Search Results (476)

The environmental persistence of antibiotic residues in aquatic systems represents a critical global challenge, with photolysis serving as a primary abiotic degradation pathway. Traditional approaches to studying antibiotic photodegradation and transformation product (TP) identification face significant limitations, including complex reaction mechanisms, multiple concurrent pathways, and analytical challenges in characterizing unknown metabolites. The integration of artificial intelligence (AI) and machine learning (ML) technologies has begun to transform this field, offering new capabilities for predicting photodegradation kinetics, elucidating transformation pathways, and identifying novel metabolites. This comprehensive review examines current applications of AI/ML in antibiotic photolysis research, analyzing developments from 2020 to 2025. Key advances include quantitative structure–activity relationship (QSAR) models for photodegradation prediction, deep learning approaches for automated mass spectrometry interpretation, and hybrid computational–experimental frameworks. Machine learning algorithms, particularly Random Forests, support vector machines, and Neural Networks, have demonstrated capabilities in handling multi-dimensional environmental datasets across diverse antibiotic classes, including fluoroquinolones, β-lactams, tetracyclines, and sulfonamides. Despite progress in this field, challenges remain in model interpretability, standardization of datasets, validation protocols, and integration with regulatory frameworks. Future directions include machine-learning-enhanced quantum dynamics for improving mechanistic understanding, real-time AI-guided experimental design, and predictive tools for environmental risk assessment. Full article

Cr and Platinum-Group Elements (PGEs), critical metallic elements, are mainly hosted in mafic and ultramafic rocks, but determining these rocks’ mineralization age has long been challenging. Zircon, the primary geochronological mineral, is scarce and fine-grained in such rocks, hindering conventional separation techniques (heavy liquid separation, magnetic separation, manual hand-picking) with low efficiency, poor recovery, and significant sample bias. This study develops an integrated workflow: mixed acid leaching enrichment (120 °C), powder stirring for mount preparation, automated mineral identification, and in situ Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA–ICP–MS) dating. Validated on the Xiugugabu diabase in the western Yarlung–Tsangpo Suture Zone (southern Tibet), the workflow yielded weighted mean ²⁰⁶Pb/²³⁸U ages of 120.5 ± 3.3 Ma (MSWD = 0.13) and 120.5 ± 2.0 Ma (MSWD = 3.2) for two samples. Consistent with the published Yarlung–Tsangpo Suture Zone (YTSZ) diabase formation ages (130–110 Ma), these confirm the Xiugugabu diabase as an Early Cretaceous Neo–Te–thys oceanic lithosphere residual recording mid-stage spreading. The workflow overcomes traditional limitations: single-sample analytical cycles shorten from 30–50 to 10 days, fine–grained zircon recovery is 15x higher than manual picking, and U–Pb ages are stable. Suitable for large-scale mafic–ultramafic geochronological surveys, it can extend to in situ zircon Hf isotope and trace element analysis, offering multi-dimensional constraints on petrogenesis and tectonic evolution. Full article

With the accelerated expansion of the Internet of Things (IoT), massive distributed and heterogeneous devices are increasingly exposed to severe security threats. Traditional centralized intrusion detection systems (IDS) suffer from significant limitations in terms of privacy preservation and communication overhead. Federated Learning (FL) offers an effective paradigm for building the next generation of distributed IDS; however, it remains vulnerable to poisoning attacks in open environments, and existing client selection strategies generally lack robustness and security awareness. To address these challenges, this paper proposes an Uncertainty-Guided Evolutionary Game-Theoretic (UEGT) Client Selection mechanism. Built upon evolutionary game theory, UEGT integrates Shapley value, gradient similarity, and data quality to construct a multidimensional payoff function and employs a replicator dynamics mechanism to adaptively optimize client participation probabilities. Furthermore, uncertainty modeling is introduced to enhance strategic exploration and improve the identification accuracy of potentially high-value clients. Experimental results under adversarial scenarios demonstrate that UEGT maintains stable convergence even under a high fraction of malicious participating clients, achieving an average accuracy exceeding 89%, which outperforms several mainstream client selection and robust aggregation methods. Full article

