Search Results (284)

Introduction: River fragmentation caused by hydropower infrastructure remains a primary threat to aquatic biodiversity, creating a critical need for fish passage solutions that can adapt to high environmental variability. Although adaptive management (AM) has the potential to significantly improve longitudinal connectivity and ecological resilience, its application in real-world fishway operations is currently limited. Objective: This study aims to present and validate a flexible AM framework designed to optimize fish passage by integrating low-cost monitoring systems with automated data processing and predictive modeling. Methodology: The proposed system combines a sensor network for real-time water-level and environmental monitoring with biological performance data obtained through Passive Integrated Transponder (PIT) technology. These data were processed locally using edge computing. Over a two-year period, weekly aggregated data were used to develop Random Forest models to identify the primary drivers of fish movement. Results: The final model successfully identified five key drivers: luminosity, water temperature, and three nested hydraulic parameters at the fishway’s upstream section. Validation at a vertical-slot fishway in Vadocondes (Duero River, Spain) showed that retrospective optimization—specifically adjusting sluice-gate regulation—could increase downstream water levels and reduce drops at the first cross wall. This adjustment demonstrated a substantial increase in predicted fish passage without requiring changes to the hydropower plant’s core operation. Conclusions: The framework is highly flexible and transferable to other regulated river systems. However, its success is contingent upon the definition of clear ecological objectives and the seamless integration of monitoring results into the day-to-day operation of river infrastructure. Full article

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives. Full article

Synthetic aperture radar (SAR) imagery provides all-weather, day-and-night observation capabilities that complement electro-optical (EO) imaging; however, the limited number of operational SAR satellites and the difficulty of acquiring expert-annotated SAR datasets constrain deep-learning-based SAR image analysis. In this paper, we propose EO2SAR-Diff, a conditional latent diffusion framework that translates EO aerial images into realistic synthetic SAR images. The framework comprises three core components: (1) domain-adaptive LoRA pre-training that anchors the Stable Diffusion backbone in the remote sensing domain, (2) a style extraction and injection network that captures SAR-specific visual characteristics via multi-scale feature encoding and parallel cross-attention, and (3) a multi-branch ControlNet with three parallel branches for complementary structural guidance. These components are coordinated by a dual-axis feature injection strategy that modulates conditioning strength along both spatial (per-block) and temporal (per-timestep) dimensions. Experiments on the DOTA 1.0 and SARDet-100K datasets demonstrate that EO2SAR-Diff ranks in the top tier among all compared methods in distributional alignment with real SAR imagery, in terms of FID and KID computed with two SAR-domain-adapted feature extractors. Augmenting the SAR training set with our synthetic images yields consistent improvements in downstream object detection performance, confirming the practical utility of the proposed framework. Full article

Surface electromyography (sEMG) gesture recognition degrades across recording days under domain shift, increasing calibration burden for myoelectric interfaces. Many cross-day adaptation pipelines retrain the deployed recognizer or require labeled target-session data, which can be impractical in assistive-device settings where classifier versions may need to remain locked for traceability and regulatory compliance. We study unsupervised cross-day adaptation under two constraints: the task classifier remains frozen and holdout-day labels are not used when training the adaptor. We propose the Source-Reprojection Module (SRM), a plug-in front end that combines conditional adversarial feature learning with a residual signal-space projector guided by the frozen classifier’s gradients, identity regularization, and latent-space distribution matching, using labeled source days and unlabeled adaptation days only. On a multi-day protocol with four healthy participants (at least five calendar-day sessions per participant, split 3:1:1 into source, adaptation, and holdout domains) and three random seeds per participant (12 runs), mean holdout accuracy increases from 70.9% for the frozen classifier alone to 72.8% with SRM ($+1.98\pm 0.91$ percentage points averaged across subjects). SRM outperforms the frozen baseline in 10 of 12 subject–seed runs. The gain is modest and the cohort is small, so the result supports proof-of-mechanism under the stated protocol rather than population-level clinical generalization. Full article

