Search Results (6,497)

This perspective examines the interactions among microplastics (MPs), emerging contaminants (ECs), climate change, and extreme hydrological events. Droughts, water scarcity, heatwaves, intense rainfall, and floods can alter the occurrence, mobilisation, transport, fate, and risks of MPs and associated ECs across water, soil, and groundwater systems. The analysis focuses on the context-dependent potential of MPs to act as vectors of ECs, soil–water interactions, and the potential influence of MPs on greenhouse gas (GHG) emissions. When these stressors co-occur, their combined effects may be additive, synergistic, or antagonistic. Accordingly, the “perfect storm” framing is used here to describe the potential for mutually reinforcing and cascading risks rather than to imply that synergy occurs universally. An integrated perspective therefore helps anticipate worst-case scenarios and move beyond the fragmented assessment of individual stressors. This paper discusses sustainable mitigation and adaptation strategies, including advanced wastewater treatment, water reuse, climate-resilient water management, infrastructure adapted to increasing hydrological variability, climate-smart agriculture, and safer alternatives to conventional plastics and chemicals. Effective implementation combines technological innovation with monitoring, governance, policy action, and public awareness. By framing the microplastics–contaminants–water–soil–climate nexus as an interconnected sustainability challenge, this work aims to support environmental resilience and progress toward the Sustainable Development Goals. Full article

Reliable clinical decision support systems require not only high predictive accuracy but also trustworthy probability estimates and robust uncertainty quantification. However, most medical artificial intelligence (AI) studies primarily emphasize discrimination performance while overlooking systematic reliability evaluation. This study proposes a reliability-aware evaluation framework for Alzheimer’s disease detection that integrates discrimination analysis, statistical validation, probability calibration, uncertainty quantification, robustness assessment, and clinical decision analysis within a unified pipeline. Multiple machine learning classifiers and ensemble configurations were evaluated using repeated stratified cross-validation and assessed through discrimination and calibration metrics. Support Vector Machine achieved the highest ROC-AUC (0.955 ± 0.046), while Extra Trees obtained the highest Accuracy (0.878) and F1-score (0.887). Friedman analysis confirmed statistically significant differences among classifiers ($p<0.001$). Platt scaling consistently improved probabilistic reliability, whereas Beta calibration demonstrated stable performance under noise, feature perturbation, and reduced-data scenarios. Uncertainty-aware selective prediction increased high-confidence diagnostic accuracy by up to 8.1%, and decision curve analysis demonstrated improved clinical utility. The reliability analysis identified calibration-aware stacking as the most reliable ensemble configuration. An independent cross-dataset evaluation on a heterogeneous Alzheimer’s disease clinical dataset with a substantially different feature space yielded stable discrimination (ROC-AUC = 0.858 ± 0.025) and calibration (ECE = 0.132 ± 0.019) after the STACK_CAL architecture was independently retrained from scratch. These findings provide evidence of the cross-dataset applicability of the proposed reliability-aware strategy across different clinical data modalities, while further prospective and independent validation remains necessary before real-world clinical deployment. Full article

In modern industrial settings, flexible production pipelines are increasingly essential to accommodate customized products and small-batch orders under dynamic operational conditions. Traditional execution and reasoning pipelines deployed at edge nodes typically rely on fixed topologies, where computation, memory access, and control flows are statically predefined. These rigid designs are ill-equipped to handle process variations or dynamic task scheduling, leading to execution discontinuities, state inconsistencies, and suboptimal runtime responsiveness in embodied systems. This paper proposes a sensor-driven, context-aware adaptive reasoning framework for embodied industrial production systems. The framework integrates heterogeneous industrial sensor perception with causal dependency graphs to dynamically reconfigure reasoning paths according to changing tasks, resource states, and real-time sensor observations. Multi-dimensional context embedding vectors generated from multi-modal sensor data are maintained in a lightweight context memory layer, ensuring seamless cross-node state continuity and efficient data migration. Furthermore, a predictive task scheduling model constructs and dismantles temporary execution chains on demand while continuously updating the global topology model. Experimental results demonstrate significant improvements in reasoning continuity, context consistency, and real-time task responsiveness compared with traditional fixed-topology reasoning pipelines. By tightly coupling sensor perception, context-aware reasoning, and adaptive execution, the proposed framework demonstrates the potential to support adaptive and distributed execution for next-generation embodied intelligence systems operating in dynamic industrial environments. Full article

