Search Results (554)

Urban air quality assessment is central to environmental sustainability and public health management. This study presents a structured comparative evaluation of Random Forest (RF), Support Vector Machine (SVM), LSTM, and Bi-LSTM models for pollutant-driven air quality classification under the Indian National Air Quality Index (NAQI) framework defined by CPCB guidelines. To provide a fair comparison, multi-pollutant data of Indian urban monitoring stations were preprocessed, and the class-balancing protocol and validation protocol were combined. RF had highest total accuracy (0.9971) in the held-out set, with Bi-LSTM (0.9615), LSTM (0.9495), and SVM (0.9442) coming next. Although ensemble methods proved to be very separable in line with the threshold-based NAQI structure, Bi-LSTM was more stable when it came to boundary-sensitive switches among the adjacent severity classes. Calibration analysis (multiclass Brier score: 0.08) showed consistent probabilistic behavior and interpretation, and using SHAP showed physically significant pollutant driving factors. The results explain the appropriateness of comparative models in organized AQI classification and present a reproducible assessment framework for the NAQI framework. Full article

Background/Objectives: Dupilumab-related ocular surface disease (DROSD) is a significant safety challenge in atopic dermatitis (AD) management, potentially leading to treatment interruption despite cutaneous efficacy. This narrative review evaluates risk stratification and management strategies to standardize monitoring and preserve long-term drug persistence. Methods: A search of PubMed/MEDLINE was conducted from inception to 31 December 2025. Evidence was synthesized from clinical trials, pooled safety analyses, and real-world registries, focusing on risk factors, monitoring tools, and interdisciplinary management algorithms for DROSD in AD populations. Results: Clinical trials identify conjunctivitis as a reproducible, context-dependent signal enriched in AD populations. Real-world data highlight that ocular symptoms disproportionately drive treatment dissatisfaction and discontinuation. Clinical vigilance must extend throughout the treatment course; while many cases appear early, a significant proportion develops between 8–16 weeks, with late-onset manifestations reported up to 12 months after initiation. Effective management relies on baseline risk documentation—including prior ocular history and AD phenotype—and the implementation of stepwise, severity-based “treat-through” protocols. Conclusions: Managing DROSD is a critical strategy for maintaining treatment persistence. Integration of routine baseline risk capture, continuous symptom surveillance, and structured multidisciplinary escalation pathways is essential to maximize ocular safety and long-term therapeutic outcomes in AD. Full article

Continuous-variable quantum key distribution (CV-QKD) enables information-theoretically secure key generation between legitimate parties. To further enhance system performance, an improved two-way CV-QKD protocol has been proposed, which is accessible in practice and exhibits increased robustness against excess noise. However, in practical implementations, device nonidealities inevitably drive the optical field from the single-mode regime into the continuous-mode regime. In this work, we introduce temporal modes to characterize the evolution of optical fields in the improved two-way protocol and establish a security analysis framework for the continuous-mode scenario based on adaptive normalization with calibrated shot-noise unit. In addition, finite-size effects are taken into account in the analysis. Our results demonstrate that the improved two-way protocol retains a performance advantage over its one-way counterpart. The analysis provides useful guidance for the practical implementation and performance optimization of improved two-way CV-QKD systems. Full article

