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This research work is devoted to the experimental investigation of both rheological and mechanical properties of self-compacting concrete (SCC) produced with waste galvanized copper wire fiber and rice husk ash (RHA). In the study, three different volume fractions of 0.5 p to 0.75 percent, 1 percent of scrap copper wire fiber as reinforcing material, and 2 percent RHA as cement replacement were used. To evaluate the fresh characteristics of SCC, the slump flow, J-ring, and V-funnel experiments were conducted for this investigation. Compressive strength, splitting tensile strength, and flexural strength of the concrete were conducted to assess the hardened properties. The test was carried out to compare each characteristic of plain SCC with this modified SCC mixture, containing RHA as pozzolanic materials and copper fiber as reinforcing material. Incorporating copper fiber in the SCC leads to a drop in fresh properties compared to plain SCC but remains within an acceptable range. On the other hand, the inclusion of 2% RHA makes the SCC more viscous. Although adding 2% RHA and 1% copper wire in SCC provide the highest strength, this mix has an unacceptable passing ability. The SCC mix prepared with 2% RHA and 0.75% copper fiber is suggested to be optimum in terms of the overall performance. According to this study, adding metallic fiber reinforcement like copper wire and mineral admixture like RHA can improve the mechanical properties of SCC up to a certain level.

The incorporation of waste materials generated in many industries has been actively advocated for in the construction industry, since they have the capacity to lessen the pollution on dumpsites, mitigate environmental resource consumption, and establish a sustainable environment. This research has been conducted to determine the influence of different rice husk ash (RHA) concentrations on the fresh and mechanical properties of high-strength concrete. RHA was employed to partially replace the cement at 5%, 10%, 15%, and 20% by weight. Fresh properties, such as slump, compacting factor, density, and surface absorption, were determined. In contrast, its mechanical properties, such as compressive strength, splitting tensile strength and flexural strength, were assessed after 7, 28, and 60 days. In addition, the microstructural evaluation, initial surface absorption test, = environmental impact, and cost-benefit analysis were evaluated. The results show that the incorporation of RHA reduces the workability of fresh mixes, while enhancing their compressive, splitting, and flexural strength up to 7.16%, 7.03%, and 3.82%, respectively. Moreover, incorporating 10% of RHA provides the highest compressive strength, splitting tensile, and flexural strength, with an improved initial surface absorption and microstructural evaluation and greater eco-strength efficiencies. Finally, a relatively lower CO -eq (equivalent to kg CO ) per MPa for RHA concrete indicates the significant positive impact due to the reduced Global Warming Potential (GWP). Thus, the current findings demonstrated that RHA can be used in the concrete industry as a possible revenue source for developing sustainable concretes with high performance.

In 2023, Bangladesh became the first country to achieve World Health Organization (WHO) validation of elimination of visceral leishmaniasis as a public health problem, defined as maintenance of annual kala-azar incidence at <1 case per 10,000 population at the subdistrict (upazila) level. The pillars of the programme are early diagnosis and effective treatment, indoor residual insecticide spraying, improved case detection, social mobilization and operational research, and effective disease surveillance. The Bangladesh National Kala-azar Elimination Programme was established in 2008, with introduction of rapid diagnostics and newer treatment modalities in health complexes at sub-district level in the endemic area in 2012–2015, initiation of blanket IRS in affected communities in 2012–2013 and adoption of a digital surveillance system in 2015. All subdistricts achieved and maintained the elimination threshold from 2017 onward. We present documentation of the course of KA elimination in Bangladesh and provide a perspective on the components necessary to maintain current success into the future.

PurposeApplication of appropriate shading device strategies in buildings can reduce direct solar heat gain through windows as well as optimize cooling and artificial lighting load. This study investigates the impact of common shading devices such as overhangs, fins, horizontal blinds, vertical blinds and drapes on energy consumption of an office building and suggests energy efficient shading device strategies in the contexts of unique Bangladeshi subtropical monsoon climate.Design/methodology/approachThis research was performed through the energy simulation perspective of a prototype office building using a validated building energy simulation tool eQUEST. Around 100 simulation patterns were created considering various types of shading devices and building orientations. The simulation results were analysed comprehensively to find out energy-efficient shading device strategies.FindingsOptimum overhang and fin height is equal to half of the window height in the context of the subtropical climate of Bangladesh. South and West are the most vulnerable orientations, and application of shading devices on these two orientations shows the highest reduction of cooling load and the lowest increment of lighting load. An existing building was able to save approximately 7.05% annual energy consumption by applying the shading device strategies that were suggested by this study.Originality/valueThe shading device strategies of this study can be incorporated into the Bangladesh National Building Code (BNBC) as new energy-efficient building design strategies because the BNBC does not have any codes or regulations regarding energy-efficient shading device. It can also be used as energy-efficient shading device strategies to other Southeast Asian countries with similar climatic contexts of Bangladesh.