The current mainstream methods for online detection of transformers all have shortcomings such as low sensitivity and susceptibility to interference from the testing environment. Aiming at the shortcomings of the existing online detection methods for transformer winding deformation in terms of feature sensitivity and diagnostic accuracy, this paper proposes a fault intelligent diagnosis method based on high sensitivity multimodal feature fusion. First, the winding deformation experiment is designed for typical fault data, which is obtained to extract multiple frequency and time domain response features and construct a multidimensional feature library. Subsequently, principal component analysis is used to evaluate the sensitivity of each feature to different faults and establish a highly sensitive multimodal feature system. On this basis, a TCN-BiGRU-PHA diagnostic model combining time convolutional network, bidirectional gated loop unit and attention mechanism is constructed to realize accurate identification of winding deformation faults. The experimental results show that the method has higher recognition accuracy under multiple types of faults, which provides feasible ideas and methodological support for realizing online intelligent monitoring of transformer winding deformation. Full article

With the growing adoption of hybrid cloud storage, the identification and protection of sensitive information within large-scale unstructured data has become increasingly challenging. Traditional rule-based and machine learning approaches have limitations in context-aware sensitive data classification and large-scale processing. In this work, a novel framework named LLM-SPSS, implementing a secure and confidential storage layout for distributed hybrid clouds through a fine-tuned XLM-R Base model and multi-dimensional data partitioning, is proposed. First, a fine-tuned XLM-R Base model with adaptive prompt tuning is employed to enable context-aware sensitive data classification and improve detection accuracy. In addition, MapReduce-based distributed processing allows the framework to scale efficiently to large datasets, thus enhancing computational efficiency. Furthermore, a multi-dimensional cloud partitioning scheme provides secure and fine-grained storage isolation within hybrid cloud environments. Experimental results demonstrate that LLM-SPSS achieves an F1-score of 99.66% and yields a 6.3× speed-up over the non-distributed baseline, outperforming traditional rule-based (F1 68.27%), conventional machine learning (SVM F1 98.32%, Random Forest F1 95.79%), and other LLM-based approaches (DePrompt F1 95.95%) and effectively balancing high accuracy with computational efficiency. Full article

Under global environmental change, the health of rivers and lakes on the “Asian Water Tower”—the Qinghai–Tibetan Plateau—is facing mounting pressures. This study examines Qinghai Lake, the Huangshui River, the Golmud River, and the Qinghai reach of the Yangtze River. By integrating the Water Quality Index (WQI) with the AHP–TOPSIS framework, we develop a multidimensional assessment system encompassing water resources, water environment, aquatic ecology, and management functions. The WQI results reveal pronounced spatial heterogeneity in water quality, with conditions ranked as Golmud River > Yangtze River > Huangshui River > Qinghai Lake. Dominant controlling factors also shift from dissolved oxygen in riverine systems to total phosphorus in the lake environment. The comprehensive AHP–TOPSIS evaluation further shows a health ranking of Yangtze River (0.736) > Golmud River (0.602) > Qinghai Lake (0.404) > Huangshui River (0.297), leading to the identification of four distinct management pathways: ecological conservation, natural restoration, nutrient control, and pollution remediation. By moving beyond single-parameter diagnostics, this study provides a robust methodological basis for differentiated river–lake management. The proposed “one river (lake), one strategy” framework, coupled with red-line management recommendations grounded in key indicators, offers direct scientific support for systematic protection and precise governance of aquatic ecosystems on the Qinghai–Tibetan Plateau, contributing to national ecological security and high-level environmental stewardship. Full article

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article

Introduction: Promoting healthy habits in childhood is fundamental for fostering long-term well-being. This study aimed to develop and psychometrically validate an app-integrated instrument to assess knowledge, habits, and attitudes related to health in children aged 8–11, within the context of the MHealth intervention Healthy Jeart. Methods: A quantitative, cross-sectional design was used. An initial item pool underwent expert content validation before being administered to a sample of 623 children from primary education centers in Andalusia, Spain. Construct validity was examined through exploratory and confirmatory factor analyses. Results: The analyses supported a coherent four-factor structure comprising 21 items: (1) Use of technologies, (2) diet and growth, (3) psychological well-being, and (4) physical activity and well-being. The instrument demonstrated satisfactory model fit and internal consistency, providing a multidimensional assessment of children’s health-related behaviors. The sample was recruited from primary schools in Andalusia (Spain), which may limit the generalizability of the findings to other regions and cultural contexts. Conclusions: The validated instrument offers a reliable and efficient means of evaluating healthy habits in children aged 8–11, particularly when embedded within digital interventions such as Healthy Jeart. It represents a valuable tool for educators and pediatric nursing professionals working in school settings, enabling early identification of gaps in health literacy and supporting targeted interventions that promote holistic child well-being. Full article

