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Precise prediction of adsorption in a multicomponent system is vital for successful design of dye-contaminated industrial wastewater treatment processes. The present work looks for the reason behind the failure of the competitive Langmuir model (CLM) to describe adsorption in such systems, while the Langmuir model (LM) successfully describes the process for a single dye solution. With that end, derivations of LM and CLM have been revisited, and a criterion for the universality of active sites has been defined. Attempts have been made to describe the competitive adsorption based on the LM parameters determined from single dye solutions. Three dyes (safranin, crystal violet, and methylene blue) and two adsorbents [activated carbon (AC) and coconut fiber (CF)] are subjected to adsorption and BET tests. AC complies with the universality criterion, while CF does not. The active site density estimated from dye adsorption is 14–18 folds lower for AC and 18–38 folds higher for CF, than those done from the BET test. The CLM predicts the equilibrium adsorption density of both adsorbents in binary dye systems satisfactorily, but there is a large gap between the predicted and experimental values of equilibrium concentration. It is argued that in liquid systems, adsorption occurs through different types of interactions, and hence the adsorption process even for a single dye should not be presented by a single KLvalue (as is formulated in LM), rather with multiple KLvalues corresponding to the interaction types. These KLvalues will classify the active sites as universal and specific in a multicomponent system, and then the CLM will predict competitive adsorption better, which will make the adsorption process design more precise.

Polyethylenimine (PEI) is a widely used cationic polymer for nonviral gene delivery, often modified to enhance transfection efficiency and reduce cytotoxicity. This study investigates how acetylation, succinylation (acPEI and zPEI), and pH influence the internal DNA packaging of polyplexes. Both modifications alter physicochemical properties, leading to complexes that decondense more readily with increasing modification. X-ray scattering reveals that high acetylation produces loosely packed DNA, while succinylation unexpectedly tightens DNA packing at higher modification levels. Polyplexes formed at low pH (pH 4) are more stable and tightly packed than those formed at pH 7.5. Acidifying polyplexes initially formed at pH 7.5 induces structural rearrangement to tighter DNA packing accompanied by significant PEI release, providing direct evidence for models where free PEI aids endosomal escape. These findings challenge conventional assumptions about PEI behavior and offer new insights into DNA packaging, emphasizing tailored polymer modifications and pH conditions to optimize gene delivery.

Skin cancer is a potentially life-threatening disease resulting from DNA damage, where early detection is critical to improving survival rates. This study introduces an advanced hybrid deep learning framework designed to enhance the accuracy of skin cancer classification, distinguishing between benign and malignant lesions. Our approach begins with data pre-processing to improve input quality, followed by training two high-performing pre-trained deep learning models, InceptionV3 and DenseNet121. We then apply a weighted sum rule for fusion of the model predictions, leading to high accuracy and generalizability across datasets. On the primary dataset, our hybrid model achieved 92.27% accuracy, 90.80% precision, 92.33% sensitivity, 92.22% specificity, and a 91.57% F1 score. Notably, the model also demonstrated robustness on an external dataset, achieving 93.45% accuracy, 91.87% precision, 93.04% sensitivity, 93.18% specificity, and a 92.30% F1 score, outperforming existing models. This study offers a reliable, highly generalizable solution for automated skin cancer diagnosis and represents a valuable contribution to early detection methods, with the potential to improve patient outcomes significantly.

•JAK2-mutant clonal hematopoiesis is associated with a strong risk of VTE.•JAK2-mutant CHIP confers a greater risk of VTE than heterozygous thrombophilia but is present at a lower frequency in the general population.

Sustainable infrastructural development is vital for both developed and developing countries. The primary concern in hot mix asphalt (HMA) pavements is related to damages caused by moisture. This research makes efforts to evaluate the moisture susceptibility of a dense-graded surface HMA mixture using simple performance tests (SPTs) and Superpave indirect tensile tests (IDTs). Coating of fines (dust) on the aggregate can prevent the asphalt binder from bonding directly to the surface of the aggregate. Therefore, SPT and IDT have been performed by varying the proportion of fines (3%, 6%, and 9%) in the mix. For each proportion of fines, the optimum asphalt content was determined and used for further preparation of test specimens. The addition of hydrated lime [Ca(OH)2] to asphalt mixtures improved the adhesive bond between aggregate and bitumen, as well as reduced the occurrence of stripping. In addition, when aggregates coated with clays, hydrated lime with a pozzolanic ally to remove those deleterious materials. The amount of antistripping agent incorporated was 1.5% by weight of dry aggregate, a proportion commonly used in the industry for such applications. Standard Marshall Mix design procedure was employed to design the asphalt mixtures. The laboratory investigation and subsequent statistical analysis were conducted using Minitab-15 software, which yielded significant insights into the effectiveness of hydrated lime as an antistripping additive in asphalt concrete mixtures. The findings indicate that incorporating hydrated lime substantially facilitates mitigating moisture-induced stripping in asphalt mixtures.