Background and Aims Early childhood development (ECD) plays a vital role in shaping a child's health and well‐being, influenced by child, family, and environmental factors. To prevent long‐term impairments, early detection and intervention are crucial. Using MICS 2019 data, this study applies supervised machine learning to predict ECD across four key domains and identify the most significant predictors and economic strategies. Methods In this study, using data of 9346 children obtained from Multiple Indicator Cluster Surveys (MICS) 2019, we evaluated and compared five classifiers: CART, Random Forest, XGBoost, Logistic Regression, and Support Vector Machines (SVM). We have addressed four early developmental domains as our target variables: literacy, numeracy, physics, learning, and social‐emotional development of children. Five‐fold cross‐validation was used to ensure appropriate test error rate estimations and reduce bias. To handle the data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is used. Results The analysis shows that most children are developing normally in the learning (90.58%) and physical (98.70%) domains, while delays are highest in literacy‐numeracy (71.37%) and social‐emotional (27.57%) domains. Among the machine learning models evaluated, Random Forest consistently performed best across all domains, achieving the highest accuracy, particularly in learning (0.83) and physical (0.97) domains. Feature importance analysis identified maternal education, child age, regional location (Division), and socioeconomic status (Wealth Index) as key predictors. Early childhood education and books read at home also play important roles in cognitive and learning outcomes, guiding targeted interventions for child development. Conclusions The results show notable differences in early childhood development, particularly in social‐emotional and literacy‐numeracy domains. Socioeconomic status, early learning experiences, and parental education are key predictors, while physical and social‐emotional development are influenced by resources, regional factors, and nutrition. These findings can guide targeted interventions and policies for holistic child development.

PurposeConstruction delay is the most common issue and creates many adverse effects in any construction industry. This study has investigated the views of engineers, project managers and contractors on the causes of delay during a construction phase to identify potential delay factors, negative effects on project delivery and prioritize the delay factors.Design/methodology/approachAn extensive literature review and interview with construction stakeholders have been conducted to identify potential causes of construction delays and design a questionnaire survey. The final questionnaire was designed with 40 potential delay factors, and a total of 102 valid Bangladeshi construction stakeholders responded to it. The result was analyzed by the relative importance index.FindingsAmong the 40 delay factors, the top five most influencing delay factors are “delay in progress payments,” “rework due to mistakes during construction,” “lack of skilled labor,” “poor monitoring and control of activities” and “delays in the making of a decision.” The top five most damaging effects of delay are “time overrun,” “cost overrun,” “disputes,” “arbitration” and “litigation,” among ten negative effects of construction delay. All construction stakeholders believe that the owner-related, consultant-related and contractor-related groups are the first, second and third most important groups of delay factors.Originality/valueThe outcome of this study would enable the Bangladeshi construction industry to develop strategies to overcome delay factors and their harmful effects. By focusing on the outcome of this research and prioritizing the critical factors, the construction industry of Bangladesh will be able to minimize construction delay significantly and propagate the progress of the construction industry by delivering quality projects timely.

In the automotive, electronics, and energy sectors, heat exchangers and cooling systems can be improved by the flow dynamics of thermally enriched nanomaterials across enlarged surfaces. In biomicrosystems, bionanocooling units, and the petroleum sector, the fluid’s stabilizing properties derive from the presence of microorganisms. Using nonlinear radiation, Arrhenius catalysts, and thermal convection, this work focuses on the electrically induced nanobioconvective migration of Maxwell fluid‐containing organisms across an exponentially grown surface. Using suitable transformations, the governing equations are converted into dimensionless forms to create a mathematical framework for nanofluids based on the Buongiorno model. After a stability analysis, the reformulated flow problems are solved using a finite difference approach, and the computational outcomes are verified against previous research. The results show that larger buoyancy ratios and magnetic components decrease the velocity of the nanofluid, while increasing viscosity and radiation increase its thermal energy. Furthermore, unpredictable motion and enhanced thermophoresis reduce heat transmission. Compared to linear radiation, nonlinear thermal radiation has a far bigger effect on thermal fields. Additionally, fewer dispersed bacteria result in a lower motile microbe density when the Peclet number ( Pe ) is larger. When it comes to estimating microbe density ( Nd ), the model’s statistical validation yields a 99.95% R 2 at a 95% confidence level, indicating its dependability. Pe and Ae show the highest and lowest sensitivity to the motile density function in Maxwell fluid, respectively. By improving the circumstances for microbial growth in nanofluid settings, the findings of this study aid in the optimization of biofuel production.

The consequences of magnetohydrodynamic mixed convection in a trapezoidal heat exchanger are investigated through numerical analysis. Due to the extensive applications of both mono and hybrid nanofluids in manufacturing and thermal engineering, the Ag-MgO-H2O hybrid nanofluid is selected as the working material for the entire domain. Additionally, a horizontal magnetic field is applied to the cavity. The finite element method is involved to solve the corresponding mathematical equations. The physical implications of the results are examined over a range of Reynolds numbers (10 ≤ Re ≤ 200), Hartmann numbers (0 ≤ Ha ≤ 100), and nanoparticle volume fractions (0 ≤ ϕ ≤ 0.08) using streamlines, isotherms, and line graphs. The impact of key factors on the response function is illustrated using the response surface methodology with 2D and 3D visualizations. Sensitivity rates are analysed by developing a best-fit correlation. It is concluded that the thermal enhancement of the hybrid nanofluid is achieved up to 11.4% by incorporating hybrid nanoparticles, and due to the upsurge of the Reynolds number. Conversely, the influence of the magnetic field leads to a decline in this rate to 10.02%. The use of Ag-MgO-H2O hybrid nanofluid improves the heat transfer efficiency of water by 6.62%. Finally, the results of this study may offer valuable insights for designing an efficient mixed convective mechanical device