The resilience of urban transportation networks refers to the system’s ability to resist, absorb, and recover performance when facing external shocks. Traditional methods have obvious limitations in temporal granularity, data fusion, and predictive capabilities. To address this, this study proposes a minute-level real-time resilience measurement model driven by ride-hailing big data. First, the spatio-temporal characteristics of urban ride-hailing data are analyzed, and a transportation cost indicator is introduced to construct a multidimensional road network resilience measurement framework encompassing transport supply–demand, efficiency, and cost. Second, a high-precision hybrid LSTM-Transformer prediction model integrating spatio-temporal attention mechanism is developed, and a time-varying node identification method based on RMSE curves is proposed to accurately capture the disturbance onset time and recovery completion time. Finally, empirical validation shows that, taking Taixing City as an example, the model achieves minute-level resilience measurement with an average prediction accuracy of 96.8%, making resilience assessment more precise and sensitive. The research results provide a scientific basis for urban traffic management departments to formulate emergency response strategies and improve road network recovery efficiency. Full article

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

This study conducted a systematic morphological comparative analysis of reproductive organ structures in 23 bamboo species from Southwest China, focusing on key morphological characteristics including spikelets, florets, lemma, palea, lodicules, pistils, and stamens. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed for multidimensional variable interpretation. The experimental results demonstrated significant interspecific differences in floral organ morphology among bamboo species; these differences not only aided in species identification but also provided morphological support for clarifying the ambiguous taxonomic boundaries within the Bambusa–Dendrocalamus–Gigantochloa (BDG) complex. Spikelet morphology, palea length, and stamen number were identified as core diagnostic indicators for the classification among different bamboo genera. The 11 core traits identified by PCA collectively explained 84.6% of the variation. The LDA further validated the taxonomic reliability of these traits, achieving an overall genus-level classification accuracy of 95.7%. Through quantitative analysis, this research confirmed the critical role of floral morphological characteristics in bamboo classification systems, offering novel morphometric evidence to enhance traditional taxonomic criteria. Full article

Background: Orthorexia nervosa (OrNe) and healthy orthorexia (HeOr) are two distinct but related dimensions of interest in eating behavior research. Evidence regarding their associations with sociodemographic, dietary, and sport-related variables in physically active young adults remains limited. Methods: A cross-sectional study was conducted in 190 physically active young adults (53.2% women; mean age = 23.16 ± 5.13 years). Participants practiced a variety of sports including fitness (25.3%), soccer (13.7%), handball (10.5%), athletics, martial arts, cycling, and other individual or team sports. Although all participants belonged to organized sports teams or structured training groups, 38.9% were not actively competing at the time of data collection. Participants completed validated instruments assessing OrNe, HeOr, and eating-related cognitions, alongside questionnaires on sociodemographic data, dietary habits, sport discipline, training frequency, and supplement use. Hierarchical and K-means clustering were applied using the standardized scores of HeOr, OrNe, and the EHQ total score. Group differences were assessed using t-tests and ANOVA with effect sizes (η²p) reported. Results: Age correlated positively with OrNe, HeOr, and eating-related cognitions, indicating greater consolidation of rigid eating patterns in young adulthood. BMI was associated with OrNe only among men. Vegetarian participants showed higher nutritional knowledge but lower overall orthorexia scores. Supplement users in fitness-related sports reported higher OrNe, whereas participants in collective sports reported lower scores. Three distinct orthorexia profiles were identified, characterized by lower, slightly above-average, and higher scores on orthorexia-related variables. Participants in the higher-scoring profile showed significantly higher EHQ total, OrNe, and HeOr scores compared with the other groups (η²p range = 0.11–0.19). Correlations among orthorexia dimensions were positive and moderate to large. Differences between clusters in sport modality, training frequency, and supplement use underscored the influence of the sporting context. Conclusions: Orthorexia in young physically active adults reflects heterogeneous patterns shaped by the interplay of individual (age, sex, BMI), dietary, and sport-related factors. The identification of differentiated profiles reinforces the multidimensional nature of orthorexia and underscores the relevance of considering specific sport environments when interpreting orthorexic tendencies. Longitudinal research is warranted to examine the stability or variability of these patterns over time and to enable the use of more robust multivariate approaches that further clarify the characterization of orthorexia. Full article