Background: Psychological flourishing is a key indicator of optimal mental health and professional well-being, particularly among psychologists who are routinely exposed to emotionally demanding clinical environments. Although self-compassion has consistently been associated with positive psychological outcomes, few studies have integrated traditional statistical methods with machine learning approaches to predict psychological flourishing among psychologists. Objective: This study aimed to examine the relationship between self-compassion and psychological flourishing among psychologists in Saudi Arabia, identify the unique contribution of self-compassion dimensions, evaluate the predictive performance of supervised machine learning models, and compare their performance with traditional multiple linear regression. Methods: A cross-sectional correlational design was employed, involving 224 psychologists practicing in Saudi Arabia. Participants completed the Self-Compassion Scale and the Flourishing Scale. Descriptive statistics, Pearson’s correlation, and multiple linear regression analyses were performed using IBM SPSS Statistics version 29.0. In addition, Random Forest Regression and Support Vector Regression (SVR) models were implemented in Python using scikit-learn version 1.8.0 to predict psychological flourishing based on self-compassion dimensions together with demographic and professional characteristics. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Results: Overall, self-compassion was positively associated with psychological flourishing (r = 0.627, p < 0.001). Multiple linear regression showed that self-kindness had a significant positive independent association with psychological flourishing (β = 0.292, p = 0.001), whereas over-identification had a significant negative independent association (β = −0.206, p = 0.009). The regression model explained 40.5% of the variance in psychological flourishing (R² = 0.405, p < 0.001). In the held-out test-set comparison using the same predictor set, predictive performance was similar across multiple linear regression (R² = 0.273; RMSE = 3.583; MAE = 2.849), Random Forest Regression (R² = 0.282; RMSE = 3.562; MAE = 2.771), and Support Vector Regression (R² = 0.272; RMSE = 3.587; MAE = 2.768), with no substantial predictive advantage of the machine-learning models over the linear benchmark. Conclusions: Self-compassion, particularly self-kindness and over-identification, was significantly correlated with psychological flourishing among psychologists. Machine-learning models demonstrated predictive performance comparable to traditional regression, with no substantial predictive advantage over the linear benchmark, indicating that increased model complexity did not improve out-of-sample prediction in the present sample. Full article

The integration of artificial intelligence (AI) into medical practice is a paradigmatic transformation that compels a reconsideration of the very conceptual structure of bioethics. We argue that this transformation is the local symptom of a wider mutation we designate the anthropological singularity: the threshold at which humanity withdraws from natural evolution and enters an artificial, self-directed one, along three converging vectors—the virtualisation of social space, the cognitive and relational reconfiguration of the human agent through interaction with AI, and the modification of the biological substratum through synthetic biology and gene editing. This approach must be seen as a conceptual study, and not as a systematic literature review, since the study does not aggregate empirical findings but constructs them by defending a theoretical framework and drawing on medical sociology, medical anthropology and biolaw, in order to examine the recurrent tensions between transparency vs. performance, justice vs. efficacy, precaution vs. innovation. We argue that algorithmic bias is part of socio-technical ecosystems, being a systemic property of these. We also argue that responsibility must migrate towards distributed frameworks between human and non-human agents, and that the digital revolution could produce an algorithmic stratification of populations, now prolonged through gene editing into the very biology of future generations. An augmented bioethics is required—not mere delegation of moral judgement to algorithms, but a reflexive framework that revises the ontological presuppositions of principlism, sustained by multi-stakeholder governance and deliberate public policies. Full article

