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Abstract This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀ × 3.19 λ₀, the antenna offers a bandwidth of 5.1 GHz, a peak gain of 9.43 dBi, high isolation of 31.37 dB, and an efficiency of 99.6%. Simulations conducted in CST Studio were validated through prototype measurements, showing strong agreement between the measured and simulated results. To further validate the design, an equivalent RLC circuit model was developed and analyzed using ADS, with the reflection coefficient results closely matching those from CST. Additionally, supervised ML techniques were employed to predict the antenna’s gain, evaluating nine models using metrics such as R-squared, variance score, mean absolute error, and root mean squared error. Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. This integration of machine learning with antenna design underscores its potential to optimize performance and enhance design efficiency. With its compact size, high isolation, and exceptional efficiency, the proposed antenna is a promising candidate for 28 GHz 5G applications, offering innovative solutions for next-generation wireless communication.

Abstract This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. This research evaluates an antenna’s performance using various methodologies, such as simulation and RLC equivalent circuit models. The suggested design has a broad bandwidth of 2.5 THz and spans from 6.2 to 8.7 GHz, a maximum gain of 14.59 dB, and small dimensions (100 × 300) µm2. It also has outstanding isolation exceeding − 31 dB with 96% efficiency. The ADS allowed us to confirm the accuracy of the CST results by creating a simulated version of the same RLC circuit. Reflection coefficients obtained from the CST and ADS simulators are similar. The supervised regression ML approach is employed accurately to predict the antenna’s potential gain. Several metrics, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE), can evaluate machine learning (ML) models. Out of the six machine learning models analyzed, the Extra Tree Regression model demonstrates the lowest error and achieves the highest level of accuracy in predicting gain.

This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies.

Clinopyroxene is a subgroup of pyroxene that is found in a variety of igneous and metamorphic rocks. This study investigates the structural, mechanical, electronic, optical, and thermal characteristics of CaTGe2O6 (T = Mn, Fe, Co) using density functional theory. These structures' lattice parameters have been optimized using GGA-PBE, GGA-PBESOL, and LDA exchange-correlation functionals, where all these structures are found to be stable in monoclinic symmetry having a little variation with experimental results. All the structures are mechanically stable and ductile in nature. The CaFeGe2O6 has the highest melting point and Debye temperature among the three structures. The electronic band diagram and spin-polarized PDOS of these structures confirm the half-metallic nature of all three structures. The half-metallic band gaps of CaTGe2O6 (T = Mn, Fe, Co) are 3.05, 1.69, 1.99 eV in GGA + U and 2.88, 1.52, 1.78 eV in the LDA + U method, respectively. CaMnGe2O6 is metallic in the spin-up state, whereas both CaFeGe2O6 and CaMnGe2O6 are metallic in the spin-down state. Refractive indices and dielectric functions of these structures ensure the transparency of CaTGe2O6 (T = Mn, Fe, Co) clinopyroxenes around 28.5 eV photon energy. These structures possess the highest reflectivity and absorption coefficients in the UV region. These salient features of these structures suggest that the spintronics and optoelectronics industries may benefit from these clinopyroxene structures in the future.

Miniaturized Millimeter Wave (mm-wave) MIMO antenna arrays with an observed 10-dB impedance broad bandwidth of 3.7 GHz (25.785–29.485) are the focus of this study's design and analysis for a 5G application. Rogers RT/duroid 5880, a low-loss dielectric material, is utilized in the antenna's fabrication. For MIMO antenna design down to the lowest frequency, the substrate and ground must have dimensions of 3.3λ0 × 3.3λ0. Besides compact, the suggested design has a supreme gain of 8.9 dB, isolation greater than 29.24, and a maximum efficiency rating of 98.4 %. A Diversity Gain (DG) has a value that is greater than 9.99, whereas an Envelope Correlation Coefficient ECC) has a value that is less than 0.00005. The effectiveness of machine learning (ML) models can be estimated using a variety of different metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). Out of the five ML models, the one that has the greatest accuracy and has a low margin of error when predicting directivity is the Random Forest Regression model. In conclusion, the data from the CST and ADS modeling as well as the actual and expected outcomes from machine learning demonstrate that the recommended antenna is a potential candidate for use with 5G.

