Search Results (5,938)

Limited by the computational performance limits of the CPU(Central Processing Unit), the traditional Spark architecture struggles to achieve high throughput and low latency under the dual pressure of a large data scale and real-time requirements in centralized control systems. This work uses a publicly available CNC(Computer Numerical Control) milling dataset as a functional validation proxy for time-series data processing, then extends validation to a large-scale synthetic power transmission grid dataset. Furthermore, Spark-GPU(Graphics Processing Unit) collaboration suffers from load balancing failure due to heterogeneous resource scheduling and communication overhead, thus failing to unleash its performance potential. This paper proposes a Spark-GPU fusion acceleration technology path. The path consists of three key components: first, it integrates the RAPIDS accelerator; second, it designs a GPU-aware partitioning and task co-scheduling strategy; and third, it optimizes the zero-copy data path. Together, these components realize an integrated collaboration of heterogeneous resources. Validation on real-world datasets yields the following results. In real-time aggregation scenarios, the proposed solution improves throughput by a factor of 3.7 over the pure CPU baseline and reduces end-to-end latency by 62%. Compared with the basic GPU solution, GPU utilization rises from 51.7% to 72.3%, representing a relative improvement of 39.8%. Furthermore, the solution meets industrial-grade high availability requirements. This research significantly improves the processing throughput and reduces end-to-end latency in typical centralized control scenarios, thus providing a feasible technical route for demanding concurrent centralized control scenarios such as electric power industry manufacturing with high real-time demands. Full article

Redevelopment precincts are often assessed through price uplift, although price appreciation alone does not show whether a local housing market becomes more active or liquid. This study examines whether residential turnover and property valuation diverged around the Etihad Campus redevelopment precinct in East Manchester after the 2014Q4 consolidation of the wider campus setting. Using Office for National Statistics House Price Statistics for Small Areas, the analysis applies a neighborhood-scale synthetic control design to a compact Core-4 treatment precinct, using a filtered within-Manchester donor pool to construct the synthetic benchmark. Residential turnover is measured as the mean residential sales count per Lower Layer Super Output Area (LSOA), and valuation is measured as the average of LSOA-level median house-price trajectories. Robustness is assessed using alternative treatment definitions and pre-intervention calibration windows. The results show a persistent post-2014 turnover shortfall relative to the synthetic benchmark, supported by rank-based placebo diagnostics and retained across all valid turnover specifications. By contrast, valuation evidence is weaker, mixed, and more sensitive to design choice. These findings indicate selective housing-market reconfiguration rather than generalized uplift. Redevelopment evaluation should therefore distinguish transaction circulation from price-based valuation, particularly in cumulative precinct-scale redevelopment settings. Full article

Smart cities increasingly rely on distributed sensing and edge intelligence to support urban planning, mobility management, environmental monitoring, and critical infrastructure operation. However, large-scale urban Internet-of-Things deployments are constrained by heterogeneous device capabilities, limited energy availability, variable communication conditions, and data-governance requirements. Federated learning offers a data-locality-preserving alternative to centralized model training, but conventional federated learning strategies often assume full, random, or fixed client participation, which can lead to unnecessary energy consumption, communication overhead, or client starvation in resource-constrained urban environments. This paper proposes an Energy- and Communication-Aware Federated Learning strategy, termed ECA-FL, for smart city sensing systems. The main novelty of the work lies in the joint use of residual device energy and communication conditions to guide adaptive client participation and local training effort, providing a tunable resource–performance trade-off rather than an accuracy-maximizing strategy alone. The framework is evaluated through a controlled simulation-based study using a synthetic multi-class urban sensing proxy task distributed across 100 federated clients under strongly non-IID conditions. Compared with full-participation FedAvg, ECA-FL reduces cumulative energy consumption by 82.9% and communication overhead by 64.7%, while maintaining a final accuracy of 0.8124 compared with 0.8319 for FedAvg-full. Compared with rigid fixed-participation strategies, ECA-FL avoids severe learning degradation by adapting participation dynamically instead of excluding clients according to a static rule. A sensitivity analysis further shows that the trade-off parameter controls the balance between learning performance and resource conservation, allowing the framework to be adjusted according to different deployment priorities. The results support the hypothesis that adaptive energy- and communication-aware participation can substantially reduce operational cost while preserving acceptable learning performance within the adopted simulation setting. The study provides practical design insights for sustainable, communication-conscious, and data-locality-preserving federated learning in smart city sensing infrastructures. Full article

