Search Results (7,077)

Mangrove microorganisms play a fundamental role in regulating sediment biogeochemical processes, particularly carbon storage and trace element cycling. Although numerous studies have examined microbial roles in individual processes, an integrated understanding of how microbial communities simultaneously regulate carbon storage and trace element dynamics in natural and restored mangrove ecosystems remains limited. This review synthesizes the global status of mangrove microbial research and the influence of microbes on carbon storage, trace element accumulation, and speciation in natural and restored mangrove ecosystems, while identifying emerging research trends and knowledge gaps. Our investigation revealed that research on the influence of microbes on carbon storage in mangrove sediments is increasing globally, with considerably increasing trends after 2017. However, less research has been reported on microbial trace metal interrelations than on carbon storage relationships, suggesting that there is limited focus from researchers on this topic. The available evidence indicates that sulfate reduction, microbial extracellular polymeric substances (EPS), microbial necromass formation, redox-driven biogeochemical coupling, and microbially mediated mineral transformations are the principal mechanisms promoting long-term carbon stabilization in mangrove sediments. Therefore, several studies have also suggested that microbial diversity regulates trace element accumulation and speciation through different pathways, such as redox transformation, bioadsorption, EPS-mediated binding or aggregation, biomineralization, and sulfide precipitation, in mangrove sediment. The principal conceptual contribution of this review is the development of an integrated framework demonstrating that microbial processes act as a central biogeochemical bridge connecting carbon storage and trace element cycling, rather than regulating these functions independently. Finally, we identify critical challenges and research priorities, including functional gene characterization, integrated metal–microbe–plant interactions, multi-omics approaches, long-term monitoring, and global meta-analyses, to improve mechanistic understanding and support evidence-based mangrove restoration and blue carbon management. Full article

This paper provides a comprehensive and systematic review of artificial intelligence (AI) integration in smart logistics management, evaluating seven core technology clusters: machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision (CV), internet of things (IoT), blockchain, and data mining. While the prior literature reviews analyze these technologies in isolation, this study directly addresses the critical research gap of technology fragmentation and integration challenges across the supply chain. Our main contribution is a novel, three-layered conceptual framework that structures smart logistics into interdependent layers: data collection (IoT, RFID, GPS), intelligent processing (ML/DL, NLP, computer vision), and logistics decision-making (route optimization, warehouse automation, risk management). By detailing the theoretical foundations (information processing, dynamic capabilities, and Cybernetics), inter-module correlations, and a phased four-stage deployment roadmap, this review provides a unified, practical blueprint for organizations transitioning from legacy systems to fully autonomous, cognitive logistics networks. Full article

This study presents the first record of Dundubia annandalei Boulard in Bangladesh and assesses its potential range dynamics through MaxEnt modeling under current and future climate scenarios (under the 2041–2026, 2061–2080, and 2081–2100 time periods). A total of 63 specimens, collected from various locations during field surveys in February–March 2025, were identified based on morphological characteristics. The species distribution model exhibited excellent predictive performance (AUC = 0.967). Precipitation of the warmest quarter (64.4%), elevation (10.8%), and precipitation of the driest month (9.1%) were identified as the most influential environmental factors. Under current climatic conditions, the suitable area is predicted to cover 861,481 km², representing 5.02% of the studied region. Future projections suggest range contraction (SSP585, 2041–2060) and expansion under all chosen emissions scenarios (SSP126 and SSP585), but significant expansion under extreme warming (SSP585), especially during 2061–2080 and 2081–2100. The potential distribution may extend to Bhutan, China, Myanmar, Laos, Nepal, Thailand, Vietnam, Malaysia, Indonesia, and the Philippines. Under climate change scenarios, the results highlight the potential for this species to emerge as a major agricultural concern in the future. Full article