3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior; however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get optimized mixture. The ML models were trained and tested using a comprehensive database on CS collected from literature considering the various fiber-reinforced cementitious composites, comprising over 299 mixtures with 11 features. The results show that the trained ML models could predict CS with R2ranging from 0.927 to 0.990 and 0.914 to 0.988 for the training and testing dataset, respectively. Furthermore, supplementary experiments were conducted to create a new dataset to validate the predictive model's accuracy, with the extreme gradient boosting (XGB) and gene expression programming (GEP). Based on the GEP, a novel empirical equation was proposed and rigorously validated using experiments. The equation exhibits a high accuracy with the GEP algorithm (R2= 0.89), providing real-world field applications that might be improve decision-making and mixture optimization, which contributes to advancements, efficiency, and innovative solutions in 3D printing practical domains.

Zintl phases have potential applications as thermoelectric materials for power generation and cooling owing to their complex crystal structures and unique electronic properties. We carried out reactions of silicon with barium and strontium in excess Mg/Zn flux to synthesize (Ba/Sr)5+xMg19-xSi12Zintl phases, investigating the effect of varying Ba/Sr ratio on site mixing and thermoelectric properties. (Ba/Sr)5+xMg19-xSi12compounds with 0 < x< 3 are charge-balanced Zintl phases which adopt the hexagonal Ho5Ni19P12structure type (space group P6̅2m). Density of states calculations indicate that these materials are semimetals. Single-crystal X-ray diffraction data and elemental analysis for Ba5Mg19Si12, Ba4.86Sr2.94Mg16.20Si12, Ba3.63Sr4.20Mg16.17Si12, Ba1.93Sr5.99Mg16.08Si12, and Sr7.82Mg16.18Si12show occupation of barium and strontium cations in Ho sites, while strontium mixes with magnesium on a specific Ni site. Powder XRD data of products show that they are single phase throughout the sample. Thermoelectric measurements indicate that increasing strontium content and mixing on three cation sites decreases thermal conductivity; it is hypothesized that improved overall thermoelectric behavior is likely due to the rattling of the Sr cations in their positions.

Bangladesh, prone to recurrent natural disasters, continually grapples with the repercussions of these events, affecting millions of its citizens each year. Amid the most vulnerable regions in the country, the riverine islands of Sirajgonj, Bangladesh, stand as particularly fragile areas, characterized by limited healthcare access. This study is dedicated to examining the intricate web of healthcare challenges prevailing in these disaster-prone islands. A semi-structured interview methodology was meticulously employed, with a total of 24 in-depth interviews conducted. Thematic analysis, adopting both deductive and inductive approaches, was rigorously applied to dissect the data. The Granheim method, in conjunction with Nvivo-14 software, facilitated seamless data processing, rigorous analysis, and meticulous coding. The findings highlight critical healthcare challenges in Sirajgonj’s disaster-prone riverine islands: residents face knowledge gaps about available healthcare resources, geographical and weather-related barriers hinder transportation, especially during disasters. Moreover, inadequate medical responses and patient distrust underscore the need for healthcare resilience. Socio-economic status affects healthcare access, perpetuating disparities. Shortages of medical support and resources further exacerbate challenges, necessitating urgent targeted interventions. This study provides crucial insights into the barriers to healthcare access in the region, offering valuable guidance for policymakers and healthcare providers. It also suggests that the government should adopt a more efficient approach to integrate the elderly into social safety net programs.

Developing a hybrid phenomenological model for predicting the sound absorption coefficient of a pineapple leaf fibre/waste tea leaf fibre/glass fibre/epoxy-based natural fibre-reinforced hybrid composites is the predominant topic of this article. Phenomenological models excel at extrapolating characteristic impedance and wave number whereas empirical models require fewer inputs but overlook wave propagation in pores. Existing models apply only to single-fibre-reinforced composites, necessitating the creation of a hybrid model for hybrid composites. The developed hybrid Zwikker–Kosten and Johnson–Champoux–Allard model shows good agreement with experimental data across the frequency range, with standard deviations of 0.001–0.029 and percent deviations of 1.11%–11.43%. The overall noise reduction coefficient between the model and experiments is 0.31 vs. 0.30, with a 3.33% deviation. Furthermore, the application of alkali treatment increased the surface roughness which in turn, enhanced the sound absorption capabilities of these composites. The increased fibre roughness also amplified friction between fibres and sound waves, resulting in higher sound absorption coefficients. In addition, X-ray diffraction, thermal stability (thermogravimetric analysis and differential scanning calorimetry), and scanning electron microscopy examinations were performed on the designated composition (5% by weight of pineapple leaf fibre and 25% by weight of waste tea leaf fibre) of the pineapple leaf fibre/waste tea leaf fibre/glass fibre/epoxy-based natural fibre-reinforced hybrid composite.

Transdermal microneedles have demonstrated promising potential as an alternative to typical drug administration routes for the treatment of various diseases. As microneedles offer lower administration burden with enhanced patient adherence and reduced ecological footprint, there is a need for further exploitation of microneedle devices. One of the main objectives of this work was to initially develop an innovative biobased photocurable resin with high biobased carbon content comprising isobornyl acrylate (IBA) and pentaerythritol tetraacrylate blends (50:50 wt/wt). The optimization of the printing and curing process resulted in μNe3dle arrays with durable mechanical properties and piercing capacity. Another objective of the work was to employ the 3D printed hollow μNe3dles for the treatment of osteoporosis in vivo. The 3D printed μNe3dle arrays were used to administer denosumab (Dmab), a monoclonal antibody, to osteoporotic mice, and the serum concentrations of critical bone minerals were monitored for six months to assess recovery. It was found that the Dmab administered by the 3D printed μNe3dles showed fast in vitro rates and induced an enhanced therapeutic effect in restoring bone-related minerals compared to subcutaneous injections. The findings of this study introduce a novel green approach with a low ecological footprint for 3D printing of biobased μNe3dles, which can be tailored to improve clinical outcomes and patient compliance for chronic diseases.