Background: Spinocerebellar ataxia type 2 (SCA2) is an inherited neurodegenerative disorder characterized by progressive cerebellar degeneration. One difficulty in treating this disease lies in identifying preclinical carriers: individuals who carry the pathogenic ATXN2 mutation but remain asymptomatic with respect to motor manifestations. Though magnetic resonance imaging (MRI) has proven valuable in supporting the diagnosis of ataxia, traditional univariate approaches using linear measurements have shown limited ability to capture the complex anatomical changes that occur across the disease spectrum, particularly during the preclinical phase. Methods: This study employed a comprehensive multivariate approach to improve the classification of individuals across the SCA2 spectrum. We developed a multinomial logistic regression model incorporating multiple linear measurements derived from magnetic resonance imaging to discriminate between healthy controls (n = 72), preclinical carriers (n = 17), and patients with manifest SCA2 (n = 61). To mitigate inherent class imbalance, particularly in the smaller preclinical subgroup, we implemented the Synthetic Minority Over-sampling Technique (SMOTE), generating a balanced dataset that enhances the model’s ability to discern the distinctive anatomical features. This was compared to the model applied to the unbalanced data. An improvement was observed when applying SMOTE. Results: The multivariate model demonstrated discriminatory performance, achieving an overall accuracy of 80.7%. The ability to identify healthy controls (AUC: 0.96), preclinical individuals (AUC: 0.75), and clinical individuals (AUC: 95%). This represents an advance over previous univariate approaches, which have had difficulty capturing the neurodegenerative changes characteristic of the preclinical stage. Conclusions: By integrating multiple neuroimaging biomarkers into a multivariable model, this study provides a tool for early identification of preclinical SCA2 carriers. The ability to accurately classify these individuals opens an opportunity for early therapeutic intervention before irreversible neurological deterioration occurs. This approach shows promise for optimizing clinical trial design and personalized care in SCA2. Full article

Polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) are systemic inflammatory diseases deeply rooted in age-related immunosenescence and inflammaging. Conventional long-term glucocorticoid (GC) therapy poses significant metabolic and infectious risks for older adults, necessitating safer alternatives. This review critically evaluates the pathophysiological rationale and clinical efficacy of small-molecule drugs, including Janus kinase inhibitors (JAKi) and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), as steroid-sparing treatments for PMR and GCA. By selectively inhibiting intracellular networks like the JAK-STAT pathway and nucleotide biosynthesis, these agents aim to attenuate maladaptive inflammation. Clinical evidence highlights that JAK inhibitors, particularly upadacitinib for GCA and tofacitinib or baricitinib for PMR, demonstrate the potential to induce remission and significantly reduce the required GC burden in a subset of patients. Although methotrexate remains the primary csDMARD, its modest overall efficacy suggests it should be reserved for patients with definitive contraindications or restricted access to JAK inhibitors. Furthermore, novel therapies like clofutriben demonstrate potential in reversing GC-induced morbidities without compromising disease control. Ultimately, integrating targeted small-molecule immunomodulators establishes a crucial therapeutic paradigm that attempts to maximize clinical remission while safeguarding the physiological integrity of geriatric patients against severe GC toxicities. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Background: Human growth hormone (hGH) deficiency (GHD) is typically treated with daily injections of recombinant human growth hormone (rhGH), which do not fully replicate physiological secretion patterns. This study evaluates a novel approach using synthetic mRNA encoding hGH encapsulated in lipid nanoparticles (LNPs) and designated VTRC-01 to enable endogenous hormone production. Methods: VTRC-01 was administered intramuscularly to hypophysectomized (Hypox) prepubertal Wistar rats, and its efficacy was compared with rhGH. A cohort of healthy rats was included to assess anabolic effects and safety. Results: VTRC-01 stimulated longitudinal growth in both Hypox and healthy rats, achieving effects comparable to rhGH. Treatment induced a significant anabolic response that exceeded the basal growth rate of healthy controls. Conclusions: These findings provide proof-of-concept for hGH mRNA-based therapy as a promising alternative to rhGH. Further improvements in mRNA and LNP technologies are expected to enhance safe hormone production. These promising results underscore the potential of reprogramming via therapeutic mRNA the synthesis of key endocrine regulators (such as hGH) directly within the organism, offering for the first time a powerful pathway for the potential treatment for endocrine therapies targeting growth hormone deficiency. Full article