This article presents unique research on the application of machine learning techniques to enhance the efficiency of antennas for wireless communication and Internet of Things (IoT) applications in the Terahertz (THz) frequency band. This work utilizes Computer Simulation Technology (CST) Microwave Studio modelling techniques considering the compact dimensions of 120 × 200 μm2 and a polyimide substrate. The design attains a peak gain of 12.116 dB, isolation exceeding 36 dB, and an efficiency of 88.86 %, covering a broad frequency range of 2.6 THz (7.2438–9.84 THz). The outcomes from the CST were verified by designing and simulating a similar RLC circuit in ADS. Both CST and advanced design system (ADS) simulators produced comparable reflection coefficients. The supervised regression machine learning technique accurately predicted the antenna's isolation. The performance of machine learning (ML) models can be assessed using criteria such as variance score, R squared, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). Gradient Boosting Regression demonstrated the smallest error and highest accuracy among the six ML models tested. The isolation prediction accuracy exceeds 94 %, as indicated by the R-squared and variance scores. The proposed antenna utilizing simulations, multiple regression machine learning models, and an equivalent Resistance-Inductance-Capacitance (RLC) circuit model are strong contenders for THz band applications.

Tourists nowadays are more dependent on online reviews to make different travel decisions. Electronic word of mouth has been playing a key role in influencing the purchase intention of tourists who travel through an online travel agency. In this context, the study aims to explore how electronic word-of-mouth determinants influence travellers' intention to purchase tourism services from online travel agencies in Bangladesh. Based on Social Exchange Theory information quantity, information quality, information credibility, and information usefulness of electronic word of mouth are identified as the four factors determining the purchase intentions toward buying services through an online travel agency. A total of 302 people participated and replied to the questionnaire, making up the sample. For the analysis, SPSS version 25 integrated with AMOS version 26 was employed. Structural equation modeling was used to test the hypothesis. The analysis revealed that, except for information quantity, the three other independent variables that determine e-WOM - information quality, information credibility, and information usefulness significantly affect tourists' service purchasing intentions from OTAs. The current research findings provide meaningful, practical implications for online travel service providers, online service agents, and managers of different tourism platforms to develop and implement strategies to manage online reviews across different online platforms to generate better responses from travelers.

The selection of Ph.D (Doctor of Philosophy) supervisor is always a vital and interesting problem in academia and especially for students who want to carry out Ph.D. Nowadays, selecting a supervisor for Ph.D in a scientific manner becomes a challenge for any student because of the variety of options available to the scholar. In this context, the present study aims to formulate a model for Ph.D. supervisor selection from the offered alternatives in an academic institute. A hybrid multi-criteria decision making (MCDM) framework has been applied to select the suitable supervisor of the student’s preferred criteria under interval-valued intuitionistic fuzzy (IVIF) scenario. The IVIF Analytic Hierarchy Process (AHP) has been employed to prioritize the criteria, whereas IVIF Technique for order preference by similarity to ideal solution (TOPSIS) technique is engaged to rank the available supervisors based on criteria weight. A set of eight criteria and five alternatives have been considered for modeling the problem. Moreover, the potential criteria are weighted and ranked by the multiple decision makers in the present study. To examine the consistency and robustness of the proposed integrated approach, sensitivity analysis and comparative analysis have been carried out. From all the analyses, it can be conferred that the suggested approach is quite useful to apply in different decision-making scenarios.

The advancement of the Mellin transformation is that it exhibits a scale-invariant nature, and thus, it is widely used in computer science. Fuzzy Mellin transformation can provide the purposes of such problems in uncertain phenomena. The existing studies on fuzzy Mellin transformation consider the fuzzy level cut approach, which makes the fuzzy valued functions into their crisp couplets regarding lower and uppercuts. This paper reconstructs a distinct mathematical frame for the Mellin transformation in a completely fuzzy environment in the sense of a modified Hukuhara derivative. In this paper, we mean the complete fuzzy environment to describe fuzzy phenomena where the conversion of the fuzzy function to its crisp counterpart is annulled. The proposed fuzzy Mellin transformation’s superiority over existing approaches is that it can be used directly to fuzzy valued functions without converting them to their crisp counter version. The challenges to dealing with fuzzy valued mappings and inputs without its level cut representation have been tackled in this manuscript. Furthermore, numerical examples, possible applications, and further extensions of this work are hinted at in this paper.