Background: Regular physical activity induces physiological cardiac remodeling (“athlete’s heart”), which may overlap with pathological hypertrophy. Regional echocardiographic and electrocardiographic data among young African adults are limited. This study evaluated how graded physical activity relates to cardiac structure and function among university students in Ghana. Methods: In this comparative cross-sectional study, 174 apparently healthy students aged 18–30 years were categorized into four physical activity groups in the preceding six months: level 1, no regular exercise (n = 29, 16.7%); level 2, <30 min/day of exercise (n = 41, 23.6%); level 3, 30 to 60 min/day of moderate exercise (n = 29, 16.7%); and level 4, >1 h/day of vigorous exercise (n = 75, 43.1%). Anthropometry, blood pressure, 12-lead electrocardiography, and comprehensive transthoracic echocardiography were obtained. Cardiac indices were compared across activity levels using the Kruskal–Wallis or Welch’s ANOVA test, with post hoc comparisons and regression analyses performed where appropriate. Results: Participants were predominantly male (56.3%), with a mean age of 22.3 ± 3.50 years, BMI of 23.0 ± 4.39 kg/m², systolic blood pressure of 118 ± 13.0 mmHg, diastolic blood pressure of 71.3 ± 9.11 mmHg, and heart rate of 66.9 ± 10.9 bpm. Compared with sedentary participants, those in level 4 had a higher IVSd (9.87 ± 1.61 vs. 8.17 ± 1.47 mm, p < 0.001), LVIDd (43.6 ± 6.96 vs. 40.2 ± 3.58 mm, p = 0.002), LVPWd (10.1 ± 1.95 vs. 8.91 ± 1.60 mm, p = 0.003), and LVM (54.6 ± 7.45 vs. 47.1 ± 6.57 g, p < 0.001). EDV and ESV also increased with activity (90.1 ± 24.8 vs. 69.7 ± 17.8 mL, p < 0.001; 32.4 ± 12.8 vs. 25.6 ± 6.52 mL, p = 0.023). Systolic function was preserved across groups, with an EF of 59.3 ± 4.86% in level 4 vs. 58.3 ± 5.34% in level 1 (p = 0.707). Level 4 participants had a higher SV (57.6 ± 16.7 vs. 46.3 ± 10.4 mL, p = 0.003), CO (3.83 ± 1.17 vs. 3.05 ± 0.70 L/min, p = 0.022), and CI (2.19 ± 0.66 vs. 1.77 ± 0.37 L/min/m², p = 0.015). Bradycardia was most frequent in level 4 (35.8% vs. 18.2% in level 1, p = 0.041), and PR interval was longer in participants exercising ≥30 min/day than in those exercising <30 min/day (166 ± 23.2 vs. 162 ± 21.8 ms, p = 0.031). Conclusions: In young African university students, greater physical activity was associated with mild physiological remodeling, including a higher left ventricular wall thickness, cavity size, and mass, while systolic and diastolic indices remained preserved. The mean values in the most active group were 9.87 mm IVSd and 10.1 mm LVPWd with preserved EF, supporting activity-related adaptation rather than overt pathological hypertrophy and highlighting the need for population-specific cardiovascular interpretation. Full article

The misuse of antibiotics is causing widespread antibiotic resistance, creating an urgent need for new treatment options such as nanoparticle-based therapies. This study aimed to compare silver nanoparticles (AgNPs) produced via green synthesis methods with those made through traditional chemical processes. Furthermore, the study investigated and contrasted the bacterial responses to these two types of AgNPs over a 21-day period of selection pressure using experimental evolution techniques. Analysis using scanning electron microscopy and transmission electron microscopy revealed a consistent, uniform morphology among the AgNPs produced via chemical methods. In contrast, AgNPs synthesized through green methods displayed an irregular morphology. Despite these morphological differences, all nanoparticles from both synthesis approaches were under 100 nm in diameter. These findings were further supported by the absorption spectrum data, which showed a maximum absorption peak between the 400 and 500 nm wavelength range. E. coli exposed to green synthesized AgNPs for 21 days adapted to their presence, exhibiting both enhanced resistance to the green synthesized AgNPs themselves and the development of cross-resistance to ionic silver, a pattern not observed in chemically synthesized AgNP-selected populations. Populations selected using chemical synthesized AgNPs did not develop increased resistance to either chemically or green synthesized AgNPs; however, they showed a slight increase in resistance to ionic silver. Genomics analysis identified polymorphism in genes in a green synthesized AgNP-resistant line including but not limited to the multidrug efflux transporter system (EmrAB), DUF4756 family protein (D1792_RS05680), putative zinc-binding protein YnfU/cold shock-like protein (ynfU/cspB) and imcF-related family protein (D1792_RS10035). Bacterial resistance to chemical AgNPs involves specific polymorphisms in key bacterial components like the RNA polymerase sigma factor (RpoE) and the EmrAB efflux pump. Collectively, the method used to synthesize the AgNPs influences their antibacterial efficacy and the likelihood of bacteria developing resistance. Understanding this interaction is vital for developing effective and resistance-controlled applications of AgNPs across medicine, environmental science, and industry. Full article

