menopausal women of north Indian population. This is a novel study of Xbal gene polymorphisms in the north Indian women. Methods: Patients suffered with vitamin D deficiency was calculated as protocol proposed by WHO and calculated total cholesterol level, BMI etc. For Xbal gene variation DNA was isolated with Qaigen kit and isolated DNA was amplified with PCR. Amplified DNA was separated with 1% agarose gel electrophoresis. The Hardy-Weinberg equilibrium, OR, CI and P-value were estimated using standard protocols. Results: Xbal gene polymorphism was found co-related significantly with vitamin D deficiency in the pre-menopausal and post-menopausal women. The GG genotype frequency was found three fold more prone for vitamin deficiency with (P

We assessed determinants of work attendance during the first 3 days after onset of acute respiratory illness (ARI) among workers 19–64 years of age who had medically attended ARI or influenza during the 2017–2018 influenza season. The total number of days worked included days worked at the usual workplace and days teleworked. Access to paid leave was associated with fewer days worked overall and at the usual workplace during illness. Participants who indicated that employees were discouraged from coming to work with influenza-like symptoms were less likely to attend their usual workplace. Compared with workers without a telework option, those with telework access worked more days during illness overall, but there was no difference in days worked at the usual workplace. Both paid leave benefits and business practices that actively encourage employees to stay home while sick are necessary to reduce the transmission of ARI and influenza in workplaces.

IntroductionOutside of pandemics, there is little information about occurrence of prolonged unplanned K-12 school closures (PUSC). We describe here the reasons, characteristics, and patterns of PUSC in the United States during 8 consecutive inter-pandemic academic years, 2011-2019.MethodsFrom August 1, 2011 through June 30, 2019, daily systematic online searches were conducted to collect data on publicly announced unplanned school closures lasting ≥1 school days in the United States. Closures were categorized as prolonged when schools were closed for ≥5 unplanned days (approximating one full workweek), excluding weekends and scheduled days off per school calendars.ResultsDuring the eight academic years, a total of 22,112 PUSCs were identified, affecting over 800,000 teachers and 13 million students that resulted in 91.5 million student-days lost. A median of 62.9% of students in PUSC-affected schools were eligible for subsidized school meals. Most affected schools were in cities (35%) and suburban areas (33%). Natural disasters (47%), adverse weather conditions (35%), and budget/teacher strikes (15%) were the most frequently cited reasons for PUSC; illness accounted for 1%, and building/facility issues, environmental issues and violence together accounted for the remaining 2%. The highest number of PUSCs occurred in Health and Human Services Regions 2, 3, 4, and 6 encompassing areas that are frequently in the path of hurricanes and tropical storms. The majority of PUSCs in these regions were attributed to a handful of hurricanes during the fall season, including hurricanes Sandy, Irma, Harvey, Florence, and Matthew.ConclusionsPUSCs occur annually in the United States due to a variety of causes and are associated with a substantive loss of student-days for in-school learning. Both these prior experiences with PUSCs and those during the current COVID-19 pandemic illustrate a need for creating sustainable solutions for high-quality distance learning and innovative supplemental feeding programs nationwide, especially in disaster-prone areas.

Due to the combined effect of biotic and abiotic constraints, rising population pressure, and inelastic demand in the crop and horticulture sector, Bangladesh has had to adopt heavily subsidized and intensified fertilizer policies to enhance crop productivity, achieve and sustain self-sufficiency in food production, and food security provision. Although the initiative has played a vital role in boosting production, it has also invigorated the unbalanced amount of fertilizer application practices raising questions about maintaining biodiversity and ecosystem services while feeding the nation’s population. Further research in this area must thus be applied to monitor and improve this sector. This study attempts to understand the issue by investigating the factors influencing Boro rice farmers’ adoption decisions of recommended fertilizer doses. The study employs an ordered probit model with a sample selection approach. The investigation is based on collected data from 405 randomly selected farmers using a face-to-face interview method. The farmers were classified into low, middle, high and non-adopter groups. The study revealed that farmers’ age, land typology, soil water retention, knowledge, and availability of cow dung significantly influenced farmers’ decision to apply fertilizers. However, farmers’ carry an aversion to following recommendations for fertilizer application due to their ambiguity about the whole system, their current fertilizer application-seeking behavior, and the lack of understanding of the environmental benefits of adoption. These issues urge policy interventions to initiate village-based demonstration programs that synthesize better synergies between recommended dose adoption, yield amelioration, sustainable soil care, and economics.

We measured contact patterns using online diaries for 304 employees of 3 U.S. companies working remotely. The median number of daily contacts was 2 (IQR 1-4); majority were conversation (55 %), occurred at home (64 %) and lasted >4 h (38 %). These data are crucial for modeling outbreak control among the workforces.

