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Aims: It is unclear whether mortality outcomes differ for patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) surgery who are readmitted to the index hospital where their surgery was performed, or to another hospital. Methods: We analyzed linked hospital and death records for residents of New South Wales, Australia, aged ≥ 18 years who had an emergency readmission within 90 days following THA or TKA surgery between 2003 and 2022. Multivariable modelling was used to identify factors associated with non-index readmission and to evaluate associations of readmission destination (non-index vs index) with 90-day and one-year mortality. Results: Of 394,248 joint arthroplasty patients (THA = 149,456; TKA = 244,792), 9.5% (n = 37,431) were readmitted within 90 days, and 53.7% of these were admitted to a non-index hospital. Non-index readmission was more prevalent among patients who underwent surgery in private hospitals (60%). Patients who were readmitted for non-orthopaedic conditions (62.8%), were more likely to return to a non-index hospital compared to those readmitted for orthopaedic complications (39.5%). Factors associated with non-index readmission included older age, higher socioeconomic status, private health insurance, and residence in a rural or remote area. Non-index readmission was significantly associated with 90 day (adjusted odds ratio (aOR) 1.69; 95% confidence interval (CI) 1.39 to 2.05) and one-year mortality (aOR 1.31; 95% CI 1.16 to 1.47). Associations between non-index readmission and mortality were similar for patients readmitted with orthopaedic and non-orthopaedic complications (90-day mortality aOR 1.61; 95% CI 0.98 to 2.64, and aOR 1.67; 95% CI 1.35 to 2.06, respectively). Conclusion: Non-index readmission was associated with increased mortality, irrespective of whether the readmission was for orthopaedic complications or other conditions. Cite this article: Bone Jt Open 2024;5(1):60–68.

Traditional healers have used the shrub Calotropis procera (CP) for many years for various therapies. The present study investigated the bioactive constituents of ethanolic extract of CP leaf and dried latex using gas chromatography-mass spectrometry and Fourier transforms infrared spectroscopy. The identification and characterization of the compounds were confirmed by examining the constituents' mass spectrum fragmentations and FT-IR spectra and comparing the results with those in the literature. The tail-flick method was used to investigate the analgesic properties of the extract and its anti-inflammatory activities using a rat model of formalin-induced oedema. Acute oral toxicity in rats was studied per OECD recommendations. Twenty male rats were divided into four groups and received an ethanolic extract of the leaves and dried milky sap of CP (200 mg/Kg) in groups 1, 2, and 3. Group 4 rats were administered aspirin 50 mg/kg as a positive control. The CP dried latex extract has the highest content of lupeol and its acetate derivative compared to its leaf extract. The CP dried latex extract inhibited inflammation more significantly than the ethanolic leaf extract and the drug indomethacin at a higher dosage (200 mg/kg). The ethanolic extracts showed analgesia comparable to aspirin. It suggests that fatty acids and their esters, particularly ethyl linoleate (8.96%), ethyl palmitate (7.99%), ethyl linoleate (6.98%), and palmitic acid (5.18%), may be valuable biomarkers for characterizing leaf and latex samples and describing the medicinal potential of CP.

Hepatocellular carcinoma (HCC) is the primary liver cancer that occurs the most frequently. The risk of developing HCC is highest in those with chronic liver diseases, such as cirrhosis brought on by hepatitis B or C infection and the most common type of liver cancer. Knowledge-based interpretations are essential for understanding the HCC microarray dataset due to its nature, which includes high dimensions and hidden biological information in genes. When analyzing gene expression data with many genes and few samples, the main problem is to separate disease-related information from a vast quantity of redundant gene expression data and their noise. Clinicians are interested in identifying the specific genes responsible for HCC in individual patients. These responsible genes may differ between patients, leading to variability in gene selection. Moreover, ML approaches, such as classification algorithms, are similar to black boxes, and it is important to interpret the ML model outcomes. In this paper, we use a reliable pipeline to determine important genes for discovering HCC from microarray analysis. We eliminate redundant and unnecessary genes through gene selection using principal component analysis (PCA). Moreover, we detect responsible genes with the random forest algorithm through variable importance ranking calculated from the Gini index. Classification algorithms, such as random forest (RF), naïve Bayes classifier (NBC), logistic regression, and k-nearest neighbor (kNN) are used to classify HCC from responsible genes. However, classification algorithms produce outcomes based on selected genes for a large group of patients rather than for specific patients. Thus, we apply the local interpretable model-agnostic explanations (LIME) method to uncover the AI-generated forecasts as well as recommendations for patient-specific responsible genes. Moreover, we show our pathway analysis and a dendrogram of the pathway through hierarchical clustering of the responsible genes. There are 16 responsible genes found using the Gini index, and CCT3 and KPNA2 show the highest mean decrease in Gini values. Among four classification algorithms, random forest showed 96.53% accuracy with a precision of 97.30%. Five-fold cross-validation was used in order to collect multiple estimates and assess the variability for the RF model with a mean ROC of 0.95±0.2. LIME outcomes were interpreted for two random patients with positive and negative effects. Therefore, we identified 16 responsible genes that can be used to improve HCC diagnosis or treatment. The proposed framework using machine-learning-classification algorithms with the LIME method can be applied to find responsible genes to diagnose and treat HCC patients.

Fuel cell holds the promise of being environmentally friendly and becomes one of the alternatives for renewable energy. Solid acids have super–protonic behavior which allows them to become conductors. It can function at high temperatures. Hydration, on the other hand, may be used to improve the performance of the solid acid. Moreover, the conductivity, stability, and crystal structure of the solid acid compounds of the fuel cell are all influenced by the size of the electrolyte membrane. Very few works have been done on solid acid fuel cells which are still under investigation to make it one viable as well as a reliable alternative to clean, renewable energy. In general, this work will provide an overview of the variables or characteristics that affect the technical effectiveness and performance of the unique super–protonic conductors for solid acid fuel cells.

Synchronously Amplified Photoacoustic Image Recovery (SAPhIRe) offers improved background suppression using non-linear properties of modulatable contrast agents. Using SAPhIRe, multiple contrast agents in the same absorption window can be detected independently based on their unique triplet-state lifetimes. Here, we have demonstrated the unmixing of rose bengal and eosin Y signals from solution based on triplet-state lifetime mapping using both fluorescence and photoacoustics. Varying the pump-probe delay enables resolution and recovery of fast-decaying rose bengal and of slowly decaying eosin Y modulated photoacoustic signals, resulting from optically gated triplet state residence. Distinct images were reconstructed within tissue-mimicking phantom using the fitting coefficients of triplet-state lifetimes. Fluorescence was used to screen for modulation prior to photoacoustic imaging. The results suggest that lifetime unmixing can be utilized to simultaneously detect multiple pathologies with overlapping spectra using photoacoustic imaging.

The growing number of divorces and separations induces the frequency of single mothers facing challenges in the mainstream socio-economic realities in Bangladesh. Past studies focused on structural aspects, economic hardship, and psychological issues of single mothers, mainly in the urban context. Nevertheless, the challenges in rural settings vary from urban single motherhood, and this aspect remains understudied. This qualitative narrative aims to analyse the challenges of single mothers in rural settings in Bangladesh. Twenty-eight in-depth interviews and ten key informant interviews were conducted using semi-structured interview guidelines. Thematic analysis identified four major themes of the rural single mothers’ challenges, social, economic, cultural, and psychological, leading a single mother to become dependent on other family members or relatives. The underlying factor of the challenges was the deprivation of property, patriarchal social structure, and social stigma. Rural single mothers face more complex challenges than urban ones because of the lack of income opportunities, insecurity, and self-dependencies. Findings will contribute to recommending and formulating a policy for the single mother considering the local realities of the rural single mothers in Bangladesh.