Nanostructured catalysts have become a core component of energy conversion in electrocatalysis and photocatalysis; however, successfully translating their performance from laboratory scale to industrial applications remains a long-standing challenge. This paper provides a critical assessment of the field, systematically tracing the entire development trajectory from catalyst design to practical application. We focus on five major classes of catalysts—monometallic catalysts, bimetallic/multimetallic alloy catalysts, metal compound catalysts, carbon-based composite catalysts, and single-atom catalysts—and explore synthetic strategies for achieving precise structural control, including hydrothermal/solvothermal methods, electrodeposition, template-assisted and MOF-derived syntheses, high-temperature pyrolysis, and post-treatment defect engineering. This paper delves into the mechanisms and performance descriptors governing the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), urea oxidation, photocatalytic water splitting, and CO₂ reduction. Based on the above analysis, this paper lays the mechanistic foundation for five core strategies to improve catalyst performance: morphology control, elemental doping, heterostructure and interface engineering, defect and vacancy engineering, and support modification. Furthermore, this paper provides an in-depth evaluation of the applications of these catalysts in water splitting, CO₂ valorization, fuel cells, metal–air batteries, and energy-saving electrolysis, with a particular focus on earth-abundant alternatives to precious metals. We argue that in many well-studied reactions, intrinsic activity may no longer be the primary bottleneck restricting their development; instead, the core challenge now lies in maintaining excellent catalytic performance under harsh and industrially relevant conditions, especially under high-current densities, impurity-containing feed systems, and long-term operating conditions. In response to this shift in research focus, this paper clearly identifies the key obstacles hindering the industrial application of catalysts and proposes practical directions for future research. Full article

Early non-invasive correction of neonatal auricular deformities is highly dependent on timely and precise diagnosis. However, clinical practice is often compromised by the subjectivity of visual assessments and the lack of objective tracking metrics, which frequently leads to missed optimal treatment windows. To address these challenges, we developed an interpretable deep learning-based diagnostic system for the automated screening and fine-grained classification of these deformities. Methodologically, a large-scale, multi-source dataset (n = 4644) was curated to support model training. The system pairs an automated object detector (YOLOv11) for background-reduced region-of-interest isolation with a cascaded classification pipeline optimized via ConvNeXt-Tiny. Crucially, we introduced a supervised contrastive learning module to project high-dimensional morphological features into a continuous severity score, enabling quantitative longitudinal tracking of therapeutic efficacy. To evaluate generalization and robustness, the framework underwent rigorous evaluation across three independent real-world cohorts and one controlled synthetic stress test. The system achieved 88.2% accuracy (Area Under the Curve (AUC): 0.949) in binary screening and 87.4% accuracy (macro-AUC: 0.976) in multi-class subtyping on the internal baseline. To enhance interpretability and build clinical trust, Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to explore the spatial distribution of the model’s attention, which frequently aligned with key anatomical landmarks. Furthermore, the learned severity scores robustly quantified post-intervention improvements (p = 0.0004), effectively capturing subtle anatomical normalization. While validation for rare subtypes remains underpowered, and the severity score currently functions mainly as a learned morphological similarity index requiring future clinical calibration, this study ultimately provides an objective and standardized web-based tool to facilitate the early intervention and precision management of neonatal auricular anomalies. Full article