Background: This integrative review investigates how behavioural and psychological factors shape non-market environmental valuation within the scope of sustainable development. Unlike traditional technical-economic approaches, the novelty of this work lies in reframing socio-cultural drivers of pro-environmental behaviours (PEBs) within macro sustainability paradigms and proposing a socially and ethically grounded framework. The review has three objectives: (i) to incorporate psychological and socio-cultural dimensions into the sustainable development agenda; (ii) to demonstrate how values, norms, and perceptions drive PEBs; and (iii) to call for an ethical consensus across socio-economic and environmental sustainability. Methods: The review follows PRISMA 2020 guidelines and synthesises English-language empirical and conceptual studies (2010–2025) from Scopus and Web of Science, supplemented by Google Scholar. The literature search was conducted in December 2025, and rigorous screening and exclusion criteria were applied to ensure methodological reliability. Results: The review includes 69 interdisciplinary studies and 2 reports. The synthesis yields a framework on ethics that integrates psychological, behavioural, and economic perspectives in non-market environmental valuation and informs the weak vs. strong sustainability debate. Discussion: The findings connect sustainability debates to socio-cultural theories to explain how values, norms, and perceptions shape PEBs and valuation-relevant preferences. The review is limited by its integrative (non-meta-analytic) design, which relies on qualitative synthesis and expert judgement across heterogeneous theoretical and empirical traditions; therefore, a formal risk-of-bias assessment was not conducted. The review protocol was registered on OSF (registration ID W9Y8T). Full article

Maritime operations are increasingly reliant on sensor data to drive efficiency and enhance decision-making. However, despite rapid advances in large language models, including expanded context windows and stronger generative capabilities, critical industrial settings still require secure, role-constrained access to enterprise data and explicit limitation of model context. Retrieval-Augmented Generation (RAG) remains essential to enforce data minimization, preserve privacy, support verifiability, and meet regulatory obligations by retrieving only permissioned, provenance-tracked slices of information at query time. However, current RAG solutions lack robust validation protocols for numerical accuracy for high-stakes industrial applications. This paper introduces Lighthouse Bot, a novel Agentic RAG system specifically designed to provide natural-language access to complex maritime sensor data, including time-series and relational sensor data. The system addresses a critical need for verifiable autonomous data analysis within the Artificial Intelligence of Things (AIoT) domain, which we explore through a case study on optimizing ferry operations. We present a detailed architecture that integrates a Large Language Model with a specialized database and coding agents to transform natural language into executable tasks, enabling core AIoT capabilities such as generating Python code for time-series analysis, executing complex SQL queries on relational sensor databases, and automating workflows, while keeping sensitive data outside the prompt and ensuring auditable, policy-aligned tool use. To evaluate performance, we designed a test suite of 24 questions with ground-truth answers, categorized by query complexity (simple, moderate, complex) and data interaction type (retrieval, aggregation, analysis). Our results show robust, controlled data access with high factual fidelity: the proprietary Claude 3.7 achieved close to 90% overall factual correctness, while the open-source Qwen 72B achieved 66% overall and 99% on simple retrieval and aggregation queries. These findings underscore the need for a secure limited-context RAG in maritime AIoT and the potential for cost-effective automation of routine exploratory analyses. Full article

This review develops a materials-to-clinic framework for patient-specific, functionally graded (FG) NiTi shape memory alloy (SMA) rods as a complementary paradigm for scoliosis correction that targets durable alignment with motion preservation. The article synthesizes the thermomechanical basis of NiTi (thermoelastic martensitic transformation, near constant superelastic plateau, and hysteretic damping) while leveraging additive manufacturing (AM) capabilities to spatially program transformation temperatures (e.g., A_f), effective stiffness, and geometric inertia along the rod. Consolidated process–structure–property linkages are provided for the PBF-LB, DED, and BJAM routes, together with contamination and composition-control strategies (mitigation of Ni volatilization; management of O/C uptake; gradient heat treatments) and segment-level quality assurance (DSC mapping, micro-CT, EBSD/indentation, and bench bending/torsion in physiologic media). Building on clinical curve classification, the methodology formalizes a grading mask and target moment vector that drive multi-objective optimization of the segmental A_f, relative density/architecture, and cross-section, followed by route-specific build plans and acceptance tolerances. A phenomenological constitutive description provides the forward map from local design variables to temperature-dependent moment–curvature loops for finite element verification and uncertainty control. Surgical handling and activation policies are codified (cold shaping in martensite and controlled intra-/postoperative warming within tissue-safe bounds), and a translational roadmap is outlined, encompassing prospective calibration of classification-to-design mappings, AM process maps with in situ monitoring, digital twin planning, and long-horizon fatigue/corrosion protocols. The proposed graded structures provide an adaptive transformation temperature gradient and tunable mechanical response, representing an important design direction toward 3D-printed, patient-specific SMA rods for durable, adjustable, and efficient scoliosis correction. Full article