In this paper, we develop a new point-of-entry security measure for smartphone users. We devise a concept, the “Quad Swipe Pattern”, which includes four swipes from a user in four directions and utilizes the user’s swipe behavior for authentication. The Quad Swipe Pattern overcomes several shortcomings present in current point-of-entry security measures. We performed several experiments to demonstrate the effectiveness of the Quad Swipe Pattern in smartphone user authentication. We evaluated the Quad Swipe Pattern using five machine learning classifiers, three datasets of different sizes, and five different fingers. In addition, we studied how fusion of information from multiple fingers and multiple classifiers can improve the performance of Quad Swipe Pattern. All of our experimental results show significant promise of the Quad Swipe Pattern as a new point-of-entry security measure for smartphones. With a Neural Network model, the Quad Swipe Pattern achieves the Accuracy of 99.7%, False Acceptance Rate of 0.4%, and False Rejection Rate of 0%. With Support Vector Machine, the Quad Swipe Pattern achieves the Accuracy of 99.5%, False Acceptance Rate of 0.4%, and False Rejection Rate of 1.7%. With fusion of two best fingers, the Quad Swipe Pattern demonstrates an excellent performance of a zero Equal Error Rate.

الملخص: أهداف البحث: على الرغم من الانتشار المتزايد للغرق، فإن سلوكيات البحث عن الرعاية الطبية بين ضحايا الغرق لا تزال غير مستكشفة في بنغلاديش. تهدف هذه الدراسة إلى استكشاف سلوكيات البحث عن الرعاية الطبية بين ضحايا الغرق في بنغلاديش. طرق البحث: أجريت هذه الدراسة المقطعية المستندة إلى السكان باستخدام طريقة أخذ العينات الجماعية متعددة المراحل. تم تجميع المعلومات باستخدام استبانة منظمة من ٢٩٩,٢١٦ من سكان القرى والمدن. النتائج: في هذا الاستطلاع، وجدنا ١٩١ من حالات الغرق; ٤٠.٨٤٪ (عدد=٧٨) كانت مميتة و٥٩.١٦٪ (عدد=١١٣) كانت غير مميتة. كان بين حالات الغرق هذه، ٧١.٢٪ (عدد=١٣٦) تم تحويلهم إلى مقدمي الرعاية الصحية، بينما تلقت ٦٢.٨٪ (عدد= ١٢٠) من هذه الحالات الرعاية الطبية من مختلف مقدمي الخدمات الصحية. أظهر المزيد من التحليل أن ٦٦.٦٪ (عدد= ١١٦) كانوا من الأطفال و٢٦.٦٪ (عدد=٤) من البالغين بحثوا عن الرعاية الصحية. سعى أكثر عدد ممكن ٧٨.٩٪ (عدد= ١٢٠ /١٥٢) من سكان القرى للحصول على الرعاية الصحية بالمقارنة ب ٦١.٥٪ (عدد = ٢٤ /٣٩) من سكان المدن. تلقى ٣١.٧٪ (عدد=٣٨) من بين ضحايا الغرق رعاية صحية من مقدمي الرعاية الصحية المؤهلين; بينما تلقى ٦٨.٣٪ (عدد= ٨٢) رعاية صحية من مقدمي الرعاية الصحية غير المؤهلين. تلقى تقريبا ٥٩ (٤٩٪) من الضحايا رعاية من الصيدلية و٣٤ (٢٨٪) من مستشفى معروف. كان معدل الدخول إلى المستشفى بسبب الغرق ١١.٧٪. تم إحضار ١٤ (١١.٧٪) من ضحايا الغرق إلى المستشفى بواسطة المركبات الآلية وغير الآلية. وسعى ما يقارب ٩٧ (٨٠.٨٪) من المرضى للرعاية الصحية وتمكنوا من البقاء على قيد الحياة. الاستنتاجات: سعى عدد كبير من ضحايا الغرق إلى الحصول على رعاية طبية من مقدمي الرعاية الصحية المؤهلين وغير المؤهلين. هناك حاجة لوضع قواعد إرشادية بشأن توفير الرعاية الطبية لضحايا الغرق في بنغلاديش. Abstract: Objective: Despite the high magnitude of drowning, medical care-seeking behaviours among drowning casualties remain unexplored in Bangladesh. This study aimed to explore this behaviour among drowning casualties in Bangladesh. Methods: A population-based cross-sectional study was conducted using a multi-stage cluster sampling method. Data were collected using a structured questionnaire from 299,216 rural and urban residents. Results: From the survey, we found 191 drowning cases: 40.84% (n = 78) were fatal and 59.16% (n = 113) were non-fatal. Among the drowning cases, 71.2% (n = 136) were referred to healthcare providers, while 62.8% (n = 120) received medical care from different health service providers. Further analysis showed that 66.6% (n = 116) of children and 26.6% (n = 4) of adults sought healthcare. As many as 78.9% (n = 120/152) of rural residents sought healthcare, as compared to 61.5% (n = 24/39) of urban residents. Among all drowning casualties, 31.7% (n = 38) received healthcare from a qualified healthcare provider, whereas 68.3% (n = 82) received it from non-qual]ified healthcare providers. About 59 (49%) casualties received care from a pharmacy and 34 (28%) from a recognised hospital. The hospital admission rate for drowning was 11.7%. About 14 (11.7%) drowning casualties were brought to hospitals in motorised or non-motorised vehicles. As many as 97 (80.8%) patients sought healthcare attention and managed to survive. Conclusion: A significant number of drowning casualties sought medical care from qualified and non-qualified healthcare providers. In Bangladesh, it is necessary to develop guidelines for providing medical care for drowning casualties.

Abstract The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross‐validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.

In molecular and cellular photoacoustic imaging with exogenous contrast agents, image contrast is plagued by background resulting from endogenous absorbers in tissue. By using optically modulatable nanoparticles, we develop ultra-sensitive photoacoustic imaging by rejecting endogenous background signals and drastically improving signal contrast through time-delayed pump-probe pulsed laser illumination. Gated by prior pump excitation, modulatable photoacoustic (mPA) signals are recovered from unmodulatable background through simple, real-time image processing to yield background-free photoacoustic signal recovery within tissue mimicking phantoms and from ex-vivo tissues. Inherently multimodal, the fluorescence and mPA sensitivity improvements demonstrate the promise of Synchronously Amplified Photoacoustic Image Recovery (SAPhIRe) for PA imaging in diagnosis and therapy.

Angiopoietins 1−4 (Ang1−4) represent an important family of growth factors, whose activities are mediated through the tyrosine kinase receptors, Tie1 and Tie2. The best characterized are angiopoietin-1 (Ang1) and angiopoietin-2 (Ang2). Ang1 is a potent angiogenic growth factor signaling through Tie2, whereas Ang2 was initially identified as a vascular disruptive agent with antagonistic activity through the same receptor. Recent data demonstrates that Ang2 has context-dependent agonist activities. Ang2 plays important roles in physiological processes and the deregulation of its expression is characteristic of several diseases. In this review, we summarize the activity of Ang2 on blood and lymphatic endothelial cells, its significance in human physiology and disease, and provide a current view of the molecular signaling pathways regulated by Ang2 in endothelial cells.