The migratory shad, Hilsa (Tenualosa ilisha) is an iconic species of profound economic and cultural value across the Indian sub-continent due to its delicious taste and significant contributions to gross domestic product (GDP). Lack of fundamental genomic data regarding sex determination, impedes development of optimized breeding techniques and target conservation goals. In this study, a next-generation sequencing (NGS)-based genotyping technique was applied to identify sex-linked markers, modes of sex determination, putative sex-determining genes and the population genomic structure of Hilsa. Genotyping of 94 Hilsa individuals (46 males and 48 females) collected from four distinct locations of Bangladesh (three different river systems and Bay of Bengal as a marine site) revealed 31,696 single-nucleotide polymorphisms (SNPs) and 12,754 presence/absence (PA) loci. Among these SNPs and PA, we identified 20 SNPs that were heterozygous in females but homozygous in males and 4 PA loci which were only present in females. Therefore, this study conclusively identifies a female heterogametic (ZZ/ZW) sex determination system in Hilsa. Comparative BLAST analysis using sex-linked loci against Hilsa genomes resulted in the identification of five candidate genes potentially involved in sex-determination pathways. Moreover, population genetic analysis revealed low spatial genetic differentiation among the four sampling sites but notable divergence between males and females (minimum 1.8–2.8% variation in principal coordinate analysis). For most of the sampling sites, higher observed heterozygosity (Ho) compared to expected heterozygosity (He) is the indicative of a robust population status with minimal evidence of inbreeding. Our study provides a baseline for further improving the management and conservation of the wild populations of the species. Full article

The PR-IHC-40X dataset comprises a high-resolution collection of region-of-interest (ROI) images and corresponding ground-truth (GT) annotations for progesterone receptor (PR) immunohistochemistry (IHC) analysis in breast cancer pathology. We obtained 50 glass slides from the University of Malaya Medical Centre (UMMC) and digitized them into whole-slide images (WSIs) at 40× magnification using a 3DHistech Pannoramic DESK scanner. Pathologists annotated ROIs on the collaborative Cytomine platform, which formed the basis of dataset extraction. Ground-truth masks were generated in a multi-stage process: binary nuclei masks for segmentation were first created with a StarDist deep learning model and refined by manual correction, while the classification ground truth was first determined using a CNN-based approach and then modified by diaminobenzidine (DAB) intensity thresholding into four expression classes: Strong (red), Moderate (yellow), Weak (green), and Negative (blue). The classification outputs were re-corrected in a loop against the pathologists’ feedback and the manually checked results. There were approximately 32,000 nuclei within 250 ROI images that were manually checked and validated by senior pathologists individually. Each ROI comes with its binary segmentation mask and four-class color annotations, which make it a reliable dataset for deep learning research on nuclei segmentation, PR expression classification, and Allred scoring. To ensure a fair and reproducible evaluation, the dataset is released with a predefined slide-level (patient-wise) partition into training, testing, and evaluation subsets so that no slide contributes regions of interest to more than one subset and data leakage across subsets is avoided. Full article

Background: An estimated 25 million children globally miss routine vaccinations each year, with 14.3 million classified as zero-dose and the majority residing in low- and lower-middle-income countries (LMICs). Pharmacies are recognised as accessible healthcare delivery points, yet their role in paediatric immunisation in LMICs is not well defined. This literature review sought to evaluate pharmacist-provided vaccination services for paediatric patients in LMICs. Methods: A systematic review was conducted following Cochrane and PRISMA guidelines. Five databases and grey literature were searched for articles published between January 2005 and June 2026. Studies were reviewed by multiple authors to reduce the risk of individual bias and included if they reported original research involving pharmacists in LMICs delivering vaccination-related services to children aged 12 years and under. For each study, items intended for extraction included country, pharmacy setting, specific vaccines administered, patient age range, and specific roles for the pharmacist in the vaccination administration process. The protocol for this systematic review was registered with the Nelson Mandela University Faculty of Health Sciences Postgraduate Studies Committee and the Research Ethics Committee for Humans (H23-HEA-PHA-007). Results: Of 162 identified records, four studies from Bangladesh, Ethiopia, India, and Jordan met inclusion criteria. No study described pharmacists routinely administering paediatric vaccines. Instead, findings focused on indirect involvement, system readiness, and public perception. Significant barriers included limited infrastructure, inadequate training, and lack of regulatory authority. Study results were limited by a lack of information published and inclusion of only articles available in English. Conclusions: Evidence on pharmacist-administered paediatric vaccinations in LMICs is scarce. Expanding pharmacist roles could improve access and reduce zero-dose prevalence, but would require policy support, infrastructure investment, and further implementation research. Full article