Surface electromyography (EMG) can drive assistive training systems in neurorehabilitation. This systematic review and meta-analysis evaluated whether EMG-driven device-assisted rehabilitation improves upper-limb (UL) and lower-limb (LL) outcomes versus conventional therapy (CT). The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and was registered in PROSPERO (CRD420251029642). We searched databases for randomized controlled trials in adults with neurological disorders; three reviewers screened records, extracted data, and assessed risk of bias using the Revised Cochrane risk-of-bias tool (RoB 2). Seven trials (n = 160) were included, all in post-stroke populations (UL: 3; LL: 4). UL trials showed mixed findings, and pooled effects were imprecise and not statistically significant for activities of daily living (ADL) (standardized mean difference, SMD −0.55; p = 0.09; I² = 0%). LL pooled estimates showed no significant differences in motor function (Fugl-Meyer Assessment, lower extremity, FMA-LE) (mean difference, MD −1.69; p = 0.40), walking independence (Functional Ambulation Categories, FAC) (MD −0.24; p = 0.61), balance (SMD 0.12; p = 0.61), mobility (Timed Up and Go, TUG) (MD −3.24; p = 0.71), or endurance (SMD −0.19; p = 0.43). Current evidence does not demonstrate clinical superiority over CT. EMG-driven systems may be used as an adjunct, but larger trials with standardized protocols, implementation outcomes, and neurological pathologies beyond stroke are needed. Full article

Ophiocordyceps xuefengensis is an important medicinal fungus with considerable pharmaceutical and economic value. However, its industrial and scientific utilization has been severely limited by the lack of an efficient genetic transformation system, largely due to limited genomic information and wild growth. In this study, we established an efficient and stable plasmid transformation system within O. xuefengensis protoplasts mediated by PEG. To overcome low protoplast yield and transformation efficiency, key factors influencing protoplast preparation including enzyme composition and concentration, fungal age, and digestion conditions were systematically optimized. The optimal protocol involved digesting 4-day-old mycelia with a mixture of 1.5% lywallzyme 1 and 1.5% snailase at 34 °C and 130 rpm for 3.5 h, yielding at least 9.42 × 10⁷ CFU/mL protoplasts. Protoplast regeneration was significantly enhanced in PY medium supplemented with 0.6 M mannitol. Under these optimized conditions, a transformation efficiency of 45.5% was achieved, with stable plasmid integration confirmed over four successive generations. Furthermore, the transformation system was successfully applied to functional gene characterization by driving exogenous gene expression using the endogenous gpd1 promoter. This study provides a foundational platform for functional gene analysis and paves the way for further elucidation of growth and development mechanisms and metabolic engineering in O. xuefengensis. Full article

Along with the swift evolution of autonomous driving and internet technologies, In-Vehicle Ethernet has evolved into the core backbone network underpinning the new generation of in-vehicle networks (IVNs). Since In-Vehicle Ethernet is susceptible to a host of cybersecurity threats—such as data pilferage, data falsification, and malicious unauthorized access—it is imperative to enhance its defense capabilities. This research focuses on anomaly identification for In-Vehicle Ethernet communication networks, with a specific focus on the intrinsic data features of the AVTP protocol and potential cyber-attack vectors targeting the network. This work develops a novel network anomaly detection approach rooted in the Fuzzy clustering algorithm. This effectively enhances the cybersecurity performance of In-Vehicle Ethernet. Experimental results demonstrate that the Fuzzy clustering algorithm proposed in this study achieves 97.4% accuracy in detecting anomalous data, outperforming the traditional K-Means and OPTICS clustering algorithms by 6.4% and 14.5% respectively in anomaly detection rate. This further elevates the cybersecurity performance of In-Vehicle Ethernet and forges a robust foundation for the stable operation and iterative advancement of intelligent connected vehicles (ICVs). Full article