Vector-borne plant pathogens exhibit a remarkable capacity to colonize distantly related hosts, reflecting long-term ecological and evolutionary interactions among pathogens, plants, and insect vectors. Phytoplasmas, which are obligate bacterial parasites transmitted by hemipteran insects, represent a model system for investigating how host–pathogen associations diversify across ecological and evolutionary timescales. Despite extensive knowledge of phytoplasma diversity and vector relationships, the historical processes shaping these associations remain poorly understood. Here, we investigated the eco-evolutionary dynamics of global phytoplasma–hemipteran associations using complementary cophylogenetic approaches that integrate phylogenetic relationships, divergence time estimates, and host association data. We tested the hypothesis that community assembly of phytoplasma–vector networks over the last ~300 million years has been driven by multiple processes, including host switching, lineage duplication, and cospeciation, with additional influences from environmental change and historical biogeography. Our analyses revealed limited evidence for cospeciation across the evolution of the associations. Instead, duplication, host switching, and lineage losses were the predominant processes shaping current associations, with more than 90 inferred incongruent links between phytoplasmas and their insect hosts. A significant cophylogenetic signal was detected only between Psyllidae and phytoplasma group 16SrX, suggesting a history of relative ecological stability or specialization following their concurrent emergence in the Late Cretaceous. Reconstruction of the origins of phytoplasma associations with particular families of insect vectors indicates that the common ancestor of modern Cicadellidae may have already acquired an association with phytoplasmas, but that associations with other vector families were acquired more recently. These findings indicate that phytoplasma–vector associations are largely dynamic and shaped by ecological opportunity rather than strict codiversification. Integrating evolutionary history with ecological interactions provides a framework to better understand the emergence, persistence, and potential future spread of vector-borne plant diseases. Full article

Vaccine responses differ substantially among individuals and across populations. Although factors such as age, genetics, and vaccine type are recognized contributors, they do not fully explain this heterogeneity. Emerging evidence suggests that the human microbiota, particularly the gut microbiota, may modulate immune responses to vaccination and represents a potentially modifiable component of immunity. This review integrates data from human studies, microbiota-targeted clinical trials, and experiments using germ-free and humanized models to clarify the mechanisms underlying microbiota–immune system interactions during vaccination. Identified mechanisms include pattern-recognition receptor signaling, modulation of innate immune activation, regulation of germinal-center responses, maintenance of mucosal barrier integrity, and the influence of microbial metabolites on T and B lymphocytes. The relevance of these pathways varies by age, developmental stage, and vaccine platform, including live-attenuated, inactivated, subunit, viral-vector, and mRNA vaccines. Additional factors such as diet, antibiotic exposure, infections, medications, and environmental or social determinants also affect both the microbiota and vaccine outcomes. Current research explores approaches to improve vaccine potency through microbiota modulation using probiotics, prebiotics, synbiotics, postbiotics, and engineered microbes, though clinical results remain inconsistent. A greater understanding of microbiota–vaccine interactions may enable personalized immunization strategies; however, further research is required to establish causality and identify actionable microbial targets. Longitudinal studies using multi-omics, advanced cellular analyses, robust clinical trials, and in silico modeling are essential to determine whether microbiome-based interventions can improve vaccine efficacy and durability. Full article

Adaptive and multi-functional nozzle actuation is highly demanded for modern aero-propulsion systems. Realizing integrated thrust vectoring, variable exit-area adjustment and reverse-thrust generation within a compact layout still poses substantial challenges. Inspired by archerfish’s integrated flow-regulation mechanism, which relies on coordinated oral bones and muscles to generate asymmetric oral deformation for jet shaping, direction control and flow-passage switching, this paper develops a nozzle actuation mechanism drawing on the kinematics of the archerfish jaw-operculum system. The design integrates pitch vector adjustment, variable exit-area modulation (full-closure included), and reverse-thrust generation. A single electric-cylinder-linkage assembly drives the nozzle exit, while lead-screw actuators govern the operculum-mimicking flow-diversion structure for reverse-thrust switching. Validated by kinematic analysis and three-dimensional flow-field simulations, the mechanism delivers a maximum continuous geometric pitch deflection of 36° within 8 s and stepless exit-area adjustment ranging from 1.8 × 10⁴ mm² to full closure. It enables active modulation of jet mixing behaviours and reverse-thrust output for landing deceleration. Compared with conventional nozzles, this bionic configuration achieves a streamlined actuation layout, competitive vectoring performance, continuous area adjustability and reduced control complexity, offering an innovative bionic solution for adaptive aero-propulsion devices. Full article