Background: Digital behavioral phenotyping of autism spectrum disorder (ASD) offers a promising approach for developing more scalable diagnostic frameworks across diverse global contexts. Machine learning (ML) models show promise for ASD diagnosis using behavioral videos, but critical questions remain regarding whether models trained on data from one country work in another, and how the background of the raters affects the accuracy. Our work addresses these questions by testing whether ML models can accurately diagnose ASD across different populations and rater groups. Methods: This work evaluates the performance of a supervised ML framework for binary classification of ASD versus non-ASD [speech, language and communication disorders (SLC) + neurotypical (NT)] in a cohort of 227 children in Bangladesh. We first assessed the cross-domain model transferability of a clinical-instrument-trained logistic regression model (LR-9) on behavioral ratings that were based on videos of Bangladeshi children interacting with caregivers and toys at two major child development centers in Dhaka, Bangladesh. We then trained five diverse classifiers (Logistic Regression, Random Forest, XGBoost, SVM, and RuleFit) on the full annotated Bangladeshi dataset. Using SHAP-based consensus elbow feature selection, we identified a compact set of features that maintained the performance. Finally, we developed ensemble models to improve predictive stability. Results: The LR-9 model, originally trained on U.S. clinical instrument data, was evaluated on video-based behavioral ratings from 214 Bangladeshi children. When tested on Bangladeshi clinician ratings, the LR-9 model achieved a sensitivity of 86.1% (95% CI: [0.78–0.93]) and AUC of 0.79 (95% CI: [0.73–0.86]). The distinction across rater groups was between trained raters (clinicians and students) and crowd workers, who showed lower sensitivity 28.5% (95% CI: [0.21, 0.39]). When tested on the aggregated ratings from all groups, the model achieved an AUC of 0.78 (95% CI: [0.72–0.84]). Inter-rater reliability followed the same pattern: individual agreement was fair (Krippendorff’s α = 0.26), but the multi-rater consensus was reliable (ICC(1,k) = 0.84), with Bangladeshi clinicians showing the highest agreement (α = 0.34) and crowd workers the lowest (α = 0.20). We then trained new models directly on the Bangladeshi ratings. All model types achieved similar AUC values (0.86–0.89), with overlapping confidence intervals. Using just 8–11 key behaviors kept the similar performance while cutting the features by 66–75%. Combining ensembles gave similar results (e.g., Bayesian averaging: AUC 0.88 [0.78, 0.95]) but with more stable predictions. Conclusion: This study provides evidence that mobile video-based ASD diagnosis can achieve comparable performance (AUC: 0.89 [0.76, 0.96]) to models trained on clinical instrument data. This work contributes to the development of broader adaptable autism detection tools, bypassing the dependence on traditional clinical instrument data. Full article

Due to the rapid development of the textile industry and increased consumption of various textiles composed of both synthetic and natural fibers, large amounts of textile waste are produced, leading to environmental and economic problems on a global scale. Turning textile waste into carbon materials that can be used in a broad range of applications has become a viable solution to address this challenge in terms of sustainability and value generation. Natural and synthetic textile fibers have distinctive molecular structures with relatively high carbon content and variable chemical functionality; therefore, they have been identified as highly promising precursors for fabricating carbon materials with various electrochemical and environmental applications. At the same time, the properties of carbonized and activated textile fibers are strongly dependent on the molecular transformations taking place during thermal treatment and functionalization of textile fibers. This review will provide a comprehensive overview of the molecular evolution of natural and synthetic textile fibers during carbonization and activation processes in terms of dehydration, depolymerization, aromatization, heteroatom preservation, and graphitization mechanisms. The effect of precursor chemical composition, pyrolysis conditions, activation process, and heteroatom incorporation on the structure of carbonized and activated textile fibers and their physical and electrochemical properties will be analyzed. Particular emphasis is placed on electrochemical applications, including capacitive deionization, supercapacitors, electrocatalysis, and emerging smart electrochemical textile systems, highlighting how molecular transformation, pore engineering, and surface chemistry govern charge storage, ion adsorption, and catalytic behavior. In addition, major characterization techniques such as Raman spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller surface area analysis will be reviewed and discussed in relation to understanding the interdependence between molecular structure and material properties. Finally, recent issues related to feedstock heterogeneity, scalability, energy efficiency, and sustainability of processing are highlighted, and future perspectives on multifunctional carbon structures and circular utilization of textile waste are discussed. Full article