This paper develops a framework for monitoring and forecasting episodes of systemic financial stress using a combination of market information, macro-financial indicators, and measures derived from time-varying correlation networks, embedded in a sequential machine-learning setting. The contribution is not tied to a single modelling innovation, but rather to the way these ingredients are brought together under an evaluation protocol designed to mimic real-time supervisory use, and to an interpretability layer that makes the resulting predictions easier to inspect. Monthly data covering the period from 2006 to 2025 are used to construct evolving correlation structures and summary indicators of market co-movement. These features are combined with standard predictors and fed into logistic regression, random forest, and gradient boosting models, all estimated in expanding windows and assessed strictly on future observations. Predictive accuracy remains limited, which is consistent with the difficulty of anticipating stress regimes several months ahead at monthly frequency, although gradient boosting attains the highest average AUC across evaluation folds and displays noticeable variation over time. Inspection of SHAP values points to instability in correlation networks, volatility conditions, and short-horizon return behaviour as recurring drivers of the predicted stress probabilities, suggesting that the models draw on information that goes beyond individual market series. Taken together, the results indicate that recurrent statistical regularities and changes in market structure can be exploited for monitoring purposes when models are trained and tested in a sequential fashion. The overall design is intended to be usable in practice and to support supervisory analysis, while remaining transparent enough to allow scrutiny of the signals driving the forecasts. Full article

Directed differentiation protocols have recently been developed to produce stem cell-derived α (SC-α) cells as a potential component of a complete cell-based therapy for T1D, to complement the more widely studied stem cell-derived β (SC-β) cells. Differentiation protocols for SC-β cells produce off-target cell populations implicated in the development of outgrowths in SC-β cell grafts, but outgrowths from SC-α cells have not been explored. This study identifies that engrafted SC-α cells generate outgrowths of similar composition to SC-β cell outgrowths. Both cell types share outgrowth-driving populations marked by SOX9, CDX2, or SOX2. Single-cell RNA sequencing was used to reveal an enrichment in cell-adhesion signaling events in outgrowth-driving populations. Small-molecule inhibition of the Notch pathway was insufficient to disrupt all three outgrowth-driving populations. A comprehensive disruption of cell-adhesion signaling via single-cell dispersion and reaggregation is found to reduce the outgrowth propensity in engrafted SC-α and SC-β cells. Together, these results suggest that disrupting residual progenitor cells with SC-α and SC-β cell clusters can enhance the safety profile of these cell therapy products for T1D therapy. Full article

This paper reviews constitutive models used to predict concrete spalling under elevated temperatures, with emphasis on fire exposure and concrete linings in deep geological repositories for spent fuel and nuclear waste. The review synthesizes (1) how material composition (ordinary Portland cement concrete, geopolymer concrete, and fiber-reinforced systems using polypropylene and steel fibers) affects spalling resistance; (2) how coupled environmental and mechanical actions (temperature, moisture, stress state, chloride ingress, and radiation) drive damage initiation and spalling; and (3) how constituent-scale characteristics (microstructure, porosity, permeability, elastic modulus, and water content) govern thermal–hydro–mechanical–chemical (THMC) transport and damage evolution. We compare major constitutive modeling frameworks, including plasticity–damage models (e.g., concrete damage plasticity), statistical damage approaches, and fully coupled THM/THMC formulations, and highlight how key parameters (e.g., water-to-binder ratio, temperature-driven pore-pressure gradients, and crack evolution laws) control predicted spalling onset, depth, and timing. Several overarching challenges emerge: lack of standardized experimental protocols for spalling tests and assessments, which limits cross-study benchmarking; continued debate on whether spalling is dominated by pore pressure, thermo-mechanical stress, or their interaction; limited integration of multiscale and constituent-level material characteristics; and high data and computational demands associated with advanced multi-physics models. The paper concludes with targeted research directions to improve model calibration, validation, and performance-based design of concrete systems for high-temperature and repository applications. Full article