This paper extends a previous classification study by examining clustering methods on the same synthetic datasets and comparing their behavior with the previously obtained classification results. This study investigates the performance of selected time series clustering methods under controlled changes in noise level and class complexity. Six clustering methods representing distance-based, feature-based, and deep learning approaches were evaluated on 82 balanced synthetic datasets. The datasets contained from two to six classes, different levels of additive Gaussian noise, 200 time series per dataset, and 1000 observations per time series. The analysis focused on clustering quality, comparative behavior with classification models, and computational cost in terms of training time and peak memory usage. Clustering quality was assessed mainly using Adjusted Rand Index and V-measure, while accuracy after Hungarian label matching was used as an auxiliary measure for comparison with classification models. The results show that distance-based methods, and particularly TimeSeriesKMedoids, achieved the most robust and consistent clustering performance across the considered settings. Clustering quality decreased with both the number of classes and the noise level, but the effect of noise was clearly stronger. Feature-based and deep learning-based clustering methods were generally more sensitive to noise, while deep models were also associated with substantially higher computational cost. In terms of memory usage, classical clustering methods remained below 50 MiB, whereas deep learning-based clustering methods required substantially more memory. This study further shows that accuracy computed after Hungarian label matching may provide an overly optimistic view of clustering quality. Accuracy after Hungarian label matching is reported only as an auxiliary metric, while the main interpretation of clustering quality is based on structure-sensitive measures such as Adjusted Rand Index and V-measure. Overall, the findings highlight the importance of robust distance-based approaches and of using structure-sensitive evaluation measures when analyzing time series clustering. Full article

Employee attrition is a significant organizational challenge associated with substantial financial costs and the erosion of institutional knowledge. This study presents an AI-based Management Information System (MIS) that integrates machine learning (ML) models to forecast employee turnover and support technical interpretability for HR decision-making. Using the IBM HR Analytics Dataset comprising 1480 employee records with 38 features, we implemented a rigorous preprocessing pipeline—including Synthetic Minority Over-sampling Technique (SMOTE) applied exclusively within training folds to prevent data leakage, one-hot encoding, Z-score normalization, and mean-value imputation. Four ML classifiers—Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost—were evaluated under a stratified 80/20 split with 5-fold cross-validation. XGBoost achieved the highest performance, attaining an accuracy of 87.83%, a ROC-AUC of 0.94, a PR-AUC of 0.96, and an F1-score of 93.04%, attributed to its sequential boosting mechanism and built-in L1/L2 regularization. Beyond predictive performance, the system incorporates SHapley Additive exPlanations (SHAP) to deliver feature-level transparency, enabling HR professionals to engage in proactive, informed retention interventions while retaining full decision-making authority. Within-dataset comparisons confirm that the proposed framework outperforms prior methods evaluated on the same benchmark; cross-study accuracy comparisons are reported as contextual reference only, given differences in datasets and experimental protocols. The system facilitates human oversight by positioning AI as a decision-support collaborator rather than an autonomous replacement in workforce management. Future work will address real-time deployment, controlled user studies with HR practitioners, and validation with actual organizational HR data. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Manual data storage methods on various mobile devices, IoT devices, and traditional computing platforms still lack sufficient security governance due to the absence of a unified security framework. Unlike application controlled environments, manual storage locations such as file systems, removable media, and IoT devices are highly susceptible to unauthorized access, misuse, and exfiltration. To address this problem, the paper proposes a security framework for manual storage systems using metadata, and the proposed framework includes three different algorithms, namely MARShield, ThreshGuard, and SentinelScheduler. These three algorithms operate together to ensure security for manual storage systems. MARShield is used for enforcing immutable metadata, multi-access rights based on tokens, and persistent source tracking by cryptographically securing provenance logs. ThreshGuard, on the other hand, enables the use of adaptive threshold-based misuse regulation and bottleneck-controlled serialized execution. SentinelScheduler optimizes the use of cryptography by incorporating trust-based application profiling and idle-time scheduling for heavy security operations. The proposed methodology is evaluated using a hybrid approach combining real-world datasets (CIC-IoT2023, TON-IoT, Bot-IoT and ISCX VPN non-VPN) and dataset-driven synthetic access pattern generation. Real datasets are used to model realistic IoT traffic behaviors, while additional synthetic scenarios are introduced to evaluate adaptability against evolving and previously unseen attack patterns. Network level features from these datasets are systematically transformed into storage-level access behaviors to evaluate metadata-driven access control. The experimental results indicate improved detection accuracy (94.6%), reduced false positive rate (4.3%), improved misuse control efficiency (92%) and scalability (94%). The proposed methodology for securing manual storage domains is scalable, adaptive, and portable, extending the security of applications and their associated domains. Full article