Achieving energy sustainability is a major challenge across all sectors of an economy. In agri-food systems (AFSs), this challenge is twofold in the context of energy sustainability; one challenge is to reduce fossil fuel dependency, and another challenge is to provide safe and nutritious food for humans and animals. An appropriate aggregate indicator needs to be used for assessing the energy sustainability of AFSs. This study presents a systematic literature review aimed at developing a core indicators framework to support both research and policymaking. It also critically evaluates and updates existing indicators currently used to evaluate energy sustainability in AFSs, considering environmental, economic, social and technical dimensions. Environmental indicators, such as energy use efficiency, net energy balance, carbon footprint, exergy destruction and renewable energy share, measure the ecological impacts of agricultural practices. Economic indicators, including energy cost share, levelized cost of energy, energy return on investment, and productivity per unit of energy input, reflect how energy use influences farm competitiveness and resilience. Social indicators such as equitable access to energy, labor productivity gains, and system resilience to energy shocks highlight the broader human and societal aspects of energy sustainability. Technical indicators, including technology performance, maintenance intensity, technology lifespan, and resource circularity, emphasize the need to minimize resource depletion and environmental harm. Together, these sustainability indicators provide a strong framework for assessing trade-offs and guiding policy decisions that support the transition toward low-carbon, resilient, and resource-efficient AFSs. Their effective development and implementation require interdisciplinary knowledge integration and active engagement with relevant societal stakeholders in a transdisciplinary sustainability context. Full article

Hypoxia-driven regulatory mechanisms play a critical role in tumor progression and therapeutic resistance in hepatocellular carcinoma (HCC), yet hypoxia-responsive microRNAs (HRMs) remain incompletely characterized. This study aimed to identify HRMs in canine HCC to evaluate their diagnostic potential and translational relevance to human disease. Next-generation sequencing of two canine HCC cell lines under normoxic and hypoxic conditions identified 332 and 321 differentially expressed miRNAs, respectively. Integrating these with tumor tissue data revealed 11 HRMs, featuring consistent upregulation of cfa-miR-210 and cfa-miR-34a, which was validated via RT-qPCR in hypoxic cells and clinical tissues. Both miRNAs were significantly elevated in plasma-derived extracellular vesicles (EVs), highlighting their value as promising circulating biomarkers (AUC 1.00 for miR-210; 0.98 for miR-34a). Target gene and pathway analyses identified shared regulatory nodes, including TGIF2 and SPRED1; and enrichment of MAPK, Ras, PI3K-Akt, and Rap1 signaling, broadly linked to hypoxia adaptation, cellular metabolism, and stress-response signaling. Cross-species comparison with human HCC datasets showed that, while miR-210 associates with poor prognosis in human HCC, miR-34a exhibits tumor-suppressive features. These findings define complementary HRM programs in canine HCC, reflecting conserved adaptive and stress-limiting regulatory mechanisms with potential diagnostic and translational relevance to human HCC. Full article

This paper examines how the travel intention of students is correlated with their mental health, in terms of stress reduction and psychological wellbeing. The study employs a combined model that integrates the Theory of Planned Behavior and Stress Recovery Theory to determine the role of travel intention. Travel self-efficacy, the pressure of social influence, the value of the travel experience, the perception of affordability, and the attitude towards tourism determines travel intention, leading to stress reduction and psychological wellbeing of students. The paper examines the mediating capacities of travel involvement and social connectedness and the moderating capacities of nature connectedness and travel frequency. Semi-structured interviews with 15 university students and a survey among 604 students in various universities in Bangladesh were used to collect data. Hypotheses were tested using PLS-SEM. Findings indicate that travel intention enhances psychological wellbeing and reduces stress, and travel involvement and social connectedness are important mediators. These relations are mediated by nature connectedness and frequency of travel, which increases the restorative effect of travel. The results indicate that tourism could be a viable approach to student mental health promotion, which has both practical and theoretical consequences on university policy and future tourism psychological studies. Full article

The increasing interest in natural versus synthetic additives is a driving force for food industry innovation to develop alternative solutions for food safety and quality. Brown algae, described as containing potential antimicrobial and antioxidant compounds, are promising alternative sources. However, optimized food-grade extracts require preserving their bioactivity. In this study, the effects of seasonality and interannual variation, together with non-thermal high-hydrostatic-pressure (HHP) pretreatment, on the extraction of antioxidant and antimicrobial compounds from the macroalga Ericaria selaginoides, collected from the northwest of Spain between November 2021 and September 2023, were assessed. The HHP pretreatment of fresh algae did not significantly change the yield of the crude extracts and only slightly improved the antimicrobial activity of the extracts against L. monocytogenes, S. aureus and B. cereus, without significantly diminishing the antioxidant activity until 600 MPa for 5 min, the total polyphenol content (TPC), and the chlorophyll A content. Interannual and seasonal variations significantly influenced pigments and protein, carbohydrate and polyphenol contents, together with the antioxidant and antimicrobial activities, of the extracts. By NMR, phloroglucinol was identified as the major secondary metabolite, together with mannitol, alanine and a mixture of meroditerpenoids, which may contribute to the reported activities. The overall results demonstrated the role of environmental factors in driving seasonal and interannual changes in macroalgal metabolism, significantly influencing the bioactive properties of extracts, while the effect of HHP pretreatment was minimal. Full article