Summary: Background and aims: One-third of the country suffers from metabolic dysfunction-associated fatty liver disease (MAFLD) due to unhealthy, improper dietary habits and sedentary lifestyles. This study aimed to explore the specific diet and lifestyle factors responsible for MAFLD. Methods: This case-control study was conducted among the source population (n=200 diagnosed with MAFLD criteria; n=100 healthy controls). Quantitative approaches, such as interviews, were applied to measure dietary practices and physical activity levels. Results: Most (89.5%) of the MAFLD patients were obese, used to sedentary lifestyles (56.0%), and took outside foods (58.5%) than their counterparts (P300 minutes sitting times (aOR=8.6), >300 g/day carbohydrate consumption (aOR=6.9), >950 kcal/day from rice (aOR=2.2), >50 g/day fat (aOR=2.9), ≤2000 Kcal/day energy-expenditure (aOR=2.9), BMI >25 kg/m2 (Adjusted odds ratio=aOR=16.1), eating outside (aOR=7.4), being housewives (aOR=4.89), service-holders (aOR=4.12), >31 age group (aOR=2.9), are significant risk factors for MAFLD. Conclusion: Longer sitting times, outside eating habits, more energy from carbohydrates, especially rice, and a daily positive energy balance lead to obesity and, consequently, an increased risk of MAFLD.

This research investigated how supplementing growing rabbits with zinc oxide nanoparticles (Nano-ZnO) and selenium nanoparticles (Nano-Se) impacts their growth performance, digestibility, blood biochemistry, and liver mineral content. The study utilised 40 male and 40 female New Zealand White rabbits, all 30 days old, with an average weight of 720 ± 6 g. Rabbits were divided into four treatment diets with 10 replicates of two rabbits each (one male and one female) for 60 days. The diets comprised of 1) control diet, 2) control diet + 40 mg/kg Nano-ZnO, 3) control diet + 0.6 mg/kg Nano-Se, and 4) control diet + 40 mg/kg Nano-ZnO + 0.6 mg/kg Nano-Se. Results showed that dietary Nano-ZnO and Nano-Se in single or combination improved (p

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

Abstract The rapid infrastructure development and the need to house the dense population of Bangladesh drive the construction business to transform from a low-rise to a medium-rise industry. In this transformation, steel–concrete composite has been prioritized for constructing high-rise buildings. It is crucial to look at how the steel industry has developed and what the future holds for the usage of steel–concrete composition in the construction sector of Bangladesh. Also, it is critical to understand the role of reinforced cement concrete (RCC) technology in this transformation process. The current study examined import and export promotion statistics for steel, iron, and cement to get insights into the development and transformation of the construction sector. To assess the existing state and prospects of the steel industry, this study also collected revenue data from the major steel companies of Bangladesh. Finally, a poll on the usage of steel–concrete composite construction has been conducted to better understand the public opinions about the steel industry. Findings from the Export Promotion Bureau (EPB) indicate that there has been a growing popularity of steel structures in the last 10 years boosting the financial revenue of Bangladesh. Import data revealed a moderately strong relationship, with cement imports accounting for 37.4% of the variation in steel and iron imports. Insights into public opinion show a generally positive attitude toward adopting steel–concrete composites, with 17.6% of the variation in willingness to pay premiums explained by factors like high cost, longevity, and cost-effectiveness. Nevertheless, the data suggests that cement has been more frequently utilized in the construction sector than steel. The findings of this study provide valuable information on the opportunities and challenges encountered by the steel industry. This information can aid policymakers in making informed investment and strategic planning decisions that will support the economic development of the country.

Abstract Electric three-wheelers consume a great deal of power causing load shedding in industrial and residential areas. This research investigates the feasibility of a solar-assisted electric three-wheeler for deployment in Bangladesh, integrating solar photovoltaic system and pedaling generator. This research focused on enhancing the energy efficiency and sustainability of electric three-wheelers using renewable energy reducing dependency on the national grid. The rooftop space of the vehicle is insufficient for the solar PV system, necessitating the whole surface area of the vehicle with a 600 W solar panel. Simulations for Dhaka, Cox’s Bazar, and Rangpur assessed the solar energy potential across Bangladesh. A 48 V solar PV array and a Maximum Power Point Tracking (MPPT) charge controller were designed to optimize battery charging, achieving converter efficiencies between 97%–99.2%. Partial shading significantly impacted performance, while tilt angle optimization suggested a 0 $$ ^{\circ }$$ ∘ tilt for moving vehicles and a 23 $$ ^{\circ }$$ ∘ tilt during idle periods from October to March for optimal energy capture. A pedaling DC generator was introduced to charge the battery during idle times without grid reliance. The simulations for energy harvesting estimated an annual energy production of approximately 820 kWh. The integration of these systems increased drag, reducing maximum speed and requiring additional power. 172.28 kWh annual additional energy consumption was calculated for the solar PV system and 200.75 kWh for the pedaling charger. The study found the solar-assisted system with enhanced efficiency and sustainability but the limited economic viability of the pedaling generator. In future, the system can be developed for other countries with different vehicles.

Abstract Transparent conducting oxides (TCOs) are crucial for high-performance displays, solar cells, and wearable sensors. However, their high process temperatures and brittle nature have hindered their use in flexible electronics. In this paper, we overturn these limitations by harnessing Cabrera-Mott oxidation to fabricate large-area, two-dimensional (2D) transparent electrodes via liquid metal printing. Our robotic, vacuum-free process deposits ultrathin (2–10 nm) indium tin oxide (ITO) with exceptional flexibility, transparency (>95%) and conductivity (>1300 S/cm) by utilizing hypoeutectic In-Sn alloys to print at

Cancer is a complex group of diseases characterized by uncontrolled cell growth that can spread throughout the body, leading to serious health issues. Traditional treatments mainly include chemotherapy, surgery, and radiotherapy. Although combining different therapies is becoming more common, predicting how these treatments will interact and what side effects they may cause, such as gastrointestinal or neurological problems, can be challenging. This research applies optimal control theory (OCT) to create precise and personalized treatment plans for cancer patients. OCT helps identify the most effective doses of chemotherapy and immunotherapy by forecasting how various treatment combinations will impact tumor growth and the immune response over time. It optimizes the integration of chemotherapy with immunotherapy to minimize side effects while maximizing therapeutic benefits. The study proposes a model for managing malignant tumors using a mix of immunotherapy, vaccines, and chemotherapy. The aim is to develop the best treatment plan that reduces new tumor growth while keeping healthy cells stable. It also takes into account individual differences among patients, including variations in tumor biology and immune responses in both younger and older individuals. To do this, we compared different optimal control strategies: interior point optimization (IPOPT), an open-source tool for nonlinear optimization; state-dependent Riccati equation (SDRE), which adapts linear control methods for nonlinear situations; and approximate sequence Riccati equation (ASRE), a globally optimal feedback control approach for nonlinear systems. The optimization criterion showed that the proposed work achieved a cost value of 52.3573 for IPOPT, compared with 52.424 for both SDRE and ASRE. For $ \mathrm{C}\mathrm{D}{8}^{+} $ T cells, the proposed method maintained a consistent value of 1.6499 for continuous (C) and dosed (D) across all techniques. Tumor cell counts had a C value of 0.0007 for IPOPT, compared with 0.0006 for ISDRE and ASRE, with D values remaining at 0 across all methods. This comparison demonstrates the successful use of control theory techniques and highlights their potential for developing personalized and effective treatment strategies for complex cancer cases. By optimizing treatment schedules and dosages, OCT can help minimize the side effects of cancer therapies, thereby enhancing patients' overall quality of life.