This study develops exact analytical solutions for transient elliptical groundwater flow toward an extended well in an anisotropic fractured confined aquifer and then discusses how the resulting hydraulic response can support groundwater-head interpretation in shale-gas development areas. The environmental connection is made at the aquifer-protection scale: the model is not a shale-gas reservoir production model, and it does not solve contaminant transport directly. Instead, it provides a hydraulic interpretation framework for estimating anisotropy, equivalent fracture length, wellbore-storage effects, and the preferential direction of head propagation around possible leakage points, old wells, fractures, or monitoring wells. Based on Mathieu-function theory and the separation-of-variables method, constant-rate and constant-head solutions are derived in Laplace space and inverted to the time domain with the Stehfest algorithm. The analytical results are validated against COMSOL5.2 finite-element simulations, and the effects of anisotropy coefficient and wellbore storage are analyzed through drawdown and flow-rate type curves. A synthetic but field-style water-head example is included to demonstrate how monitoring records can be converted to drawdown, fitted to the elliptical-flow solution, and used to delineate a preliminary hydraulic response zone. The results show that anisotropy mainly controls early-to-middle time response, whereas wellbore storage may obscure early head changes and delay the recognition of fracture connectivity. Therefore, the solution is best regarded as a rapid hydraulic-screening and monitoring-design tool that can precede, but not replace, site-specific contaminant-transport modeling in shale-gas groundwater-protection studies. The relevant technical issues are possible head disturbances and preferential groundwater pathways associated with surface spills, flowback-water handling, old wells, faults, and fracture-connected water-bearing zones. Because verified local field-monitoring records were not available for us, the application example is explicitly described as a synthetic field-style demonstration; it is used to show the workflow and its limitations, not to claim site-specific prediction of contaminant concentration. Full article

Polymer coatings are integral to nearly every modern cardiovascular and endovascular device, including drug-eluting stents (DESs) and drug-coated balloons (DCBs), bioabsorbable vascular scaffolds (BVSs), occluders, grafts, and catheter and guidewire hydrophilic surfaces. Persistent complications, including late stent thrombosis, delayed endothelialization, hypersensitivity, and restenosis, show that coatings actively shape biological responses rather than acting as inert drug carriers. Their surface chemistry, drug release kinetics, and degradation behavior are upstream determinants of blood– and tissue–material responses that govern healing and failure. This review frames coating selection as a structure–property–biological response problem. It surveys the major classes of synthetic polymer coatings and the defining surface and bulk properties. This review also examines how composition and architecture control drug release, and traces the interfacial cascade of protein adsorption, coagulation and complement activation, platelet and leukocyte responses, and neutrophil extracellular trap (NET) formation. These mechanisms are linked to contemporary design strategies that improve hemocompatibility, limit thrombosis, promote endothelial recovery, and tune degradation, and to the standardization and translation gaps that remain. The central message is that polymer coatings are not biologically equivalent. Their surface chemistries and degradation profiles determine the thrombo-inflammatory outcomes. Therefore, coating design should be guided by intended biological response, not drug release alone. Full article