Outdoor pedestrian wind comfort and indoor natural ventilation are important aspects of wind environment performance in high-rise residential developments. However, the two objectives are evaluated in different spatial domains and may exhibit inconsistent responses to changes in design parameters. Identifying solutions that balance both objectives through conventional trial-and-error design becomes increasingly difficult as the number of variables and candidate combinations increases. This study develops a multi-objective optimization method that integrates parametric modeling, computational fluid dynamics (CFD), and a hybrid NSGA-II–MOPSO algorithm to coordinate outdoor and indoor wind performance. Building positions, orientations, and window locations are represented by a 13-dimensional design vector. Outdoor pedestrian wind comfort and indoor natural ventilation are quantified using two objective functions, DCTC_out and DCTC_in, defined as the ratios of evaluation points outside the prescribed comfort ranges to those within them. Indoor simulations use façade pressures obtained from the outdoor CFD calculations as boundary inputs. The method is applied to a planned high-rise residential development in Chongqing, China. The optimization used a population of 25 candidate designs over 50 generations, yielding 1250 CFD-evaluated designs. The population mean values of DCTC_out and DCTC_in decreased by 16.25% and 17.62%, respectively, while their best-so-far values reached 2.14888 and 0.52469. Nine solutions remained on the final first non-dominated front, representing different trade-offs between outdoor and indoor performance. Compared with the outdoor-priority solution, the compromise solution increased DCTC_out by only 3.81% while reducing DCTC_in by 22.81%. Further improvement in indoor performance was accompanied by a substantially greater deterioration in outdoor performance. The results support the joint consideration of residential layout and opening design when outdoor and indoor wind performance are evaluated simultaneously. Full article

Cities are the main sites of production, employment, and capital accumulation in advanced economies, which places urban economic performance at the centre of economic policy and urban governance. This work develops an integrated grey-based multi-criteria approach for assessing that performance and applies it to the sixteen G7 cities covered by the Global Power City Index (GPCI). Criterion weights are obtained with Grey RANCOM (G-RANCOM), which converts the ordinal rankings of a five-member expert panel into interval weights, and the cities are ranked with Grey MUNRA (G-MUNRA), which aggregates linear, vector, and non-linear normalization. Each performance entry is an interval bounded by the minimum and the maximum annual score observed in the GPCI Economy function over 2021–2025. Market size, economic vitality, and business environment emerge as the most influential criteria, and New York, London, and Tokyo occupy the highest positions, while Osaka, Milan, and Fukuoka occupy the last three. Robustness is tested via scenario analyses on the model parameters and through a global analysis of 100,000 replications in which all of them vary jointly. New York holds the first position in 74% of the replications and the three lowest positions are unchanged in 87%, whereas cities in adjacent middle positions are not separated reliably. Rankings produced by five established grey approaches, by crisp and fuzzy counterparts, and by the published GPCI Economy rankings agree with the reported ordering, with Spearman correlations between 0.92 and 1.00. The framework offers urban policymakers a transparent benchmarking tool for evidence-based competitiveness strategies. Full article

Sensitive detection of treatment-associated Raman spectral alterations in tumor tissues remains challenging, particularly when such changes are not readily apparent from conventional morphological evaluation. Here, we developed a label-free Raman biosensing strategy combined with explainable machine learning to characterise treatment-associated spectral signatures in melanoma tumours. A B16-F10 melanoma-bearing mouse model was used to compare untreated and PBS-treated controls with cohorts receiving immune checkpoint blockade, anti-angiogenic intervention, or combination therapy. Raman spectra were acquired from multiple spatial regions of melanoma tissues and analyzed using nonlinear dimensionality reduction, supervised classification, and SHAP-based feature interpretation. Although cohort-averaged spectra showed substantial overlap, multivariate analysis revealed treatment-dependent spectral organization, with the combination-treatment cohort showing the most compact and distinguishable spectral profile. Supervised models, including convolutional neural networks, support vector machines, and k-nearest neighbors, further supported the reproducibility of treatment-associated Raman signatures when evaluated using mouse-level validation strategies. SHAP analysis identified discriminative Raman features mainly located within lipid, phospholipid, ester, protein, and collagen-associated vibrational domains, suggesting potential contributions from metabolic- and extracellular-matrix-related biochemical components to treatment-associated spectral discrimination. These findings indicate that Raman spectroscopy integrated with explainable machine learning provides a sensitive, label-free method for distinguishing treatment-associated spectral differences among melanoma tissues. The proposed approach may serve as a complementary spectroscopic tool alongside conventional histological and molecular analyses for investigating treatment-associated tissue-state alterations. Full article