The southwest region of Bangladesh is particularly vulnerable to a range of climatic threats, including cyclones, prolonged flooding, sea level rise, saltwater intrusion, drought, and riverbank erosion. The study investigates how these different drivers affect aquaculture systems and aims to provide critical insights for sustainable management. The survey work focused on the problems, vulnerabilities, migration, and adaptive strategies of communities of the southwest region of Bangladesh, that rely heavily on shrimp, fish, and crab production for their livelihoods. Data were collected from the 80 respondents across four unions (Atulia, Burigoalini, Gabura, and Bhurulia) in Shyannagar Upazila, Satkhira District, using a structured questionnaire survey and focus group discussions. Findings reveal that climate change events have significantly changed shrimp farming in enclosures, pond aquaculture, and crab point management, and negatively impacted livelihoods. Pond aquaculture appeared to be the most vulnerable to climate change conditions, followed by shrimp farming in enclosures and crab points. Furthermore, the adverse effects of climate change compelled human migration within the study area, primarily driven by the search for employment. This study provides evidence of the effects of various climate change stressors on shrimp, fish, and crab production systems and the adaptive difficulties of the communities dependent on aquatic ecosystems. As the natural calamity like cyclone cannot be prevented, understanding the impact of previous events may help people of the affected area as well as the policy makers to plan for better survival.

Abstract The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices. We provide an overview of Green AI, highlighting its significance and current state regarding AI’s energy consumption and environmental impact. The paper explores sustainable AI techniques, such as model optimization methods, and the development of efficient algorithms. Additionally, we review energy-efficient hardware alternatives like tensor processing units (TPUs) and field-programmable gate arrays (FPGAs), and discuss strategies for designing and operating energy-efficient data centers. Case studies in natural language processing (NLP) and Computer Vision illustrate successful implementations of Green AI practices. Through these efforts, we aim to balance the performance and resource efficiency of AI technologies, aligning them with global sustainability goals.

Abstract Global warming has led to a notable increase in heatwaves globally and regionally. In spring 2022, South Asia witnessed an unprecedented heatwave with temperatures breaking historical records and exceeding 5° C from climatological mean at several locations in the northern Indian subcontinent. Here, using 3D tracking, this heatwave ranked the most severe in the past 64 years, characterized by its protracted duration and wide spatial extent. The excess atmospheric heat, represented by temperature anomalies, during the mega-heatwave triggered rapid snowmelt across the snow-covered areas in the region, leading to an average loss of 42% in snow cover and 57% in snow depth of the regional snowpack. This rapid melting resulted in the complete disappearance of low-level snowpack in the western Himalayas, Pir Panjal, and Afghanistan highlands, leading to the lowest snowpack observed in the last six decades. Moreover, during the heatwave, the amount of snowfall received was only 29% of the long-term average. This combination of excessive melting and reduced snowfall culminated in a severe regional snow drought in spring 2022. The heatwave genesis lay a persistent high-pressure system over Northwest South Asia, reinforced by a quasi-stationary Rossby wave packet over Europe during the initial spell. Continuous heat from high-pressure ridges associated with circumglobal Rossby waves combined with the physical barrier of the Himalayas, lent staying power to this system during the second phase.

Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Abstract This study aimed to determine the longitudinal predictors of lifestyle behaviours among stroke survivors in New South Wales, Australia. This longitudinal study utilised data from the baseline survey (2005–2009) and a sub-study survey (2017) of the 45 and Up Study. Physical activity, alcohol consumption, smoking status, and supplement use were included as dependent variables. Generalised estimating equation models were employed to assess the longitudinal association between the dependent variable and demographic and health status measures. The average age of the participants (n = 576) was 67 (SD = 9) years at baseline and 76 (SD = 9) years at the sub-study survey time, with 54.9% being male. The longitudinal analysis revealed that the likelihood of moderate/high physical activity significantly declined over time and was lower among participants with diabetes, but was higher among those with university education. The likelihood of smoking was significantly higher in females, moderate/high-risk alcohol consumers, and those with depression, but was lower among supplement users. The likelihood of moderate/high-risk alcohol consumption significantly declined with time, and was lower among females, but higher among smokers. The likelihood of supplement use significantly declined over time, but was higher among females and/or those with asthma. Our findings help illustrate that many stroke survivors may benefit from further support in adopting and maintaining a healthy lifestyle as part of their stroke management and long-term rehabilitation, which is crucial to optimising their quality of life and successful secondary stroke prevention.

Abstract The number of theoretical and empirical literature on Industry 4.0 research has seen tremendous growth over the past few years. We seek to identify and classify the popular research topics published on Industry 4.0 in business and economics literature using a novel approach. First, we performed a co-occurrence analysis and found Industry 4.0 to have the strongest connection with innovation, implying the indispensability of innovation for the fourth industrial revolution. Further, we employed LDA-based topic modelling to identify the most frequent terms and labelled eight different topics based on the quantitative value of each term. Finally, we reviewed the relevant literature for each topic identified through the topic modelling process. Our literature review reveals that the concentration of existing literature is model-focussed. This study provides quantitative evidence of frequent occurrences of popular terms. We conclude that future studies should focus more on empirical measurement of the benefits of transiting towards the fourth industrial revolution.

Abstract Mushrooms have been considered as a therapeutic and nutrient-rich foods in numerous countries for years due to their significant levels of nutritional components, vitamins and antioxidants. This study aimed to assess the nutrient content, mineral profile and antioxidant properties of eight different kinds of oyster mushrooms (Pleurotus sp.) cultivated in Bangladesh. The mushrooms were analyzed for their moisture content, carbohydrates, proteins, fats, fibers, ash, vitamin A, and vitamin C. The mineral profile included sodium, potassium, calcium, and iron, as well as heavy metals. Antioxidant activities were evaluated through various methods, including phenolic, flavonoid and tannin content, total antioxidant activity, and DPPH (2,2-diphenyl-1-picryl-hydrazyl) scavenging capacity. The findings revealed that the mushrooms under investigation were high in carbohydrate (ranges between 45.25 and 63.22 g/100 g), protein (23.48–33.16 g/100 g) and dietary fiber (42.87–52.31 g/100 g). The total ash content was ranged from 7.28 to 9.41 g/100 g and vitamin C content was 0.020–0.416 mg/100 g. Interestingly, all of the mushrooms had a lower fat content (0.91–2.6%). The mineral profile indicated the presence of varying concentrations of essential elements. Hazardous heavy metals, such as Cd and Cr were not detected in the mushroom sample, while As and Pb were detected but lower than the tolerance levels. The oyster mushrooms exhibited high levels of phenolic, flavonoid, and tannin contents, as well as significant antioxidant capacity. A methanol extract of the oyster mushrooms exhibited scavenging activity against DPPH in a dose dependent manner with the highest capacity (75.4%) obtained at a concentration of 0.2 mg/ml. Our result suggests that these studied oyster mushrooms could be a good source of nutrition and antioxidants, which could be used as functional foods.

