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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.