Japanese encephalitis (JE) is associated with an immense social and economic burden. Published cost-of-illness data come primarily from decades-old studies. To determine the cost of care for patients with acute JE and initial and long-term sequelae from the societal perspective, we recruited patients with laboratory-confirmed JE from the past 10 years of JE surveillance in Bangladesh and categorized them as acute care, initial sequalae, and long-term sequelae patients. Among 157 patients, we categorized 55 as acute, 65 as initial sequelae (53 as both categories), and 90 as long-term sequelae. The average (median) societal cost of an acute JE episode was US $929 ($909), of initial sequelae US $75 ($33), and of long-term sequelae US $47 ($14). Most families perceived the effect of JE on their well-being to be extreme and had sustained debt for JE expenses. Our data about the high cost of JE can be used by decision makers in Bangladesh.

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Abstract Background Tuberculosis (TB) has been an important public health concern in Bangladesh. The most common cause of human TB is Mycobacterium tuberculosis, while bovine TB is caused by Mycobacterium bovis. Objective The objective of this study was to determine the frequency of TB in individuals with occupational exposure to cattle and to detect Mycobacterium bovis among cattle in slaughterhouses in Bangladesh. Methods Between August 2014 and September 2015, an observational study was conducted in two government chest disease hospitals, one cattle market, and two slaughterhouses. [Correction added on 27 June 2023, after first online publication: In the preceding sentence, the year “2014” has been added after the word “August”.] Sputum samples were collected from individuals who met the criteria for suspected TB and had been exposed to cattle. Tissue samples were collected from cattle that had low body condition score(s). Both humans and cattle samples were screened for acid‐fast bacilli (AFB) by Ziehl–Neelsen (Z‐N) staining and cultured for Mycobacterium tuberculosis complex (MTC). Region of difference (RD) 9‐based polymerase chain reaction (PCR) was also performed to identify Mycobacterium spp. We also conducted Spoligotyping to identify the specific strain of Mycobacterium spp. Results Sputum was collected from a total of 412 humans. The median age of human participants was 35 (IQR: 25–50) years. Twenty‐five (6%) human sputum specimens were positive for AFB, and 44 (11%) were positive for MTC by subsequent culture. All (N = 44) culture‐positive isolates were confirmed as Mycobacterium tuberculosis by RD9 PCR. Besides, 10% of cattle workers were infected with Mycobacterium tuberculosis in the cattle market. Of all TB (caused by Mycobacterium tuberculosis) infected individuals, 6.8% of individuals were resistant to one or two anti‐TB drugs. The majority of the sampled cattle (67%) were indigenous breeds. No Mycobacterium bovis was detected in cattle. Conclusions We did not detect any TB cases caused by Mycobacterium bovis in humans during the study. However, we detected TB cases caused by Mycobacterium tuberculosis in all humans, including cattle market workers.

Fuzzy set theory is a generalized form of crisp set theory where elements are binary inclusion forms. In fuzzy set, it differs with degree of membership for every element in the set. There are several strategies for arithmetic operations on fuzzy numbers. Previous studies show that there are many approaches, such as the α−cut technique, extension principle, vertex method, etc., to execute arithmetic operations on fuzzy numbers. In this study we perform details analysis and interpretation on arithmetic operations based on the α−cut method in a new way.

The coexistence of metals and ceramics properties has made the MAX phase materials different from other three-dimensional materials. This study investigates several physical properties such as the structural, mechanical, and optoelectronic features of the pressure-induced Zr2AN (A = In, Ga) MAX phase via the density functional theory (DFT) insight. The lattice parameter c is greater than a (=b) as it is layered along the z direction. The electronic ground state energy and the applied pressure are inversely related. The elastic stiffness constants of the hexagonal crystal system have a positive and linear correlation with increasing pressure. The band structure reveals that both structures are electrically conductive at 0–50 GPa pressure range. The PDOS reveals the contribution of the Zr-4d orbital at the Fermi level. The optical parameters in the visible range point to possible optoelectronics and various technological applications.

Malaria is a mosquito-borne infectious disease caused by the Plasmodium parasite. It is prevalent in many regions of the globe, particularly in sub-Saharan Africa, where it is a major cause of illness and death. Mathematical models can aid in understanding the dynamics of malaria transmission and guiding disease-control interventions. The authors discuss a mathematical model and focus on severely infected people of malaria transmission in both crisp and fuzzy environments in this article. A parameter pertaining to the cases where relapse of the disease has occurred is considered. This study discusses the stability of the model in both crisp and fuzzy environments and also considered numerical illustrations to validate the said model.