Abstract This study focuses into strategizing the usage of self-reacting bobbin tool friction stir welding (SRBT-FSW) to obtain consistent microstructural homogeneity along the thickness of AA6061 aluminium alloy (AA) thick plates during welding. The SRBT-FSW technology, distinguished by its dual-shoulder design, represents a significant step forward in FSW by eliminating the requirement for a backing anvil and promoting balanced heat distribution. This study seeks to address the issues of maintaining uniform microstructural characteristics throughout the weld zone, which is crucial for the mechanical performance and durability of welded joints in structural applications. The experimental study entails the systematic welding of AA6061 plates of 6 mm thickness using a self-reacting bobbin tool under a fixed processing condition. Electron backscatter diffraction (EBSD) was used to characterize the grain structure and phase distribution over the weld. Mechanical parameters like as tensile strength and hardness were determined to establish correlations between microstructure and mechanical performance. The results demonstrated that SRBT-FSW significantly enhances microstructural homogeneity across the weld zone, leading to improved mechanical properties. In the Bottom Zone (BZ), a refined grain structure with an average grain size (AGS) of 3.53 µm and a random or weak texture was observed, contributing to enhanced hardness and mechanical performance, with an ultimate tensile strength (UTS) of 220 MPa. In contrast, the Top Zone (TZ) exhibited a coarser AGS of 4.33 µm with a pronounced {111} crystallographic texture, resulting in a slightly lower UTS of 205 MPa. The Middle Zone (MZ), influenced by the greater heat input from both the TZ and BZ, showed an intermediate AGS of 3.99 µm, predominantly oriented along the {101} plane, and achieved a UTS of 194 MPa, with a slight reduction in ductility. This study highlights the potential of self-reacting bobbin tool friction stir welding as a reliable method for making high-quality, homogeneous welds in thick aluminium plates and paving way for their wider application in the aerospace, automotive, and shipbuilding industries, where homogeneous microstructural qualities are of significant importance.

This case series aims to describe the clinical presentation of mastitis, the conventional sonography and elastography findings, and histopathological features in the diagnosis of chronic mastitis. We present 3 cases of breast swelling in young ladies with one of the cases is related to breastfeeding with similar imaging appearance of complex breast cyst and the histopathology finding of chronic mastitis. We will describe the role of elastography in evaluating and differentiating the benign and malignant complex breast cyst.

Ultrasonic metal welding machines are suitable for various complex applications (e.g., battery tabs) through unique mechanical design, special pressure application methods and high-precision welding. This work reports the weldability, forming quality and fractographic analysis of copper multilayered welded joints which were studied by SEM-EDS characterization, micro-hardness testing and tensile testing based on ultrasonic double roller welding (UDRW). Three groups of process parameters (A, B and C) were established to investigate the performance, production quality and welded joint surface interconnections. The tensile testing results of sample under parameter 3 in group A [S-P3(A)] indicate the maximum tensile strength of 69.859 N in T-peel test while the average tensile strength has increased by 58.525 N due to rise in welding time from 2 sec to 5 sec. The results analysis indicates that welding quality features in S-P3(A) joints under 4 bar, 100 mm/s, 45 % have been exploited. The over-welded zone was transformed into good-welded zone. The micro-cracks, fatigue stations and peeling texture in multilayers were reduced. It was found that when the welding energy was 10000 J then the tearing edges and interlayers cracks were minimized while keeping the other parameters constant. Moreover, when the amplitude increased up to 50 %, then numerous micro-cracks and micro-fissure stations were created, which leads to the occurrence of fracture in multi-layer welded joint. The EDS study investigated that the complex features are formed at the interface junction of sample 3 S3(A) in multilayer welds. The complex multilayer microstructures can induce and produce unique hardness properties for battery manufacturing. It leads to high quality and durable welds. Eventually, it is experimentally demonstrated that robust 40 layer welded joints can be obtained by the UDRW process. Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Introduction: Global environmental changes, including rising atmospheric temperatures due to climate variability, are increasingly linked to the emergence or resurgence of fungal infections worldwide. Candida auris (C. auris) is a notable example, with studies suggesting that heat stress may prompt genetic adaptations in these fungi, facilitating their environmental resilience. This study reviews the existing knowledge and clinical impact of emerging C. auris infections in Bangladesh. It underscores the importance of evidence-based research in shaping policies for early detection and enhanced preparedness for potential outbreaks. Methods: Between November 1st and December 31st, 2023, we conducted a rapid review of the scientific literature focusing on C. auris, antibiotic resistance, fungal disease, Bangladesh, and climate change. This involved systematic searches of databases like Google Scholar, MEDLINE (PubMed), and Web of Science, as well as grey literature and relevant websites like the World Health Organization (WHO) and the Center for Disease Control (CDC), with a narrative synthesis of studies in English that addressed the incidence of C. auris infections and their antibiotic susceptibility in Bangladesh, excluding non-primary research such as protocols, reviews, dissertations, and posters. Results: Our review synthesized four studies from hospitals in Dhaka and Mymensingh, Bangladesh, revealing the prevalence of C. auris infections, especially in neonates and adults in intensive care units. These studies primarily identified C. auris from blood and urine samples, with a significant concentration in neonatal intensive care units, and highlighted a persistent presence and resistance to antibiotics, particularly fluconazole. Antifungal susceptibility testing across these studies demonstrated varying resistance levels, notably high resistance to fluconazole and moderate resistance to other antifungals like amphotericin B and voriconazole. Conclusions: Bangladesh's insufficient healthcare infrastructure, together with inappropriate use and overprescription of antibiotics, are significant causes of antibiotic resistance. More studies need to be undertaken on C. auris in Bangladesh and worldwide.

Periodontal disease, characterized by bacterial plaque accumulation and subsequent immune response, can lead to gingivitis and periodontitis if untreated. Salivary alpha-amylase (sAA) has emerged as a potential biomarker with implications in periodontal disease progression. Objectives: This study aimed to assess and compare salivary alpha-amylase levels in individuals with periodontitis and healthy controls and to investigate its relationship with clinical parameters of periodontal disease. Forty-five participants were categorized into periodontally healthy (n = 13), Stage I and II Periodontitis (n = 17), and Stage III and IV periodontitis (n = 15) groups. Saliva samples were collected and analyzed using ELISA kits. Statistical analyses included tests for normality, group comparisons, post hoc analysis, and correlation analysis. Significant differences in salivary alpha-amylase levels were observed among severity groups (p < 0.05), with higher levels in periodontitis patients than healthy controls. Spearman correlation revealed moderate positive associations between alpha-amylase levels and probing depth (PD) and clinical attachment loss (CAL). Elevated salivary alpha-amylase levels were found to be associated with more severe periodontal disease, suggesting its potential as a biomarker for periodontitis severity. These findings support the utility of salivary biomarkers in periodontal disease diagnosis and monitoring, although further validation and standardization are warranted for clinical application.