This paper proposes Phase-PCANet, a new image representation method for fingerprint liveness detection. Phase-PCANet integrates both local and global phase features. The local phase, capturing fine-grained edges and textures, is extracted via short-time Fourier transform with singular value decomposition; the global phase, encoding the holistic structural layout, is obtained from a full-image Fourier transform. These two phase components are separately fed into an improved PCANet, which employs dual binary coding, i.e., scalar-based intra-channel coding and vector-similarity-based inter-channel coding, to preserve within-channel structures and cross-channel correlations. Multi-stage features from different PCA layers within each phase path are aggregated, and the resulting deep features from both paths are concatenated to form the final image representation. Experiments on the LivDet 2011, 2013, and 2015 databases verify the effectiveness of the proposed method. Full article

Severe dengue remains an important cause of pediatric hospitalization, morbidity, and mortality in tropical and subtropical regions, particularly where rapid triage and pediatric critical-care capacity are limited. This structured narrative review synthesizes evidence on the epidemiology, pathogenesis, early recognition, diagnosis, management, and prevention of severe dengue in children. Severe disease reflects interactions among viral factors, pre-existing immunity, dysregulated host responses, and microvascular endothelial injury. Antibody-dependent enhancement, inflammatory mediators, dengue nonstructural protein 1, and endothelial glycocalyx disruption contribute to vascular hyperpermeability, plasma leakage, shock, severe bleeding, and organ impairment. Because deterioration often occurs abruptly around defervescence, serial clinical assessment, hematocrit trends, urine-output monitoring, and timely recognition of warning signs are central to risk stratification. Molecular assays and NS1 antigen testing are most useful during the early febrile phase, although diagnostic performance varies with illness timing and immune status. Carefully titrated isotonic crystalloid therapy remains the cornerstone of treatment; both delayed resuscitation and excessive fluid administration may worsen outcomes. Reducing mortality requires integrated clinical and public-health strategies combining standardized pediatric management, accessible diagnostics, effective referral systems, vaccination where appropriate, surveillance, and vector control. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

The dq-axis coupling effect significantly influences the vector control performance of permanent magnet synchronous motors (PMSMs), and traditional proportional-integral (PI) control cannot control the system as desired. Extensive studies have addressed this coupling effect. However, traditional evaluation methods based on pole–zero distribution are not intuitive, which is unfavorable for engineers to select and apply decoupling strategies. This paper employed singular value analysis to assess coupling trends and magnitude–frequency characteristics of the system. In consideration of the effects of rotor speed, parameter estimation error, and digital delay, a comprehensive evaluation of controllers was conducted. This evaluation included feedforward, feedback, and internal model control (IMC) decoupling, with the analysis facilitated by singular value plots. Under the parameters chosen in this paper, the feedforward decoupling controller improves slightly in decoupling under time delay, but higher rotor speed degrades its performance more. The feedback decoupling controller worsens as the system’s overshoot rate surges from 19.4% to 33.4% under the effect of time delay, though it can decouple completely under ideal conditions. It is evident that the efficacy of all strategies is diminished in the presence of parameter estimation deviation. In contrast, the internal model decoupling controller shows better decoupling capability and stronger robustness, while its overshoot remains under 24%. The effectiveness of the proposed method was confirmed through a combination of simulations and experiments. Full article