The multi-criteria decision-making (MCDM) method often entails a number of steps, such as identifying the decision problem, defining the criteria, calculating the weighting of the criteria, assessing the alternatives against the criteria, and finally choosing the best alternative based on the overall evaluation. In this paper, the essential factor for sports, politics, journalism or media, and technological empowerment of women in India are found by MCDM techniques, which may also be utilized to examine the key factors influencing women’s empowerment. The uses the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) MCDM methodologies are taken to discover the optimal criteria for empowerment. The decision-making for finding the factors is done by taking multi experts’ opinions. First, determined the best criteria among the six criterion’s, such as Family Support (C1), Financial Support (C2), Cultural and Religious Barrier (C3), Child Marriage (C4), Nutrition (C5), and Shame and concealment (C6). The weights are calculated by the AHP method. According to the importance of the criteria, select a significant alternative for empowering women in many fields. Also take six possibilities into consideration, which are Empowerment in Sports (A1), Empowerment in Politics (A2), Empowerment in Administrative Service (A3), Empowerment of Journalism or Media (A4), Empowerment for Social Work (A5)and Empowerment of Technological (A6)and they are ranked by TOPSIS method. To verify the validity and robustness of our result, sensitivity analysis is performed by removing some criteria to make sure the procedure is reliable and a comparative study using a fuzzy environment.

Purpose – This study aims to explain the effect of entrepreneurial orientation (EO) dimensions on firms’ export performance. The study has considered three dimensions of EO: innovativeness, proactiveness and risk-taking. Export performance has been measured through multifaceted determinants: financial, strategic and satisfaction levels of exporting firms. Design/methodology/approach – To explain the association between such determinants, qualitative face-to-face, in-depth interviews were conducted with the respondents from the apparel manufacturing companies of Bangladesh. The study has also applied a content analysis technique to explore and triangulate the descriptions of EO dimensions on performance. Findings – The research results reveal that EO has a meaningful explanation of export performance from the lived experience of the respondents. The study has also found frequent explanations and indicators about the association of innovativeness and proactiveness on export performance, but the risk-taking dimension has revealed no effects. Research limitations/implications – The research has different implications on theoretical, managerial and policy-making aspects of EO and export performance. Nevertheless, the findings are based on the opinions of a small sample of entrepreneurs and high-level managers. Thus, it is important to test the generalizability of these findings in future research using a larger sample. Originality/value – This study indicated product, process and market creation innovativeness. Risks related to product innovation, new market entry and politics are considered for risk-taking measures, while proactive innovation, technology and new market searching are used for measuring proactiveness. Additionally, multifaceted performance determinants have been applied to evaluate export performance. Hence, this study is a unique qualitative analysis with important theoretical, practical and methodological implications.

Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number (GDHHχFN) is used to diagnose disease. We also introduce a new de-fuzzification method for GDHHχFN. To diagnose a given condition, GDHHχFN coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies.

Abstract Background The burden of maternal undernutrition and low birth weight (LBW) incurs enormous economic costs due to their adverse consequences. Women’s empowerment is believed to be one of the key factors for attaining maternal and child health and nutritional goals. Our objective was to investigate the association of women’s empowerment with maternal undernutrition and LBW. Methods We used nationally representative data from the Bangladesh Demographic Health Survey for 2011 and 2014. We analysed 27357 women and 9234 mother-child pairs. A women’s empowerment index (WEI) was constructed using principal component analysis with five groups of indicators: a) education, b) access to socio-familial decision making, c) economic contribution and access to economic decision making, d) attitudes towards domestic violence and e) mobility. We estimated odds ratios as the measure of association between the WEI and the outcome measures using generalized estimating equations to account for the cluster level correlation. Results The overall prevalence of maternal undernutrition was 20% and LBW was 18%. The WEI was significantly associated with both maternal undernutrition and LBW with a dose-response relationship. The adjusted odds of having a LBW baby was 32% [AOR (95% CI): 0.68 (0.57, 0.82)] lower in the highest quartile of the WEI relative to the lowest quartile. Household wealth significantly modified the effect of the WEI on maternal nutrition; in the highest wealth quintile, the odds of maternal undernutrition was 54% [AOR (95% CI): 0.46 (0.33, 0.64)] lower while in the lowest wealth quintile the odds of undernutrition was only 18% [AOR (95% CI): 0.82 (0.67, 1.00)] lower comparing the highest WEI quartile with the lowest WEI quartile. However, the absolute differences in prevalence of undernutrition between the highest and lowest WEI quartiles were similar across wealth quintiles (6–8%). Conclusions This study used a comprehensive measure of women’s empowerment and provides strong evidence that low levels of women’s empowerment are associated with maternal undernutrition as well as with delivering LBW babies in Bangladesh. Therefore, policies to increase empowerment of women would contribute to improved public health.