As intelligent systems become increasingly embedded in industrial ecosystems, the demand for transparency, reliability, and interpretability has intensified. This study investigates how explainable artificial intelligence (XAI) contributes to enhancing accountability, trust, and human–machine collaboration across industrial contexts transitioning from Industry 4.0 to Industry 5.0. To achieve this objective, a systematic bibliometric literature review (LRSB) was conducted following the PRISMA framework, analysing 98 peer-reviewed publications indexed in Scopus. This methodological approach enabled the identification of major research trends, theoretical foundations, and technical strategies that shape the development and implementation of XAI within industrial settings. The findings reveal that explainability is evolving from a purely technical requirement to a multidimensional construct integrating ethical, social, and regulatory dimensions. Techniques such as counterfactual reasoning, causal modelling, and hybrid neuro-symbolic frameworks are shown to improve interpretability and trust while aligning AI systems with human-centric and legal principles, notably those outlined in the EU AI Act. The bibliometric analysis further highlights the increasing maturity of XAI research, with strong scholarly convergence around transparency, fairness, and collaborative intelligence. By reframing artificial intelligence through the lens of transparent machine reasoning, this study contributes to both theory and practice. It advances a conceptual model linking explainability with measurable indicators of trustworthiness and accountability, and it offers a roadmap for developing responsible, human-aligned AI systems in the era of Industry 5.0. Ultimately, the study underscores that fostering explainability not only enhances functional integrity but also strengthens the ethical and societal legitimacy of AI in industrial transformation. Full article

To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper proposes an adaptive optimization signal decomposition method combined with dual-modal time-series and image deep feature fusion for variable-speed multi-condition bearing fault diagnosis. First, to overcome the strong parameter dependency and significant noise interference of traditional adaptive decomposition algorithms, the Crested Porcupine Optimization Algorithm is introduced to adaptively search for the optimal noise amplitude and integration count of ICEEMDAN for effective signal decomposition. IMF components are then screened and reorganized based on correlation coefficients and variance contribution rates to enhance fault-sensitive information. Second, multidimensional time-domain features are extracted in parallel to construct time-frequency images, forming time-sequence-image bimodal inputs that enhance fault representation across different dimensions. Finally, a dual-branch deep learning model is developed: the time-sequence branch employs gated recurrent units to capture feature evolution trends, while the image branch utilizes SE-ResNet18 with embedded channel attention mechanisms to extract deep spatial features. Multimodal feature fusion enables classification recognition. Validation using a bearing self-diagnosis dataset from variable-speed hybrid operation and the publicly available Ottawa variable-speed bearing dataset demonstrates that this method achieves high-accuracy fault identification and strong generalization capabilities across diverse variable-speed hybrid operating conditions. Full article

Background: The accelerating global population aging underscores the urgency of addressing public health challenges. Sarcopenia and depression are prevalent, interrelated conditions in older adults, yet prevailing research often treats depression as a static state, neglecting its longitudinal progression and limiting predictive capability for sarcopenia. Methods: Using data from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS), we identified distinct depressive symptom trajectories via Group-Based Trajectory Modeling. Seven machine learning algorithms were employed to develop predictive models for sarcopenia risk, incorporating these trajectory patterns and baseline characteristics. Results: Three depressive symptom trajectories were identified: ‘Persistently Low’, ‘Persistently Moderate’, and ‘Persistently High’. Tree-based ensemble methods, particularly Random Forest and XGBoost, demonstrated superior and robust performance (mean accuracy: 0.8265 and 0.8178; mean weighted F1-score: 0.8075 and 0.8084, respectively). Feature importance analysis confirmed depressive symptoms as a core, independent predictor, ranking third (5.7% importance) in the optimal Random Forest model, only after BMI and cognitive function, and surpassing traditional risk factors like age and waist circumference. Conclusions: This study validates that longitudinal depressive symptom trajectories provide superior predictive power for sarcopenia risk compared to single-time-point assessments, effectively mapping mental health trajectories to physical risk. The robust ML framework not only enables early identification of high-risk individuals but also reveals a multidimensional risk profile, highlighting the intricate mind–body connection in aging. These findings advocate for integrating dynamic mental health monitoring into routine geriatric assessments, demonstrating the potential of AI to facilitate a paradigm shift towards proactive, personalized, and scalable prevention strategies in public health and clinical practice. Full article