Sea level rise poses a major threat to dry beach areas, particularly in low-lying and managed coastal environments. Reliable assessments of future beach vulnerability therefore require the combined consideration of sea level rise, tidal regime, meteorological forcing, and wave-driven processes. Here, a physically based methodology is applied to evaluate future inundation and beach response at five representative sandy beaches along the southern coast of Spain. The selected sites span mesotidal Atlantic and microtidal Mediterranean settings. The approach integrates present-day conditions with sea level rise projections under RCP 4.5 and RCP 8.5 scenarios, astronomical tide, and meteorological residuals. Wave run-up is estimated using the IH2VOF CFD (Computational Fluid Dynamics) model. Extreme still water levels and maximum inundation levels are derived for mid-century (2026–2045) and end-of-century (2081–2100) periods, and their impacts on available dry beach surface and beach width are quantified using cross-shore profiles. Results indicate a progressive reduction in dry beach surface and width across all sites, with impacts intensifying from mid- to end-century and from moderate to high-emission scenarios. While losses remain comparatively moderate under still-water assumptions, the inclusion of wave effects leads to substantially larger impacts. At the most vulnerable sites, dry beach surface losses reach up to 80% under still-water conditions, and up to complete loss (100%) when wave run-up is included, particularly along the mesotidal Atlantic coast. Overall, the results demonstrate that neglecting wave run-up can lead to a substantial underrepresentation of future beach inundation, and that its explicit inclusion provides a more reliable basis for beach management and adaptation planning under sea level rise. Full article

Hulunbuir short-tailed sheep (HSTS) and Hu sheep (HS) exhibit distinct tail phenotypes linked to ecological adaptation, with HSTS carrying a loss-of-function mutation (c.G334T) in the T gene while HS retain the wild-type allele. However, the cellular and molecular mechanisms underlying T-mediated tail development remain unclear. Here, we performed single-cell RNA sequencing on HSTS and HS embryos at embryonic days 16 and 19 (E16 and E19), complemented by cross-species validation using a CRISPR/Cas9 mouse model carrying the same mutation. We identified 12 cell types in E16 HSTS and E16 HS embryos, and 15 cell types in E19 HSTS and E19 HS embryos and found that the MDK_ITGA6+ITGB1 ligand–receptor pair consistently mediated core intercellular communication. The MDK_ITGA6+ITGB1 axis mediates intercellular communication critical for tail bud formation; BMP activation and FGF repression disrupt AER survival, leading to tail shortening. Developmental trajectories showed a shift from early progenitor states at E16 to terminal differentiation at E19. Crucially, HSTS embryos showed transcriptomic signatures consistent with premature AER regression. The T mutation showed transcriptomic signatures of increased BMP pathway activity and reduced FGF8 expression, which may disrupt AER survival and contribute to the short-tail phenotype. In the mouse model, mutant T expression was reduced, and expression dynamics of WNT5B and FGF8 were perturbed, corroborating the sheep findings; however, homozygous T mutation causes embryonic lethality in mice but not in sheep, indicating species-specific differences. This study provides single-cell transcriptomic evidence linking the T c.G334T mutation to premature AER regression in sheep, complemented by cross-species validation in a CRISPR/Cas9 mouse model, offering new insights into the cellular mechanisms of tail development and may provide a basis for future investigations into tail-related breeding markers, pending experimental validation. These changes are associated with AER maintenance and tail outgrowth. Full article