Site selection for an institute or a university is a challenging task. The selection of sites for setting up a new university depends on multiple criteria. In backward, under privileged area people’s perception towards the co-educational universities and women universities are different. Poor families with their conservative mentality possess inhibitions while sending their girl child to co-educational universities as they have concerns about safety, security and family honor. Hence many attributes which are not so important for co-educational universities are more pertinent for women university. In this research paper, we have considered a model for selecting women’s university sites in different backward locations in the state of West Bengal, India. This model incorporated different types of uncertainty related to site selection. Ten important criteria are chosen for the selection of sites. To capture the uncertainty of the problem, trapezoidal neutrosophic numbers are used along with the Multi-criteria Decision Making tool Analytic Hierarchy Process (AHP) for obtaining criteria weights. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are applied for ranking of the sites. Comparative and sensitivity analyses are conducted to check the steadiness of the techniques used.

The aim of this study is to investigate the impact of entrepreneurial orientation (EO) on differentiation strategy. In this study, the components of EO are innovativeness, proactiveness and risk-taking. On the other hand, differentiation strategy indicates the product, process, market and brand differentiation. The study uses survey questionnaire for data collection. The data are collected from Muslim entrepreneurs of the apparel industry from Bangladesh. The study uses 339 data to conduct the research. After data collection, SmartPLS is applied for quantitative analysis to examine the effect of EO on differentiation strategy. The study indicates a positive and significant effect of innovativeness, proactiveness and risk-taking (EO) on differentiation strategy. Among the three components of EO, proactiveness is found the most significant component on differentiation strategy followed by innovativeness and risk-taking.

ObjectiveThis study examines trends and puts forward projections of under-5 mortality (U5M) in Bangladesh and identifies the effects of maternal high-risk fertility behaviours and use of healthcare services.MethodsData from seven waves of the Bangladesh Demographic and Health Survey (1994-2014) were analyzed for trends and projections of U5M and a Chi-square (χ2) test was used to identify if there was any association with maternal high-risk fertility behaviours and use of healthcare services. A multivariate logistic regression model was used to determine the effects of fertility behaviors and healthcare usage on the occurrence of U5M adjusting with confounders.ResultsU5M declined from 82.5 to 41.0 per 1000 livebirths during 1994-2014 and is projected to further reduce to 17.6 per 1000 livebirths by 2030. The study identified a noticeable regional variation in U5M with maternal high-risk fertility behaviours including age at birth ConclusionThe Sustainable Development Goals require a U5M rate of 25 per 1000 livebirths to be achieved by 2030. This study suggests that with the current trend of reduction, Bangladesh will achieve this target before the deadline. This study also found that maternal high-risk fertility behaviours and non-use of maternal healthcare services are very prevalent in some regions of Bangladesh and have increased the occurrence of U5M in those areas. This suggests therefore, that policies and programmes designed to reduce the pregnancy rates of women that are at risk and to encourage an increase in the use of maternal healthcare services are needed.

Abstract Background Inappropriate dispensing of antibiotics for acute respiratory illness (ARI) is common among drug sellers in Bangladesh. In this study, we evaluated the impact of an educational intervention to promote guidelines for better ARI management among drug sellers. Methods From June 2012 to December 2013, we conducted baseline and post-intervention surveys on dispensing practices in 100 pharmacies within Dhaka city. In these surveys, drug sellers participated in 6 standardized role-playing scenarios led by study staffs acting as caregivers of ARI patients and drug sellers were blinded to these surveys. After the baseline survey, we developed ARI guidelines and facilitated a one-day educational intervention about ARI management for drug sellers. Our guidelines only recommended antibiotics for children with complicated ARI. Finally, we conducted the six month post-intervention survey using the same scenarios to record changes in drug dispensing practices. Results Only 2/3 of participating pharmacies were licensed and few (11%) of drug sellers had pharmacy training. All the drug sellers were male, had a median age of 34 years (IQR 28–41). For children, dispensing of antibiotics for uncomplicated ARI decreased (30% baseline vs. 21% post-intervention; p = 0.04), but drug sellers were equally likely to dispense antibiotics for complicated ARI (15% baseline vs. 17% post-intervention; p = 0.6) and referrals to physicians for complicated ARIs decreased (70% baseline vs. 58% post-intervention; p = 0.03). For adults, antibiotic dispensing remained similar for uncomplicated ARI (48% baseline vs. 40% post-intervention; p = 0.1) but increased among those with complicated ARI (44% baseline vs. 78% post-intervention; p