AbstractThe ORF3a is a large accessory protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which plays an important role in virulence and viral replication; especially in inflammasome activation and apoptosis. However,, the existing cryo-EM structure of SARS-CoV-2 ORF3a is incomplete, . making it challenging to understand its structural and functional features. The aim of this study is to investigate the dynamic behaviors of the full-sequence homology model of ORF3a and compare it with the cryo-EM structure using microsecond molecular dynamics simulations. The previous studies indicated that the unresolved residues of the cryo-EM structure are not only involved in the pathogenesis of the SARS-CoV-2 but also exhibit a significant antigenicity. The dynamics scenario of homology model revealed higher RMSD, Rg, and SASA values with stable pattern when compared to the cryo-EM structure. Moreover, the RMSF analysis demonstrated higher fluctuations at specific positions (1-43, 97-110, 172-180, 219-243) in the model structure, whereas the cryo-EM structure displayed lower overall drift (except 1-43) in comparison to the model structure.Secondary structural features indicated that a significant unfolding in the transmembrane domains and β-strand at positions 166 to 172, affecting the stability and compactness of the cryo-EM structure , whereas the model exhibited noticeable unfolding in transmembrane domains and small-coiled regions in the N-terminal. , The results from molecular docking and steered molecular dynamics investigations showed the model structure had a greater number of non-bonding interactions, leading to enhanced stability when compared to the cryo-EM structure. Consequently, higher forces were necessary for unbinding of the baricitinib and ruxolitinib inhibitors from the model structure.. Our findings can help better understanding of the significance of unresolved residues at the molecular level. Additionally, this information can guide researchers for experimental endeavors aimed at completing the full-sequence structure of the ORF3a.Communicated by Ramaswamy H. Sarma

ABSTRACTIntroductionGuanine nucleotide exchange factors (GEFs) regulate the activation of small GTPases (G proteins) of the Ras superfamily proteins controlling cellular functions. Ras superfamily proteins act as ‘molecular switches’ that are turned ‘ON’ by guanine exchange. There are five major groups of Ras family GTPases: Ras, Ran, Rho, Rab and Arf, with a variety of different GEFs regulating their GTP loading. GEFs have been implicated in various diseases including cancer. This makes GEFs attractive targets to modulate signaling networks controlled by small GTPases.Areas coveredIn this review, the roles and mechanisms of GEFs in malignancy are outlined. The mechanism of guanine exchange activity by GEFs on a small GTPase is illustrated. Then, some examples of GEFs that are significant in cancer are presented with a discussion on recent progress in therapeutic targeting efforts using a variety of approaches.Expert opinionRecently, GEFs have emerged as potential therapeutic targets for novel cancer drug development. Targeting small GTPases is challenging; thus, targeting their activation by GEFs is a promising strategy. Most GEF-targeted drugs are still in preclinical development. A deeper biological understanding of the underlying mechanisms of GEF activity and utilizing advanced technology are necessary to enhance drug discovery for GEFs in cancer.

Graphical abstract

The COVID-19 pandemic brought about a significant transformation in the way education is delivered, with e-learning becoming the primary mode of instruction worldwide. In the Chittagong Hill Tracts region of Bangladesh, this shift posed unique challenges for indigenous rural communities. The present study endeavours to investigate, explore, and diagnose the e-learning experience of the indigenous rural community residing in the Chittagong Hill Tracts region of Bangladesh. The research employs a qualitative approach, and three specific areas from the aforementioned region were purposively selected for data collection. In order to accomplish the research objectives, a total of 60 semi-structured, in-depth interviews and participant observations were conducted. Data analysis used both a deductive method through thematic analysis and a hybrid integrated data-driven inductive approach. The Granheim method and Nvivo-12 software were used for data analysis, processing, and coding. The result revealed that the experiences of e-learning among indigenous students of Chittagong Hill Tracts have been suffering from technical illiteracy, insufficient equipment, and devices, poor network coverage, inattention in online classes, feeling isolation, moral degradation, and online addiction. Consequently, the majority of the indigenous students had unfavourable experiences with online teaching and learning techniques. Based on the results, the study recommends strategies to provide better access to e-learning resources, improve internet connectivity, provide access to devices, and enhance digital literacy for students in remote rural areas. Furthermore, the findings of the study will be crucial for academics, policymakers, stakeholders, and development practitioners in developing a stronger e-learning policy and practice for all, particularly for the indigenous rural community in Chittagong Hill Tracts.