Recently, computational sentiment analysis has become an essential tool for detecting evaluative language in large text collections. However, its application to many low-resource language families and historical corpora remains largely unexplored. This paper reviews the evolution of sentiment analysis methods in the Turkic language family, with a particular focus on Chagatai, the classical predecessor of several modern Turkic languages. We outline the methods that have evolved since the advent of lexicon-based and rule-based approaches up to the present day with large language models, addressing longstanding problems in agglutinative morphology, data scarcity, orthographic instability, and multilingual lexical mixing. To examine the available options, we conducted a pilot experiment using multilingual models in a zero-shot setting on a curated Chagatai corpus. In the absence of ground-truth annotations, prediction stability was validated with ensemble consistency and inter-model agreement. The results show real promise but also distinct limitations when adapting traditional NLP technologies for historically remote, low-resource languages. Progress in the field will require cross-disciplinary work, systematic diachronic dataset deployment, and a nuanced adaptation of multilingual representation learning to handle linguistically rich, low-resource settings. Full article

Background/Objectives: Frailty is well-recognized as a predictor of adverse postoperative outcomes. The Clinical Frailty Scale (CFS) is a widely recommended frailty assessment tool due to its simplicity and rapid bedside applicability; however, it has never been validated in Serbia. The aim of this study was to translate, culturally adapt, and validate the CFS in Serbia, in patients undergoing elective major surgical procedures. Methods: This cross-sectional study included 149 patients aged ≥50 years undergoing elective major urological oncology surgery. Frailty was assessed preoperatively using three scales: the CFS, the Edmonton Frail Scale (EFS), and the FRAIL scale. The CFS evaluations were independently performed by two raters and repeated after 7 days. Concurrent validity was evaluated via Spearman’s correlation between the CFS, the EFS, and the FRAIL scale. The “known-group” construct validity of the CFS was assessed using the test for trends across clinically relevant groups. Both inter-rater and test–retest reliability were assessed using the intraclass correlation coefficient (ICC). Results: The CFS was translated and culturally adapted into the Serbian language in accordance with ISPOR guidelines. The Serbian version of the CFS demonstrated both excellent inter-rater reliability (ICC = 0.957; 95% CI 0.941–0.968), and test–retest reliability (ICC = 0.958; 95% CI 0.943–0.970). A strong positive correlation was observed between the CFS and both the EFS (ρ = 0.698) and the FRAIL scale (ρ = 0.614). A known-group comparison confirmed the construct validity of the CFS. Conclusions: The Serbian version of the CFS is a reliable, valid, and clinically feasible tool for preoperative identification of frailty in patients aged 50 years and older undergoing major elective uro-oncological procedures. Full article

In the field of remote sensing applications, multimodal object detection has emerged as an important technique for enhancing perception robustness in UAV-based scenarios. Nevertheless, RGB–IR UAV detection remains difficult: Degraded illumination destabilizes shallow representations and weakens local discriminative cues, while spatial inconsistencies and fluctuating modality reliability further hinder cross-modal interaction. In addition, existing methods, which often depend on global illumination estimation or simplistic fusion schemes, struggle to jointly maintain contextual stability, reliable cross-modal interaction, and compact discriminative representations in complex aerial scenes. To address these issues, this paper proposes LDSDet, an RGB–IR multimodal UAV object detector for challenging illumination conditions. Specifically, LDSDet integrates three complementary modules: a Long-range Aware Residual Convolution (LARC) module that enhances contextual perception and stabilizes shallow features; a Dynamic Attention-based Cross-modal Fusion (DACF) block that performs spatially adaptive RGB–IR interaction; and a lightweight SeqShuffleGate (SSG) module that suppresses redundant fusion responses to yield compact and discriminative multimodal representations. Extensive experiments on DroneVehicle, FLIR-Aligned, and LLVIP demonstrate the effectiveness of LDSDet, which achieves 85.2% ${\mathrm{mAP}}_{50}$, 45.3% mAP, and 67.1% mAP, respectively, showing strong robustness under day–night alternation, low-light environments, and complex illumination variations. Full article