Preventing emergence of new zoonotic viruses depends on understanding determinants for human risk. Nipah virus (NiV) is a lethal zoonotic pathogen that has spilled over from bats into human populations, with limited person-to-person transmission. We examined ecologic and human behavioral drivers of geographic variation for risk of NiV infection in Bangladesh. We visited 60 villages during 2011–2013 where cases of infection with NiV were identified and 147 control villages. We compared case villages with control villages for most likely drivers for risk of infection, including number of bats, persons, and date palm sap trees, and human date palm sap consumption behavior. Case villages were similar to control villages in many ways, including number of bats, persons, and date palm sap trees, but had a higher proportion of households in which someone drank sap. Reducing human consumption of sap could reduce virus transmission and risk for emergence of a more highly transmissible NiV strain.

With limited infection control practices in overcrowded Bangladeshi hospitals, surfaces may play an important role in the transmission of respiratory pathogens in hospital wards and pose a serious risk of infection for patients, health care workers, caregivers and visitors. In this study, we aimed to identify if surfaces near hospitalized patients with respiratory infections were contaminated with respiratory pathogens and to identify which surfaces were most commonly contaminated. Between September-November 2013, we collected respiratory (nasopharyngeal and oropharyngeal) swabs from patients hospitalized with respiratory illness in adult medicine and paediatric medicine wards at two public tertiary care hospitals in Bangladesh. We collected surface swabs from up to five surfaces near each case-patient including: the wall, bed rail, bed sheet, clinical file, and multipurpose towel used for care giving purposes. We tested swabs using real-time multiplex PCR for 19 viral and 12 bacterial pathogens. Case-patients with at least one pathogen detected had corresponding surface swabs tested for those same pathogens. Of 104 patients tested, 79 had a laboratory-confirmed respiratory pathogen. Of the 287 swabs collected from surfaces near these patients, 133 (46%) had evidence of contamination with at least one pathogen. The most commonly contaminated surfaces were the bed sheet and the towel. Sixty-two percent of patients with a laboratory-confirmed respiratory pathgen (49/79) had detectable viral or bacterial nucleic acid on at least one surface. Klebsiella pneumoniae was the most frequently detected pathogen on both respiratory swabs (32%, 33/104) and on surfaces near patients positive for this organism (97%, 32/33). Surfaces near patients hospitalized with respiratory infections were frequently contaminated by pathogens, with Klebsiella pneumoniae being most common, highlighting the potential for transmission of respiratory pathogens via surfaces. Efforts to introduce routine cleaning in wards may be a feasible strategy to improve infection control, given that severe space constraints prohibit cohorting patients with respiratory illness.

Asphalt binder is a very complex chemical compound. Much work has been done to understand and model its chemical, morphological, rheological, and mechanical features. This paper synthesizes and presents findings from pertinent studies available in the public domain. Understanding of asphalt characteristics at the very finite level is the first critical step to develop a better macroscopic- or pavement-level performance model. This paper showcases a summary of current knowledge gained on (a) how chemical elemental compositions and molecular groups play critical roles in asphalt binder’s performance in pavement composites; (b) morphological properties and their relationships with the asphalt’s structural performance; and (c) mechanistic characteristics of asphalt binder’s at nanoscopic, mesoscopic, and microscopic levels, and how they are related to macroscopic- or pavement-level performance.

With the advancement of exascale computing, the amount of scientific data is increasing day by day. Efficient data access is necessary for scientific discoveries. Unfortunately, the I/O performance is not improved, like the CPU and network speed. So, I/O operations take longer time than data generation or analysis. Asynchronous I/O has been proposed to extenuate the I/O bottleneck by overlapping I/O and computation time. However, multiple small write operations can diminish the benefits of asynchronous I/O, as the I/O time becomes significantly longer than the compute time, with little time to overlap with. To overcome these issues, we present an optimization technique to merge small contiguous write operations. We integrated our solution into the HDF5 asynchronous I/O VOL connector and demonstrated the effectiveness of merging HDF5 write operations automatically and transparently without requiring any code change from the application.

BACKGROUND:We combined hospital-based surveillance and health utilization survey data to estimate the incidence of respiratory viral infections associated hospitalization among children aged < 5 years in Bangladesh. METHODS:Surveillance physicians collected respiratory specimens from children aged

Pneumonia is the leading cause of childhood death in Bangladesh. We conducted a longitudinal study to estimate the incidence of virus-associated pneumonia in children aged