At charged aqueous interfaces, the second-order nonlinear optical response originates from water molecules within the diffuse part of the electrical double layer, which are ordered by the surface field and from water that additionally experiences chemical and physical interactions with the surface in the Stern layer. These two environments can either reinforce or diminish the overall signal and can be disentangled by varying the coherence length of their interaction with external laser fields. Here, we demonstrate a method in which the angle of incidence is varied to afford a significant change in the coherence length. When this technique was applied to the silica–water interface, it was observed that water molecules in the Stern and diffuse layers direct their hydrogen atoms toward the mineral surface at a low ionic strength and neutral pH. A decrease in the signal with increasing ionic strength is attributed to hydrated cation adsorption that competes with free water for deprotonated silanol sites.

Crystals of a new ternary intermetallic compound were grown from reactions of manganese with zinc in a bismuth flux. Mn8Zn1.8Bi7.4forms as silver needle-like crystals that have a hexagonal (P63/mmc) structure related to the poorly understood polymorphs of the strong ferromagnet MnBi. The structure features disorder involving the replacement of a fraction of bismuth on a specific site with Zn3triangular units. Mn8Zn1.8Bi7.4has a hexagonal network of manganese atoms that undergoes ferromagnetic ordering at 185 K. A copper-containing analogue Mn8Cu0.58Bi7.7can also be synthesized in low yield.

Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and can result in severe complications such as blindness, kidney failure, and stroke. Early detection of diabetes can potentially save millions of lives globally, making it a crucial focus of research. In this study, we propose a machine learning model to aid in predicting diabetes. The model comprises several machine learning methods: Linear Regression (LnR), Logistic Regression (LR), k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Prior to feeding the pre-processed data into the machine learning model for evaluation, we conducted several pre-processing steps, such as removing null values, standardizing data using normalization, and labeling data using the label encoding process. Imbalanced datasets can adversely affect the accuracy of machine learning algorithms, and we address this problem by balancing the datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. We assessed the model’s performance on two datasets and found that the random forest algorithm produced optimal results, with 97% accuracy on the diabetes dataset 2019 and 80% accuracy on the Pima Indian dataset. However, using a balanced dataset, we can significantly reduce the number of false-negative detections.

The objectives of this study were to compare dengue virus (DENV) cases, deaths, case-fatality ratio [CFR], and meteorological parameters between the first and the recent decades of this century (2000–2010 vs. 2011–2022) and to describe the trends, seasonality, and impact of change of temperature and rainfall patterns on transmission dynamics of dengue in Bangladesh. For the period 2000–2022, dengue cases and death data from Bangladesh’s Ministry of Health and Family Welfare’s website, and meteorological data from the Bangladesh Meteorological Department were analyzed. A Poisson regression model was performed to identify the impact of meteorological parameters on the monthly dengue cases. A forecast of dengue cases was performed using an autoregressive integrated moving average model. Over the past 23 yr, a total of 244,246 dengue cases were reported including 849 deaths (CFR = 0.35%). The mean annual number of dengue cases increased 8 times during the second decade, with 2,216 cases during 2000–2010 vs. 18,321 cases during 2011–2022. The mean annual number of deaths doubled (21 vs. 46), but the overall CFR has decreased by one-third (0.69% vs. 0.23%). Concurrently, the annual mean temperature increased by 0.49 °C, and rainfall decreased by 314 mm with altered precipitation seasonality. Monthly mean temperature (Incidence risk ratio [IRR]: 1.26), first-lagged rainfall (IRR: 1.08), and second-lagged rainfall (IRR: 1.17) were significantly associated with monthly dengue cases. The increased local temperature and changes in rainfall seasonality might have contributed to the increased dengue cases in Bangladesh.

Hypertension increases risk of stroke and other cardiovascular diseases, however, its prevalence and determinants in South Asian urban communities using country representative community-based datasets is lacking. This study evaluated prevalence of hypertension and it’s determinants among urban residents of three South Asian countries. Urban population data from demographic and health surveys in Bangladesh, India, and Nepal were extracted. Hypertension prevalence was defined as systolic/diastolic blood pressure ≥140/ 90 mmHg. Age, education, wealth, physical activity, alcohol, BMI were considered as risk factors associated with the increased risk of hypertension. We performed binary logistic regression and calculated adjusted Odds Ratios (AOR) with 95% confidence interval (CI) to assess factors related to hypertension. Hypertension prevalence was 37.4% in India, 25.1% in Bangladesh and 18.4% in Nepal. Prevalence increased with age in all settings. Females had reduced odds of hypertension in Bangladesh (AOR 0.75; CI: 0.69, 0.81) and Nepal (AOR 0.62; CI: 0.54, 0.71), but higher risk in India (AOR 2.54; CI: 2.45, 2.63). Low education, caffeine consumption, obesity was associated with higher prevalence of hypertension in all three countries. Smokers had increased odds of hypertension in India (AOR 1.11; CI: 1.06, 1.15) and Nepal (AOR 1.23; 1.02, 1.47). Overall, hypertension prevalence is high in all three countries. Modifiable socioeconomic and lifestyle factors (education, wealth index, smoking status, caffeine consumption and BMI) associated with hypertension. Comprehensive hypertension pacific and sensitive interventions (including behavioral modification treatments and timely screening and access to health care) are urgently needed to prevent and control hypertension among urban populations in South Asia.

•In PWH, CD4 nadir associates with clonal hematopoiesis, a risk factor of atherosclerosis and cancer.•Clonal hematopoiesis is less common outside of North America among PWH.