Abstract Blue economy has the potential to promote economic growth, improve livelihoods, and create jobs while protecting marine ecosystems. This research uses a comprehensive analysis of secondary data sources to assess various blue economy sectors, including maritime transport, fisheries, aquaculture, offshore renewable energy, marine tourism, marine biotechnology, and ocean mining. By examining the blue economy experiences of developed nations like the United States, Canada, Japan, Norway, and Australia, the study identifies the best SDG practices and strategic lessons applicable to Bangladesh. In the case of Bangladesh, the research focuses on the blue economy initiatives, opportunities, and challenges associated with the Sustainable Development Goals (SDGs). The blue economy and SDGs nexus in the context of Bangladesh demonstrates that out of 17 goals, 12 SDGs (SDG 1, SDG 2, SDG 3, SDG 7, SDG 8, SDG 9, SDG 11, SDG 12, SDG 13, SDG 14, SDG 16 and SDG 17) are linked with blue economy practices in Bangladesh. However, in the case of developed countries, only six SDGs (SDG 7, SDG 8, SDG 9, SDG 12, SDG 13, SDG 14) are connected to the blue economy because of the diversity of blue economy practices across the countries. Situated along the Bay of Bengal, Bangladesh has significant potential to utilize its marine resources for sustainable development. However, it faces challenges such as inadequate infrastructure, regulatory gaps, environmental risks, and limited technological advancements. The study thus emphasizes the need for integrated policy frameworks, stakeholder coordination, investments in sustainable infrastructure, public–private partnerships, technological innovation, and community engagement.

Abstract Background Capsicum annuum, a significant agricultural and nutritional crop, faces production challenges due to its sensitivity to various abiotic stresses. Glyoxalase (GLY) and D-lactate dehydrogenase (D-LDH) enzymes play vital roles in mitigating these stresses by detoxifying the stress-induced cytotoxin, methylglyoxal (MG). Methods A genome-wide study was conducted to identify and characterize glyoxalase I (GLYI), glyoxalase II (GLYII), unique glyoxalase III or DJ-1 (GLYIII), and D-LDH gene candidates in Capsicum annuum. The identified members were evaluated based on their evolutionary relationships with known orthologues, as well as their gene and protein features. Their expression patterns were examined in various tissues, developmental stages, and in response to abiotic stress conditions using RNA-seq data and qRT-PCR. Results A total of 19 GLYI, 9 GLYII, 3 DJ-1, and 11 D-LDH members were identified, each featuring characteristic domains: glyoxalase, metallo-β-lactamase, DJ-1_PfpI, and FAD_binding_4, respectively. Phylogenetic analysis revealed distinct clades depending on functional diversification. Expression profiling demonstrated significant variability under stress conditions, underscoring their potential roles in stress modulation. Notably, gene-specific responses were observed with CaGLYI-2, CaGLYI-7, CaGLYII-6, CaDJ-1 A, and CaDLDH-1 showed upregulation under salinity, drought, oxidative, heat, and cold stresses, while downregulation were shown for CaGLYI-3, CaGLYII-1, CaDJ-1B, and CaDJ-1 C. Remarkably, CaGLYI-1 presented a unique expression pattern, upregulated against drought and salinity but downregulated under oxidative, heat, and cold stress. Conclusion The identified GLY and D-LDH gene families in Capsicum annuum exhibited differential expression patterns under different abiotic stresses. Specifically, CaGLYI-2, CaGLYI-7, CaGLYII-6, CaDJ-1 A, and CaDLDH-1 were upregulated in response to all five analyzed abiotic stressors, highlighting their critical role in stress modulation amidst climate change. This study enhances our understanding of plant stress physiology and opens new avenues for developing stress-resilient crop varieties, crucial for sustainable agriculture.

Abstract Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Abstract Alzheimer’s disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Abstract As metallic nanostructures shrink towards the size of the electronic mean free path, thermal conductivity decreases due to increased electronic scattering rates. Matthiessen’s rule is commonly applied to assess changes in electron scattering rates, although this rule has not been validated experimentally at typical operating temperatures for most of the electronic systems (e.g., near room temperature). In this study, we experimentally evaluate the validity of Matthiessen’s rule in determining the thermal conductivity of thin metal films by measuring the in-plane thermal conductivity and electronic scattering rates of copper (Cu) films with varying thicknesses (27 nm — 5 µm), microstructures, and grain boundary segregation. Comparing total electron scattering rates measured with infrared ellipsometry to infrared ultrafast pump-probe measurements, we find that the electron-phonon coupling factor is independent of film thickness, whereas the total electronic scattering rate increases with decreasing film thickness. Our findings provide experimental validation of Matthiessen’s rule for electron transport in thin metal films at room temperature and also introduce an approach to discern critical heat transfer processes in thin metal interconnects, which holds significance for the advancement of future CMOS technology.

Abstract Mutant KRAS promotes the proliferation, metastasis, and aggressiveness of various cancers including pancreatic ductal adenocarcinoma (PDAC), non-small cell lung cancer (NSCLC), and colorectal adenocarcinoma (CRC) respectively. Mutant KRAS therapeutics are limited, while Sotorasib and Adagrasib were the only FDA-approved drugs for the treatment of KRASG12C mutated NSCLC. Chimeric antigen receptor (CAR) T-cell therapy has been emerged as an effective strategy against hematological malignancies and being extended towards solid cancers including PDAC. mesothelin (MSLN) and Carcinoembryonic Antigen (CEA) were reported to be highly overexpressed in KRAS-mutated PDAC. Meanwhile, in clinical trials, several CAR T-cell therapy studies are mainly focused towards these two cancer antigens in PDAC, however, the dual targeting of these two neoantigens is not reported. In the present study, we have designed and developed a novel dual-targeting CAR protein by employing various bioinformatics approaches such as functional analysis (antigenicity, allergenicity, antigen binding sites & signalling cascades), qualitative analysis (physicochemical, prediction, refinement & validation of 2D and 3D structures), molecular docking, and in silico cloning. Our results revealed that the designed CAR protein specifically binds with both MSLN & CEA with significant binding affinities, and was predicted to be stable & non-allergenic. Additionally, the protein–protein interaction network reveals the T-cell mediated antitumor responses of each domain in the designed CAR. Conclusively, we have designed and developed a dual targeting (MSLN & CEA) CAR protein towards KRAS-mutated PDAC using computational approaches. Alongside, we further recommend to engineer this designed CAR in T-cells and evaluating their therapeutic efficiency in in vitro and in vivo studies in the near future.

Purpose – Drawing on the signaling theory and technology acceptance model, the main purpose of this study was to predict prospective employees' intentions to apply for jobs in a firm, with a special focus on the mediating role of attitudes toward corporate websites and the moderating role of perceived value fit. Design/methodology/approach – Collecting data from a convenient sample of 318 prospective job candidates, the research hypotheses were tested using structural equation modeling (SEM) with AMOS (version 24) and SPSS Process Macro (version 3.4). Findings – The test results revealed that prospective employees' attitudes toward corporate websites partially mediate the association of corporate reputations, perceived ease of use and perceived usefulness with their intentions to apply for jobs in an organization. Noticeably, perceived value fit moderated the perceived usefulness–application intentions link in such a way that the impact of perceived usefulness on intentions to apply appears higher for individuals with a low level (than a high level) of perceived value fit. Research limitations/implications – Consistent with the research findings, a notable theoretical contribution and practical implications for HR professionals have been discussed. This paper ends with outlining some limitations and future research directions. Originality/value – Despite having the salient buffering effects of perceived value fit on the applicant attraction process, empirical study on this theoretical phenomenon is still sparse in a pre-employment context. This may be the first study that demonstrates under what circumstances prospective employees' job pursuit intentions could be optimized in respect of their perceived value fit within a single framework comprised of two theories.