The possibility of zero-day attacks on Internet of Things (IoT) networks is high, particularly in dynamic and heterogeneous IoT environments, including emerging battlefield scenarios (IoBT). Detecting these attacks requires adaptive and generalizable security mechanisms. Due to the unique and unknown signatures of these attacks, they go undetected using signature-based Intrusion Detection Systems (IDSs) on the one side. On the other side, current anomaly-based IDSs that employ traditional machine learning on statistical features struggle to adapt and generalize to unknown networks, which is the case in IoBT. Transformer-based deep learning models have shown the capability of learning complex sequential patterns. This ability can be leveraged to analyze packet payloads that encompass opcodes capable of executing malicious patterns within an IoT network. In this work, we propose a dual-stage Transformer IDS that operates on the raw payload of network packets to detect zero-day attacks. Due to the lack of IoBT datasets, we evaluate the algorithm on three comprehensive IoT traffic benchmarks—MQTT-IoT, IoT-23, and CIC-IoT-2022—which have a high number of IoT devices and various attacks. Importantly, model evaluation is performed in two cross-validation settings to address the key operational challenges associated with unseen scenarios and networks. The evaluation settings are split-at-scenario to evaluate the detection ability of zero-day attacks and split-at-dataset to evaluate the model’s generalizability to new environments. In the former, the average increase in the F1-score of the proposed algorithm over the baseline model is 44% in detecting four zero-day attacks presented in the MQTT-IoT dataset. In the latter, the average increase in the F1-score is 16% in detecting malicious attacks across the three datasets. These results show the benefit of advanced AI in securing the next generation of IoT systems in future Internet applications. Full article

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R² of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy. Full article

Certain medications may adversely affect health during hot days and heatwaves by altering chronic conditions, comorbidities, fluid balance, or impairing heat adaptation. This study aims to develop evidence-based cross-sectoral recommendations for the safe administration of heat-sensitive medications, compiled into a so-called ‘CALOR’ list (calor: Latin for ‘heat’). Development of the CALOR list will follow a four-pillar process. First, a scoping review of scientific literature and best practices will identify potentially inadequate medications during heat events (heat-PIMs) and adaptation measures, resulting in a first draft. Second, an expert panel will refine this draft through a Delphi process to reach consensus on clinically relevant recommendations. Third, German statutory health insurance (SHI) claims data will be analysed to determine heat-PIMs prevalence; data from Cologne residents will additionally be linked with climate data to investigate health outcomes during heat events. Fourth, thirty health professionals (i.e., medical doctors, nurses, pharmacists) will field-test the CALOR list in summer, providing qualitative feedback on feasibility, leading to further refinement of the CALOR list. To our knowledge this study protocol presents the first study attempting to collate a comprehensive and actionable list of recommendations for drug safety management during hot days and heatwaves. Full article

Field-service operations for utility companies require routing technicians across multiple depots while guaranteeing same-day response to critical infrastructure customers, a constraint that standard multi-depot routing methods cannot structurally enforce. We introduce the MDVRP with Priority Requests (MDVRP-PR), formalised as a lexicographic optimisation problem that guarantees service to priority customers before maximising coverage and minimising route duration. A three-stage pipeline is proposed: hybrid DBSCAN-Hierarchical clustering for topology-aware depot assignment, an Enhanced Max-Min Ant System (MMAS) with priority-driven construction, lexicographic solution selection, and repair, and a Boundary Relocate post-optimisation stage with global cross-depot recovery. The approach is evaluated on a real-world applied case study from Algérie Télécom (Guelma, Algeria), comprising a single four-depot field-service instance scaled to three sizes (55, 90, and 150 customers) and assessed over 2135 controlled runs. On this case study, the proposed clustering method outperforms the MDVRP-adapted Sweep baseline by $22.9$ percentage points on the largest instance (n = 150; Friedman p < 0.001). The priority mechanisms sustain $100\%$ feasibility across all configurations, compared to complete collapse without them ($0/10$ seeds at $\ge 40\%$ priority), at a route-time overhead below $5\%$. Relative to the company’s current manual practice, the framework improves customer coverage by $16.1$ percentage points within 28 s, confirming its practical utility for daily deployment in this capacity-constrained, priority-sensitive routing context. Full article