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID.

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

Pavement condition rating (PCR) is a popular indicator for prioritizing maintenance of flexible pavements. Flexible pavements are vulnerable because of inappropriate geometric design, reduced maintenance, and climate hazards. This study investigates the condition of the flexible pavements in the arterial roads of Dhaka city. Currently, no database exists for the pavement condition of the roads of Dhaka. This study is undertaken to rank roads based on pavement condition ratings and to recommend maintenance alternatives. The pavement evaluation is done manually through visual inspection of recorded images of the distress of the selected road sections. Eighteen representative road sections from four zones of Dhaka were examined. In 2019, the length and frequency of pavement cracks, drainage deficiencies, traffic volume, and driving discomfort were measured. The PCR values for the examined roads ranged from 58 to 92 on a scale of 0–100. Riding quality was related to edge cracking, block cracking, drainage inadequacy, and Annual Average Daily Traffic (AADT) in a correlation test of the connected variables. Based on the PCR value and kind of cracks, general and crack-specific maintenance suggestions for the selected roadways and associated costs were presented. Potholes and Alligator cracks were more noticeable than patching, raveling, and shoving. More than half of the road sections needed regular maintenance, while others required overlay. Interestingly, no pavement needed reconstruction as the maintenance option. Some suggestions to modify existing pavement maintenance practices, such as increasing curing time, scraping older pavements, and using cost-effective treatment solutions, were recommended. This condition rating can be used to repair pavement distresses and ensure proper pavement maintenance across Dhaka city.

Frequent road intersections in Dhaka–Chittagong National Highway (NH1), the major transport corridor of Bangladesh, significantly reduce the level of service of the corridor and eventually leads to inefficient fuel economy and excessive greenhouse gas (GHG) emission. In spite of upgrading NH1 into a four-lane highway, major road intersections reduce vehicle speed and increase congestion time and eventually burn fuel. Fuel expenses during this lost time cover no distance but increase vehicular emission within the vicinity of road and contribute to roadside temperature. Besides, the transport sector’s energy demand in Bangladesh is supported mostly by imported fuel that drains out foreign currency and inhibits GDP growth. Against the backdrop, the Government of Bangladesh is proposing to construct a four-lane expressway. The paper attempts to estimate the fuel loss savings, GHG emission reduction and economic benefit of constructing Dhaka–Chittagong Expressway. As the construction of the expressway paved a way for an increment of traffic growth by 10%, the study infers that the average lost time because of 36 intersections for a projected annual average daily traffic of 27,334 vehicles/day (in 2022). In addition to that, the fuel loss savings for various vehicle classes affect economic growth and the ensuing idling emission of EFI and MFI engines contributes to transport sector pollution. The study intends to expedite the fact that Dhaka–Chittagong Expressway would not only replace road interventions that reduce travel time cost, expenditures regarding vehicle operating and accident but also contributes cardinally to economic emancipation of the country. The estimated Benefit–Cost Ratio (BCR) was 1.23, net present value was 762.34 Million USD) and Economic Internal Rate of return was 18.27% of the proposed project.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by the formation of intracellular neurofibrillary tangles (NFTs) and extracellular amyloid plaques. Growing evidence has suggested that AD pathogenesis is not only limited to the neuronal compartment but also strongly interacts with immunological processes in the brain. On the other hand, aggregated and misfolded proteins can bind with pattern recognition receptors located on astroglia and microglia and can, in turn, induce an innate immune response, characterized by the release of inflammatory mediators, ultimately playing a role in both the severity and the progression of the disease. It has been reported by genome-wide analysis that several genes which elevate the risk for sporadic AD encode for factors controlling the inflammatory response and glial clearance of misfolded proteins. Obesity and systemic inflammation are examples of external factors which may interfere with the immunological mechanisms of the brain and can induce disease progression. In this review, we discussed the mechanisms and essential role of inflammatory signaling pathways in AD pathogenesis. Indeed, interfering with immune processes and modulation of risk factors may lead to future therapeutic or preventive AD approaches.

The research investigates the characteristics of lightweight concrete by incorporating class C fly ash (FA) and corn stalk ash (CSA) as partial substitutes for cement, along with lightweight pumice aggregates replacing coarse aggregates. The massive energy consumption in the cement manufacturing process is posing a threat to the environment. Furthermore, using waste to dispose of and re-cover natural concrete components might harm the environment. This investigation includes the study of workability, water absorption, compressive strength, and unit weight test. In this experimental study, 0%, 5%, 10%, and 20% by weight of cement have been replaced with FA and CSA, and coarse aggregates are replaced in different proportions, including 0%, 5%, 10% and 20% by pumice aggregate. In total, 15 mix proportions were prepared and used for compressive strength, water absorption and other parameter each. Based on the results obtained, the optimal solution for optimum strength (54.5 MPa) is obtained by replacing 5% cement with FA and CSA each and coarse aggregate with pumice aggregate. Furthermore, workability increases with increased fly ash and corn stalk ash content. Water absorption also decreases with an increase in corn stalk ash as well as in fly ash. XRD patterns with significant peaks demonstrate that most fly ash and corn stalk ash structures are amorphous. XRF is employed to determine the elemental composition of FA and CSA.