Background: The adaptive immune response is a crucial component of the protective immunity against SARS-CoV-2, generated after infection or vaccination. Methods: We studied antibody titers, neutralizing antibodies and cellular immune responses to four different COVID-19 vaccines, namely Pfizer-BioNTech, Moderna Spikevax, AstraZeneca and Sinopharm vaccines in the Bangladeshi population (n = 1780). Results: mRNA vaccines Moderna (14,655 ± 11.3) and Pfizer (13,772 ± 11.5) elicited significantly higher anti-Spike (S) antibody titers compared to the Adenovector vaccine AstraZeneca (2443 ± 12.8) and inactivated vaccine Sinopharm (1150 ± 11.2). SARS-CoV-2-specific neutralizing antibodies as well as IFN-γ-secreting lymphocytes were more abundant in Pfizer and Moderna vaccine recipients compared to AstraZeneca and Sinopharm vaccine recipients. Participants previously infected with SARS-CoV-2 exhibited higher post-vaccine immune responses (S-specific and neutralizing antibodies, IFN-γ-secreting cells) compared to uninfected participants. Memory B (BMEM), total CD8+T, CD4+ central memory (CD4+CM) and T-regulatory (TREG) cells were more numerous in AstraZeneca vaccine recipients compared to other vaccine recipients. Plasmablasts, B-regulatory (BREG) and CD4+ effector (CD4+EFF) cells were more numerous in mRNA vaccine recipients. Conclusions: mRNA vaccines generated a higher antibody response, while a differential cellular response was observed for different vaccine types, suggesting that both cellular and humoral responses are important in immune monitoring of different types of vaccines.

Insufficient rainfall in the dry season and scarcity of surface water has resulted in firms’ reliance on groundwater for agriculture in the northern part of Bangladesh. Most irrigation systems in the country are diesel or electric, which raises the cost and demand for energy and pollutes the environment. Utilizing the abundant sunshine and disseminating solar-based irrigation systems is expected to be a fittingly rewarding experience for irrigation purposes. Therefore, this study identifies the factors influencing the adoption of solar irrigation facilities (SIFs) and the impacts of their adoption on irrigation cost, return on investment (ROI), and production costs, using survey data collected from 405 rice farmers of Dinajpur district. The study employed three treatment effect estimators, namely inverse probability weighting (IPW), regression adjustment (RA), and inverse probability weighted regression adjustment (IPWRA), to address the potential selection bias issue. The results revealed that farming experience, knowledge, environmental awareness, soil fertility, and irrigation machinery ownership significantly influenced adoption decisions. The treatment effect model result indicated that farmers who adopted this method could minimize irrigation costs by 1.88 to 2.22%, obtain 4.48 to 8.16% higher ROI, and reduce total production cost by 0.06 to 0.98% compared to non-adopters. Our findings suggested that policy interventions targeting scaling up SIFs should consider focusing on government and stakeholders’ greater attention on designing more appropriate schemes through experimentation and multiple iterations.

Abstract Background Social distancing is one of the community mitigation measures that may be recommended during influenza pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as schools or workplaces. We conducted a systematic review to assess the evidence that social distancing in non-healthcare workplaces reduces or slows influenza transmission. Methods Electronic searches were conducted using MEDLINE, Embase, Scopus, Cochrane Library, PsycINFO, CINAHL, NIOSHTIC-2, and EconLit to identify studies published in English from January 1, 2000, through May 3, 2017. Data extraction was done by two reviewers independently. A narrative synthesis was performed. Results Fifteen studies, representing 12 modeling and three epidemiological, met the eligibility criteria. The epidemiological studies showed that social distancing was associated with a reduction in influenza-like illness and seroconversion to 2009 influenza A (H1N1). However, the overall risk of bias in the epidemiological studies was serious. The modeling studies estimated that workplace social distancing measures alone produced a median reduction of 23% in the cumulative influenza attack rate in the general population. It also delayed and reduced the peak influenza attack rate. The reduction in the cumulative attack rate was more pronounced when workplace social distancing was combined with other nonpharmaceutical or pharmaceutical interventions. However, the effectiveness was estimated to decline with higher basic reproduction number values, delayed triggering of workplace social distancing, or lower compliance. Conclusions Modeling studies support social distancing in non-healthcare workplaces, but there is a paucity of well-designed epidemiological studies. Systematic review registration number PROSPERO registration # CRD42017065310.

Abstract Background During an evolving influenza pandemic, community mitigation strategies, such as social distancing, can slow down virus transmission in schools and surrounding communities. To date, research on school practices to promote social distancing in primary and secondary schools has focused on prolonged school closure, with little attention paid to the identification and feasibility of other more sustainable interventions. To develop a list and typology of school practices that have been proposed and/or implemented in an influenza pandemic and to uncover any barriers identified, lessons learned from their use, and documented impacts. Methods We conducted a review of the peer-reviewed and grey literature on social distancing interventions in schools other than school closure. We also collected state government guidance documents directed to local education agencies or schools to assess state policies regarding social distancing. We collected standardized information from each document using an abstraction form and generated descriptive statistics on common plan elements. Results The document review revealed limited literature on school practices to promote social distancing, as well as limited incorporation of school practices to promote social distancing into state government guidance documents. Among the 38 states that had guidance documents that met inclusion criteria, fewer than half (42%) mentioned a single school practice to promote social distancing, and none provided any substantive detail about the policies or practices needed to enact them. The most frequently identified school practices were cancelling or postponing after-school activities, canceling classes or activities with a high rate of mixing/contact that occur within the school day, and reducing mixing during transport. Conclusion Little information is available to schools to develop policies and procedures on social distancing. Additional research and guidance are needed to assess the feasibility and effectiveness of school practices to promote social distancing.