Dairy cattle are particularly susceptible to welfare challenges that can negatively impact their behaviour, physiological responses, milk yield, and overall health. These challenges include rough handling by unfamiliar people, extreme climatic conditions, inconsistent feeding, regrouping, and transportation. This pilot study investigated the interrelationships between behavioural and physiological responses and milk production of dairy cows reared in a smallholder stall housing system, restricting cow movement to standing and lying down in the Rajshahi district of Bangladesh. Fifty cows were observed, with all cows assessed over two days. Video recordings were made using a smartphone during the morning milking routine, focusing on specific behaviours: ear position (upright, forward, backward, downward), sniffing, stepping, and tail movement. Observations were conducted during the first two minutes after the start of the milking process manually. Physiological responses recorded included rectal temperature, vaginal temperature, and heart rate. Productivity outcomes include body condition score (BCS), milk yield per milking, daily milk yield, and milking frequency. Environmental data were collected to calculate the Temperature-Humidity Index (THI), which averaged 84.5 (±1.78 SD), exceeding the thermal comfort zone for dairy cattle (THI < 72). Statistical analyses comprised principal component analysis (PCA) to determine the most descriptive variables. Pearson correlation assesses the direction and strength of relationships between variables, and simple linear regression to evaluate milk production variables using all other variables as predictors. Results showed that cows displaying an upright ear position during milking had a positive relation with average daily milk yields (r = 0.456, p = 0.001). Milk yield was negatively correlated with THI (r = −0.331, p = 0.02), forward ear position (r = −0.356, p = 0.01), kicking (r = −0.531, p < 0.001), and sniffing behaviour during milking (r = −0.511, p < 0.001). The daily milk yields were positively correlated with body condition scores of milking cows (r = 0.661, p < 0.001). Rectal and vaginal temperatures were positively associated with each other (r = 0.777, p < 0.001). Similarly, rectal temperature and heart rate were positively associated with each other (r = 0.511, p < 0.001). Therefore, ear position, kicking, and sniffing behaviour during milking were used to assess emotional states, with forward ears associated with signs of stress. Healthy cows with higher body condition scores (BCS) produced more milk, requiring farmers to milk them more than twice a day. This pilot study highlights key behaviour and physiological responses relevant to stress and milk production, and thus overall welfare. For greater generalisability, future research should incorporate larger sample sizes and repeated environmental assessments over extended periods. Full article

Trichoderma species are widely investigated and commercially applied as eco-friendly biocontrol agents in sustainable agriculture. These filamentous fungi protect plants through multiple complementary mechanisms, including mycoparasitism, antibiosis, competition for nutrients and ecological niches, and induction of systemic resistance in host plants. These activities are mediated by a diverse array of secondary metabolites, hydrolytic enzymes, and signaling pathways that collectively suppress pathogens and enhance plant health. Beyond disease control, selected Trichoderma strains promote plant growth by improving nutrient acquisition, modulating phytohormone signaling, and increasing tolerance to abiotic stresses. This review summarizes recent advances in the mechanisms underlying Trichoderma spp. mediated biocontrol, with particular emphasis on secondary metabolites, formulation strategies, commercialization, and field applications. Commercial products are available in various formulations, including wettable powders, granules, and liquid preparations, and have demonstrated efficacy against several economically important plant diseases under field conditions. However, their performance remains highly dependent on strain characteristics, host species, environmental conditions and agricultural practices, resulting in inconsistent efficacy across agroecosystems. Recent progress in genomics, transcriptomics, and metabolomics has substantially improved our understanding of Trichoderma–plant–pathogen interactions and revealed considerable strain-specific variation in biocontrol and plant growth-promoting traits. Future research should prioritize strain-specific optimization, formulation stability, microbiome-informed applications, and improved field predictability. Overall, Trichoderma spp. Represents a valuable component of integrated disease management, offering an effective and sustainable alternative to synthetic pesticides. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article