HER2 testing represents a cornerstone of the treatment algorithm in advanced gastric and gastroesophageal junction adenocarcinoma (GC), yet its evaluation remains complex due to tumor heterogeneity and methodological variability. Unlike breast cancer, HER2 expression in GC is often incomplete and heterogeneous, resulting in discordant results between biopsies, resections, and metastatic sites. Both spatial and temporal HER2 heterogeneity are key determinants of testing reproducibility, diagnostic accuracy, and treatment selection and response in GC. Optimizing sampling through multiple, well-targeted biopsies, standardizing IHC/ISH protocols, and reassessing HER2 status at progression may be crucial steps to ensure diagnostic accuracy. The recognition of HER2-low disease introduces a new pathological and clinical subgroup of GC with potential sensitivity to antibody–drug conjugates, while emerging techniques such as circulating tumor DNA analysis are increasingly applied to detect HER2 amplification and co-existing genetic alterations. Integrating molecular tools and standardized reassessment strategies can enhance HER2 testing reliability and enable more precise treatment strategies, with the potential to minimize HER2 resistance mechanisms. This review provides a practice-oriented guide on the interpretation and optimization of HER2 testing in gastric cancer, while providing insight into the underlying molecular mechanisms driving heterogeneity and resistance. Full article

Aberrant glycosylation is linked to several diseases, making glycoproteins and their glycoforms promising biomarkers. Traditional methods like mass spectrometry offer high sensitivity but are costly, time-consuming, and unsuitable for point-of-care testing. Electrochemical biosensors emerge as an attractive alternative due to their simplicity, affordability, portability, and rapid response. This review focuses on electrochemical strategies developed to assess the glycosylation level of a specific glycoprotein or biological structure rather than merely glycoprotein or cell concentration, as in previous reviews. Approaches include the use of aptamers, boronic acid derivatives, antibodies, and lectins, often combined with nanomaterials for enhanced sensitivity. Applications span the diagnosis/prognosis of several illnesses such as diabetes, congenital disorders of glycosylation, cancer, and neurodegenerative diseases. Innovative designs incorporate microfluidic and paper-based platforms for faster, low-cost analysis, while strategies using dual-signal acquisition or competitive assays improve accuracy. Despite promising sensitivity and selectivity, most sensors require multi-step protocols and lack of validation in clinical samples. Future research should focus on simplifying procedures, integrating microfluidics, and exploring novel capture or detection probes such as metal complexes or metal–organic frameworks. Overall, electrochemical sensors hold significant potential for point-of-care testing, enabling rapid and precise evaluation of glycosylation status, which could drive cell-based biomarker discovery and disease diagnostics. Full article

Medical artificial intelligence (AI) systems depend heavily on high-quality data representations to support accurate prediction, diagnosis, and clinical decision-making. However, the availability of large, well-annotated medical datasets is often constrained by cost, privacy concerns, and the need for expert labeling, motivating growing interest in self-supervised representation learning. Among these approaches, contrastive learning has emerged as one of the most influential paradigms, driving major advances in representation learning across computer vision and natural language processing. This paper presents a comprehensive review of contrastive learning in medical AI, highlighting its theoretical foundations, methodological developments, and practical applications in medical imaging, electronic health records, physiological signal analysis, and genomics. Furthermore, we identify recurring challenges, including pair construction, sensitivity to data augmentations, and inconsistencies in evaluation protocols, while discussing emerging trends such as multimodal alignment, federated learning, and privacy-preserving frameworks. Through a synthesis of current developments and open research directions, this review provides insights to advance data-efficient, reliable, and generalizable medical AI systems. Full article