Extended shelf life of fruits is of prime importance to ensure their availability in the market and facilitate longer-distance transportation. In this case, effective pre-harvest treatments are crucial for managing the climacteric nature of mangoes by reducing post-harvest decay during storage. So, implementing pre-harvest spray of nutrients and bagging is vital to reduce deterioration and improve the quality of mango cv. ‘Amrapali’ in storage. A study assessed various interventions, including bagging, 2% CaCl2, 1.5% K2SO4, and 2% CaCl2+ 1.5% K2SO4+ bagging, in preserving postharvest quality and retarding fruit softening of mangoes at ambient storage (25 ± 3 ºC and 80–85% RH). Several physicochemical attributes and disease incidence at 0, 4, 8, 12, and 16 days of storage were analyzed. The findings indicated that 2% CaCl2+ 1.5% K2SO4+ bagging treatment appeared superior compared to control in terms of lower weight loss (5.10–10.84, 6.83–17.34%), pH (2.83–5.15, 3.00–5.48), decay incidence (0–9.87, 1.47–41.37%), ethylene production (0.21–0.84, 0.36–1.92 µl kg−1h−1), respiration rate (2.92–27.03, 4.48–52.76 ml CO2kg−1h−1) while higher in fruit firmness (4.92–2.01, 4.41–1.23 kg cm−2), total soluble solids-TSS (11.67–17.60, 11.33–15.66º Brix), ascorbic acid content (39.86–11.26, 35.26–5.56 mg 100 g−1), total sugar (7.96–16.07, 3.82–14.52 mg 100 g−1), carotenoid (173.53–664.33, 67.00–567.00 µg 100 g−1), total phenol (163.53–113.58, 143.61–51.72 mg 100 g−1), DPPH scavenging activity (431.29–305.53, 308.65–228.49 µmol g−1). 2% CaCl2+ 1.5% K2SO4+ bagging treatment also showed better peel and pulp colour of mango fruits with greater values of L* (brightness), a* (redness), as well as b* (yellowness) compared to control. Notably, fruits that were treated with 2% CaCl2+ 1.5% K2SO4+ bagging had the lowest polyphenol oxidase (PPO), while control showed reduced activity of catalase (CAT) and peroxidase (POD). These findings suggest that 2% CaCl2+ 1.5% K2SO4+ bagging might be a successful approach to improving the quality and postharvest physiology of mangoes cv. Amrapali at ambient storage.

The impact of epigenetic modifications like DNA methylation on plant phenotypes has expanded the possibilities for crop development. DNA methylation plays a part in the regulation of both the chromatin structure and gene expression, and the enzyme involved, DNA methyltransferase, executes the methylation process within the plant genome. By regulating crucial biological pathways, epigenetic changes actively contribute to the creation of the phenotype. Therefore, epigenome editing may assist in overcoming some of the drawbacks of genome editing, which can have minor off-target consequences and merely facilitate the loss of a gene’s function. These drawbacks include gene knockout, which can have such off-target effects. This review provides examples of several molecular characteristics of DNA methylation, as well as some plant physiological processes that are impacted by these epigenetic changes in the plants. We also discuss how DNA alterations might be used to improve crops and meet the demands of sustainable and environmentally-friendly farming.

Seismic intensity prediction from early or initial seismic waves received by a few seismic stations can enhance earthquake early warning systems (EEWSs), particularly in ground motion-based approaches like propagation of local undamped motion (PLUM). While many operational EEWSs currently utilize point-source-based models that estimate the warning area based on magnitude and distance measures, direct intensity prediction offers a potential improvement in accuracy and reliability. In this article, we propose a novel deep learning (DL) approach, seismic contrastive graph neural network (SC-GNN), for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms from a few seismic stations. The SC-GNN consists of two key components: 1) a GNN to propagate spatiotemporal information through a graph-like structure representing seismic station distribution and wave propagation and 2) a self-supervised contrastive learning component to train the network with larger time windows and enable predictions using shorter initial waveforms. The efficacy of our approach is demonstrated through experiments on three real-world seismic datasets, where it shows superior performance over existing techniques, including a significant reduction in mean-squared error (mse) and the lowest standard deviation (SD) of error, indicating its robustness, reliability, and strong positive relationship between predicted and actual values. Notably, the SC-GNN model maintains superior performance even with 5-s input waveforms, making it especially suitable for enhancing EEW applications.

Flexible perovskite solar cells have attracted substantial attention owing to their promises for soft and high power–weight compatibility. However, the inferior quality of the buried perovskite–substrate interface due to low interfacial adhesion and large deformation of flexible substrates have greatly limited the performance of flexible perovskite solar cells. Here we add the organic molecule entinostat into the hole extraction material poly(bis(4-phenyl)(2,4,6-trimethylphenyl)amine) to enhance adhesion at the perovskite–substrate interface using the interaction of entinostat with perovskites, poly(bis(4-phenyl)(2,4,6-trimethylphenyl)amine) and indium tin oxide through its multiple functional groups. In addition, entinostat reduces the density of voids at the bottom of the perovskite film owing to its capability to tune the crystallization of perovskites. We demonstrate inverted small-area flexible perovskite solar cells with a power conversion efficiency of 23.4%. Flexible perovskite minimodules with an area of 9 cm2achieve a certified aperture efficiency of ~19.0%. The optimized unencapsulated flexible minimodule retains 84% of its initial efficiency after 5,000 bending cycles and 90% of the initial power conversion efficiency after light soaking for >750 h.