Abstract Biodiversity offsets are a popular policy tool for mitigating the impact of development on biodiversity, but the ecological success of offsets arise from complex interactions among socio‐economic, ecological and policy processes, making outcomes challenging to assess. Many offset policies use habitat surrogates to determine offset requirements, rather than using direct measures of impacted biota, and this can lead to poor outcomes for species. One potential solution to this is for offsets to be delivered by a public agency (agency‐led) rather than by developers (developer‐led). This is because agencies may be able to strategically choose offset sites that maximise outcomes for species (e.g. abundance), while there may be little reason for developers to act strategically in this way when offset requirements are based purely on habitat surrogates. Yet, the success of a strategic agency‐led approach is likely to depend on patterns of development and offset site availability. To examine this, we developed a novel integrated spatially explicit model of land‐use change, habitat, species abundance and offset regulation. We apply the model to the Queensland Government's Environmental Offsets Policy for koalas Phascolarctos cinereus in South East Queensland, Australia, and test how patterns of development and offset site availability influence the performance of agency‐led versus developer‐led offsets. When potential offset sites were plentiful, agency‐led offsets tended to outperform developer‐led offset delivery for maximising koala abundance while achieving similar or better outcomes for habitat area. Yet, when potential offset sites were rare, the relative performance of agency‐led offset was often poor, and offset requirements for habitat area were less likely to be met. Different spatial patterns of development had little effect on the relative performance of agency‐led versus developer‐led offsets. Our analysis shows that agency‐led offsets with strategic choices of offset sites can improve species' outcomes for habitat‐based offsets but can also risk failing to meet habitat area requirements when the availability of offset sites is low. Importantly, our integrated spatial model provides a holistic approach to assessing policy options for biodiversity offsets in dynamic human‐modified landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Abstract The goal of the current study is to understand the elemental concentration, reserve assessment, mineralogical composition, and distribution of heavy minerals in the dune sands, foreshore, and backshore of Sonadia Island, Southeast Bangladesh. The study area yielded a number of drill hole composite sand samples that were used for density and magnetic separation of heavy minerals. These samples were then subjected to petrographic characterization, grain counting, mineralogical analyses using scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDX), x-ray diffraction (XRD), and bulk sand geochemistry using X-ray fluorescence (XRF). The results of heavy liquid separation show that the total heavy mineral (THM) enrichments in foreshore sand (about 4.86%), backshore sand (about 9.03%), and dune sand (about 22.28%) are significantly higher. After characterizing the sand samples from Sonadia Island based on the abundance of each mineral species, the average content values relative to total economic heavy minerals (TEHM) were determined to be 38.14% ilmenite, 5.74% magnetite, 51.52% garnet, 1.01% zircon, 3.57% rutile, and 0.01% monazite. An SEM–EDX examination reveals that the elemental concentration of ZrO2 in zircon is 66.60%, while the average TiO2 content in rutile is ~ 95%, whereas TiO2 is 45% and the total Fe2O3 is 45.70% in ilmenite. Major phases including amphibole (tremolite) 45.9%, muscovite 15.6%, quartz 12.6%, garnet (almandine) 10.9%, dolomite 7.9%, ilmenite 4.1%, and kaolinite 1.3% are also identified by the XRD pattern. There are an estimated 19,794 tons of garnet in the foreshore sand, 47,324 tons in the backshore sand, and 276,868 tons in the dune sand within the reserve, making it the most prevalent heavy mineral. Ilmenite is the second most common heavy mineral, with quantities detected in the foreshore (15,016 tons), backshore (30,487 tons), and dune sand (215,092 tons).

Abstract In the quest for early cancer diagnosis, early identification and treatment are paramount. Recently, ctDNA detection has emerged as a viable avenue for early screening of cancer. The examination of ctDNA in fluid biopsies has gained substantial attention in tumor diagnosis and therapy. Both the scientific community and industry are actively exploring this field. However, developing cost-effective, portable, and real-time ctDNA measurement methods using conventional gene detection equipment poses a significant challenge. This challenge has led to the exploration of alternative approaches. Electrochemical biosensors, distinguished by their heightened sensitivity, remarkable specificity, affordability, and excellent portability, have emerged as a promising avenue for ctDNA detection. This review is dedicated to the specific focus on ctDNA detection, highlighting recent advancements in this evolving detection technology. We aimed to reference previous studies related to ctDNA-targeted cancer detection using electrochemical biosensors to advocate the utilization of electrochemical biosensors in healthcare diagnostics. Further research is imperative for the effective integration of ctDNA analysis into point-of-care cancer testing. Innovative approaches utilizing multiple markers need to be explored to advance this technology and make substantial contributions to societal well-being.

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.

Introduction Total hip arthroplasty (THA) is the most common, successful modern surgical intervention in orthopedic practice. Physiotherapy interventions are a major part of patient rehabilitation following THA. This study aimed to evaluate the effectiveness of physiotherapy interventions for improving functional outcomes following THA. Methods This narrative review was conducted on electronic platforms and databases, including PubMed, PEDro, CINAHL, and EMBASE, using a combination of search terms related to “physical therapy”, “physiotherapy”, “physical exercise”, “physiotherapy intervention”, “hip arthroplasty”, “total hip arthroplasty”, and “hip joint replacement”. Inclusion criteria included randomized controlled trials or clinical trials published in English from 2014 to 2021. Where THA was recognized as the primary concern, and physiotherapy management was one of the treatment options. Results The initial search of the databases revealed a total of 77 studies. Of those studies, 19 were selected as potentially meeting the inclusion criteria. Eventually, 10 studies were analyzed for the narrative review’s final conclusions. The evaluations supported the hypothesis that physiotherapy interventions have favorable outcomes for patients with THA. Conclusions After reviewing, it has been revealed that physiotherapy can improve physical function, balance, range of motion, muscle strength, and gait pattern following THA.

Graphical Abstract Highlight Research • The LD50 (median lethal dose) of Vibrio parahaemolyticus and harveyi in zebrafish was determined to be 1 x 106 CFU/mL, while for V. alginolyticus it was found to be 1 x 105 CFU/mL. • The LAVh vaccine demonstrated cross-protection against various pathogenic strains of Vibrio, leading to an average of 80% survival rate in vaccinated individuals. • The analysis of the LAVh vaccine emphasized its versatility, as it can be quickly deployed and stored as a freeze-dried powder. • The LAVh vaccine can be easily accessible and user-friendly in various aquaculture environments, such as offshore and remote farms. Abstract Vibriosis poses a significant threat to marine teleosts, causing substantial losses in the global aquaculture industry. Previous work in our lab led to the development of a live-attenuated V. harveyi vaccine (LAVh) candidate that targets the serine endoprotease gene with a three-point knockout and has shown promise in protecting against vibriosis. However, further investigation is necessary to evaluate the stability and efficacy of its various storage conditions for broader applications. This study aims to determine how well the three different LAVh vaccine storage (fresh, stale, and freeze-dried LAVh) worked against vibriosis. A total of 1000 adult zebrafish (Danio rerio) (mean weight: 0.20±0.5 g) were divided into four groups. Groups 1, 2, and 3 were intraperitoneally injected with different LAVh vaccine storage (fresh, stale, and freeze-dried, respectively), while Group 4 received 0.01 M phosphate-buffered saline (PBS) and served as the unvaccinated control. Fish were monitored for 21 days post-vaccination for safety, stability, efficacy, and antibody analysis. The results showed that a modest dosage of 1 x 104 CFU/mL of LAVh vaccine from all storage conditions provided 80% survival upon intraperitoneal challenge with pathogenic strains of pathogenic V. harveyi, V. alginolyticus, and V. parahaemolyticus. This dosage induced significant antibody production and conferred cross-protection against different Vibrio spp., indicating the LAVh vaccine’s potential for commercial application. The LAVh vaccine demonstrated high effectiveness and suitability for storage as a freeze-dried powder. This study might offer significant insights into practical strategies for reducing vibriosis, especially in aquaculture settings with limited infrastructure.