In plants and algae, the primary antenna protein bound to photosystem II is light-harvesting complex II (LHCII), a pigment–protein complex that binds eight chlorophyll (Chl) amolecules and six Chl bmolecules. Chl aand Chl bdiffer only in that Chl ahas a methyl group (–CH3) on one of its pyrrole rings, while Chl bhas a formyl group (–CHO) at that position. This blue-shifts the Chl babsorbance relative to Chl a. It is not known how the protein selectively binds the right Chl type at each site. Knowing the selection criteria would allow the design of light-harvesting complexes that bind different Chl types, modifying an organism to utilize the light of different wavelengths. The difference in the binding affinity of Chl aand Chl bin pea and spinach LHCII was calculated using multiconformation continuum electrostatics and free energy perturbation. Both methods have identified some Chl sites where the bound Chl type (aor b) has a significantly higher affinity, especially when the protein provides a hydrogen bond for the Chl bformyl group. However, the Chl asites often have little calculated preference for one Chl type, so they are predicted to bind a mixture of Chl aand b. The electron density of the spinach LHCII was reanalyzed, which, however, confirmed that there is negligible Chl bin the Chl a-binding sites. It is suggested that the protein chooses the correct Chl type during folding, segregating the preferred Chl to the correct binding site.

This study uses neoclassical labour theory to examine the association between migration and the time-spending behaviour of left behind family members by gender and the female relationship with migrated members. Using two-year panel data, this study controls the individual-level time-invariant factors to correct potential self-selection. The study finds that migration of a family member is associated with decreasing off-farm labour of both working-age females and males. Additionally, it is observed that the leisure time of working-age females increases while their work burden on domestic labour decreases. Migration also increases off-farm labour of left-behind wives when only the husbands migrate and decreases off-farm and domestic labour, and increases the leisure of left-behind mothers when only sons/daughters migrate. Thus, migration reshapes the left behind family members’ time allocation between labour and leisure with a heterogeneous impact on the wives and mothers.

Despite the promising performance of automated pain assessment methods, current methods suffer from performance generalization due to the lack of relatively large, diverse, and annotated pain datasets. Further, the majority of current methods do not allow responsible interaction between the model and user, and do not take different internal and external factors into consideration during the model's design and development. This article aims to provide an efficient cooperative learning framework for the lack of annotated data while facilitating responsible user communication and taking individual differences into consideration during the development of pain assessment models. Our results using body and muscle movement data, collected from wearable devices, demonstrate that the proposed framework is effective in leveraging both the human and the machine to efficiently learn and predict pain.

Minor cereals are becoming increasingly important for global food security because of their high nutritional value and their tolerance to environmental stresses. In adverse agrosystems, these crops are often more promising than the major cereals. Considering the climate change effect on a number of major cereals, minor cereals are becoming popular in Asian countries. In Bangladesh, minor cereals have been cultivated on a small-scale for a long time, but limited information is available on their growth, yield, and the possibility of expanding the cultivated area. This study evaluated the status and potential of minor cereals using secondary data, GIS mapping, and structured survey questionnaires. Currently, minor cereals are grown on around 4813 ha of land. Foxtail millet (Setaria italica) alone accounts for 63.0% of the area cultivated, followed by proso millet (Panicum miliaceum) 28.2%, barley (Hordeum vulgare) 7.5%, pearl millet (Pennisetum glaucum) 0.6%, and oats (Avena sativa) 0.6%. The current yield of minor cereals is low, around 2.0 t ha−1. The crops are mostly grown on marginal lands formed by river siltation, locally called charlands. Minor cereal farmers take advantage of the low production costs, including inputs, relatively simple cultural practices, and minimal risks of crop loss from natural hazards to grow minor cereals. Mainly low yield levels and lack of high-yielding varieties and production technologies limit large-scale cultivation of minor cereals. Strengthening research to develop high-yielding, climate-resilient varieties of minor cereals and improved agronomic management methods and raising farmers’ awareness about their nutritional value and economic benefits will be needed to ensure their sustainable production and extension.

Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.

Three border provinces in southern Thailand commonly known as the Deep South have a long history of conflicts and insurgency. These chronic conflicts in the Deep South may forcibly hinder educational attainment and its effectiveness in the aggregate economy. This study aimed to analyze the comparative effects of education on the aggregate economy between the Deep South (a region with conflict) and other provinces of the South (a region of harmony) in Thailand. The Thai Labor Force Survey from 1995 to 2015, a large-scale national survey conducted by the National Statistical Office, and data from the Office of National Economic and Social Development Council (NESDC) of Thailand were used for the analysis. Employing a Random Effect Model and pooled regression, this study revealed that if average schooling increases by one percent, overall economic output will increase by 2.62%. However, the effects of educational attainment are significantly lower in the Deep South economy compared to other southern provinces. Therefore, this study provides an analysis of the comparative effects of schooling on the economy between areas of conflict and harmony because this issue has not been properly addressed in the existing literature.