Abstract Background In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS). Methods Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates. Results Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies. Conclusions Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Abstract The fast fashion industry significantly impedes sustainability by its rapid production cycles, low-cost products, and heavy carbon footprint. Nowadays, circular economy (CE) has become an interesting concept, which follows the “take-make-dispose” model and allows products, materials, and resources to be in use as long as possible. There is a paucity of research in the identification of circular economy strategies (CES) and mapping them based on their interrelationships, which is very important from a managerial perspective. In this vein, this study identifies nine CES and analyzes their causal relationships using the Fuzzy Decision-Making Trial and Evaluation Laboratory method in the context of fast fashion footwear manufacture. The key contributions include providing actionable insights for implementing CE practices, enhancing the sustainability of the footwear industry, and offering a strategic roadmap for industry stakeholders. The results and implications of this study will guide footwear industry decision-makers in making a strategic map considering the cause-and-effect interrelationships among the CES for implementing CE business practices in the footwear industry.

This paper proposes a field-oriented control (FOC) of a rotor-tied DFIG (RT-DFIG) using a high gain observer (HGO). For speed/DC-voltage and current regulation, the FOC employs a Proportional-Integral (PI) controller in both outer and inner loops. The goal is to maximize wind power mining by allowing wind turbines to operate at variable speeds. In addition, by utilizing current/voltage measurements, HGO estimates the mechanical speed of the rotor for FOC deployment. Finally, the simulation results of the suggested method for the 7.5 kW RT-DFIG are provided. The HGO was tested in both static and dynamic wind settings and with parameter modifications. Furthermore, various existing observer-based control methods were compared, which verified the proposed method’s superiority.

Abstract Background Millions of people die every year as a result of antimicrobial resistance worldwide. An inappropriate prescription of antimicrobials (e.g., overuse, inadequate use, or a choice that diverges from established guidelines) can lead to a heightened risk of antimicrobial resistance. This study aimed to determine the rate and appropriateness of antimicrobial prescriptions for respiratory tract infections. Methods This review was conducted in accordance with the PRISMA guidelines. Web of Science, PubMed, ProQuest Health and Medicine, and Scopus were searched between October 1, 2023, and December 15, 2023, with no time constraints. Studies were independently screened by the first author and the co-authors. We included original studies reporting antimicrobial prescription patterns and appropriateness for respiratory tract infections. The quality of included studies’ was assessed via the Joanna Briggs Institute’s Critical Appraisal Checklists for Cross-Sectional Studies. The assessment of publication bias was conducted using a funnel plot and Egger’s regression test. A random effect model was employed to estimate the pooled antibiotic prescribing and inappropriate rates. Subgroup analysis was conducted by country, study period, data source, and age group. Results Of the total 1220 identified studies, 36 studies were included in the review. The antimicrobial prescribing rate ranged from 25% (95% CI 0.24–0.26) to 90% (95% CI 0.89–0.91). The pooled antimicrobial prescription rate was 66% (95% CI 0.57 to 0.73). Subgroup analysis by region revealed that the antimicrobial prescription rate was highest in Africa (79%, 95% CI 0.48–0.94) and lowest in Europe (47%, 95% CI 0.32–0.62). Amoxicillin and amoxicillin-clavulanate antimicrobials from the Access group, along with azithromycin and erythromycin from the Watch group, were the most frequently used antimicrobial agents. This study revealed that the major reasons for antimicrobial prescription were acute bronchitis, pharyngitis, sinusitis, and the common cold. The pooled inappropriate antimicrobial prescription rate was 45% (95% CI 0.38–0.52). Twenty-eight of the included studies reported that prescribing antimicrobials without proper indications was the main cause of inappropriate antimicrobial prescriptions. Additionally, subgroup analysis by region showed a higher inappropriate antimicrobial prescription rate in Asia at 49% (95% CI 0.38–0.60). The result of the funnel plot and Egger’s tests revealed no substantial publication bias (Egger’s test: p = 0.268). Conclusion The prescribing rate and inappropriate use of antimicrobials remain high and vary among countries. Further studies should be conducted to generate information about factors contributing to unnecessary antimicrobial prescriptions in outpatients. Systematic review registration Systematic review registration: CRD42023468353.

Liquefaction can cause significant damage to the built environment; therefore, assessing the liquefaction hazard in a seismically active region is essential to minimize the risk. This study attempted to evaluate the liquefaction potential of the south-central coastal areas of Bangladesh by calculating the liquefaction potential index (LPI) considering a scenario earthquake of Mw ​= ​7.5 having a peak ground acceleration of 0.15g. For calculating LPI, both standard penetration test blow count (SPT-N) and shear wave velocity (Vs) data have been used in this study. The results show that the study area's LPI values vary from 0 to 37. A liquefaction hazard map is prepared for the area using the calculated LPI values from Vs data shows about 8% of the study area is very highly susceptible to liquefaction hazard, whereas 62% of the area falls under high hazard-prone area while about 28% and 2% area of the study have respectively low (0

Introduction: Vagus nerve stimulation (VNS) is clinically useful for treating epilepsy, depression, and chronic pain. Currently, cervical VNS (cVNS) treatment is well-established, while auricular VNS (aVNS) is under development. Vagal stimulation regulates functions in diverse brain regions; therefore, it is critical to better understand how electrically-evoked vagal inputs following cVNS and aVNS engage with different brain regions. Objective: As vagus inputs are predominantly transmitted to the nucleus of tractus solitarius (NTS), we directly compared the activation of NTS neurons by cVNS or aVNS and the brain regions directly projected by the activated NTS neurons in mice. Methods: We adopted the targeted recombination in active populations method, which allows for the activity-dependent, tamoxifen-inducible expression of mCherry—a reporter protein—in neurons specifically associated with cVNS or aVNS. Results: cVNS and aVNS induced comparable bilateral mCherry expressions in neurons within the NTS, especially in its caudal section (cNTS). However, the numbers of mCherry-expressing neurons within different subdivisions of cNTS was distinctive. In both cVNS and aVNS, anterogradely labeled mCherry-expressing axonal terminals were similarly observed across different areas of the forebrain, midbrain, and hindbrain. These terminals were enriched in the rostral ventromedial medulla, parabrachial nucleus, periaqueductal gray, thalamic nuclei, central amygdala, and the hypothalamus. Sex difference of cVNS- and aVNS-induced labeling of NTS neurons was modest. Conclusion: The central projections of mCherry-expressing cNTS terminals are comparable between aVNS and cVNS, suggesting that cVNS and aVNS activate distinct but largely overlapping projections into the brain through the cNTS.