BARI Alu-86 (12.13) is a potato variety with medium maturity (92.2 days) and rapid tuber bulking which makes it fit for early and full-season harvest. The Tuber Crops Research Centre of the Bangladesh Agricultural Research Institute released it in 2019 as a result of a cross between BARI Alu-50 and CIP 389429.31. In early season harvest, this variety produced an average of 29.0 t/ha potato, which was 30.0% greater than the popular early bulking check variety Granola. This variety yielded 45.7 t/ha tuber yield during full-season harvest, which was 36.8 and 39.7% higher than widely used table purpose check cultivars Diamant and Asterix, respectively. The BARI Alu-86 variety is famed for its delicious qualities as well as its visually appealing tubers with red skin, long oval shape, shallow eyes, and cream-coloured flesh. Average combined sensory ratings (16.1 out of 19) were very similar to Lady Rosetta. It had an average dry matter content of 18%, specific gravity of 1.072, and starch content of 20.9 mg/g. BARI Alu-86 also showed some promise as an export variety, as it produced smooth skin large tubers (63.9% > 40 mm diameter) with an average weight of 120–250 g per tuber. This variety had minimal weight and rotting losses, as well as a nice tuber, look after 90 days of storage. It also had long dormancy periods (80–85 days). This variety has no cases of hollow heart, cracking, PVX, early blight, late blight, stem rot, stem canker, black leg, or bacterial wilt. However, a lower percentage of PLRV, PVY, common scab and cutworm infestation occurred.

Bangladesh reported the highest number of annual deaths (n= 281) related to dengue virus infection in 2022 since the virus reappeared in the country in 2000. Earlier studies showed that >92% of the annual cases occurred between the months of August and September. The 2022 outbreak is characterized by late onset of dengue cases with unusually higher deaths in colder months, that is, October–December. Here we present possible hypotheses and explanations for this late resurgence of dengue cases. First, in 2022, the rainfall started late in the season. Compared to the monthly average rainfall for September and October between 2003 and 2021, there was 137 mm of additional monthly rainfall recorded in September and October 2022. Furthermore, the year 2022 was relatively warmer with a 0.71°C increased temperature than the mean annual temperature of the past 20 yr. Second, a new dengue virus serotype, DENV-4, had recently reintroduced/reappeared in 2022 and become the dominant serotype in the country for a large naïve population. Third, the post-pandemic return of normalcy after 2 yr of nonpharmaceutical social measures facilitates extra mosquito breeding habitats, especially in construction sites. Community engagement and regular monitoring and destruction of Aedes mosquitoes’ habitats should be prioritized to control dengue virus outbreaks in Bangladesh.

Titanium dioxide (TiO2) is a wide bandgap semiconductor that has a wide range of applications including wastewater treatment, photocatalysis, solar cells, and sensors owing to its excellent electronic and optical properties. However, the wide bandgap of TiO2(∼3.2 eV) along with the recombination processes of the photoexcited charge carriers reduce its interfacial charge transport properties and respective activities. Energetics, optical response, and corresponding photophysics of excited charge carriers can be altered through doping and codoping of TiO2and composite formulation.Therefore, it is very crucial to understand the structure–property and property-application relationships of doped, co-doped, and composite TiO2to maximize the efficiency and extend the fundamental understanding for their applications in different fields. In this Perspective, we summarize the ongoing research on the advancement of undoped, doped, co-doped, and composite TiO2including their structure and morphology, energetics, and corresponding photophysics relative to their applications in photocatalysis, wastewater treatment, and water-splitting reactions. We then propose our perspectives on the future potential of energetic manipulation of photocatalytic TiO2to develop advanced and highly efficacious catalytic materials.

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Despite the promise of climate-smart agriculture (CSA) to improve food security in South Asia, most CSA practices and technologies have not been widely adopted. We identify the key barriers to CSA adoption in South Asia and suggest strategies to overcome them to increase CSA adoption at scale.

ABSTRACT Externally bonded carbon fiber reinforced polymer (CFRP) sheet has shown excellent performance to repair, restore and increase the load-carrying capacity of RC structures. In this paper results of an [...]

AbstractAlthough firm growth is an old issue, very limited number of studies have found on the issues of small firm growth. Studies in many countries have focused on some specific growth factors and no comprehensive research on this issue is available to draw a conclusion. Based on the concept of the theory of ‘Resource Based View (RBV)’, current study motivated to see how the resources like finance and financial literacy of owner-manager affect financial growth of small firms and the role government support plays in that relationship. Data was collected through questionnaire from 407 owner-managers of small firms. Using partial least squares, the study found that both finance and financial literacy has strong positive relation with small firm financial growth. Astoundingly, the study uncovered that government support does not moderate the relationships between finance, financial literacy and small firm financial growth in the expected way, which the study argues to be a result of the seemingly weakness of the government support distributions. Therefore, policy makers and practitioners should ensure the better access to financial resources of small firms and required financial literacy of owner-managers as well as effective government support for fostering more growth of small firms.

•We present a practical method to ascertain CHIP that combines sequence-based and population-based filtering in the UK Biobank and All of Us.•Small changes in filtering parameters can have a large effect on the accuracy of CHIP variant classification.