Search Results (422)

Amid growing urban climate uncertainty and complex water demand, conventional standalone rainwater harvesting (RWH) systems often fail to ensure supply reliability and overflow control. Most existing studies focus on single-function building clusters, leaving a gap in understanding how functionally diverse groups with complementary demand patterns can be coordinated. This study addresses this gap by applying an hourly water balance model to compare decentralized and coordinated modes for an integrated RWH system serving a campus and adjacent student dormitories in Kitakyushu, Japan. Five performance metrics were evaluated: potable water supplementation, reliability, non-potable replacement rate, overflow volume, and overflow days. The results show that coordinated operation reduced annual potable supplementation by 14.1%, improved overall reliability to 81.7% (a 9.6% gain over decentralized operation), and increased the replacement rate to 87.9%. Overflow volume decreased by 295 m³ and overflow days by five, with pronounced benefits during summer rainfall peaks. Differential heatmaps further revealed distinct spatiotemporal advantages, though temporary disruptions occurred under extreme events. Overall, the study demonstrates that cross-functional coordination can enhance system resilience and operational stability, while highlighting the need for adaptive scheduling and real-time information systems for broader urban applications. Full article

Background: Acute appendicitis is the most common surgical emergency in children, yet timely and accurate diagnosis remains challenging due to nonspecific clinical presentations and limitations of imaging and blood tests. Saliva has emerged as a promising diagnostic medium because it is non-invasive, painless, inexpensive, and highly acceptable for pediatric patients. Salivary biomarkers may provide rapid and child-friendly adjuncts to existing diagnostic pathways. Methods: A systematic literature search was performed in Ovid/MEDLINE, Scopus, Web of Science, and the Cochrane Library to identify studies assessing salivary biomarkers in pediatric appendicitis. Eligible studies included children with suspected or confirmed appendicitis and evaluated the diagnostic accuracy of salivary markers compared to clinical, laboratory, or imaging standards. Results: To date, only three salivary biomarkers have been investigated. Leucine-rich α-2-glycoprotein 1 (LRG1) demonstrated high specificity of 100% but low sensitivity of 35–36%, with diagnostic accuracy ranging from AUC 0.77 to 0.85. C-reactive protein (CRP) showed excellent diagnostic performance with sensitivity of 91.3% and specificity of 95.4% (AUC 0.97), and strong correlation with serum CRP (ρ = 0.96). Irisin showed sensitivity of 90% and specificity of 60% with estimated AUC around 0.75, suggesting potential as an adjunct marker but limited as a standalone test. Conclusions: Salivary biomarkers in pediatric appendicitis are promising but remain underexplored, with evidence limited to small, single-center studies totalling fewer than 300 patients. Their advantages include feasibility, tolerability, and suitability for integration into point-of-care testing. Future research should focus on multicenter validation, development of multi-marker salivary panels, and application of biosensor technologies. With further evidence, salivary diagnostics could complement existing strategies and improve the accuracy and child-friendliness of appendicitis care. Full article

Arrhythmogenic cardiomyopathy (ACM; MIM #107970) is a primitive heart muscle disease characterized by progressive myocardial loss and fibrosis or fibrofatty replacement, predisposing patients to ventricular arrhythmias, sudden cardiac death, and heart failure. Despite advances in imaging and genetics, early diagnosis remains challenging due to incomplete penetrance, variable phenotypic expressivity, and the fact that fatal arrhythmic events may often occur in the early stages of the disease. In this context, the identification of reliable biomarkers could enhance diagnostic accuracy, support risk stratification, and guide clinical management. This narrative review examines the current landscape of potential and emerging biomarkers in ACM, including troponins, natriuretic peptides, inflammatory proteins, microRNAs, fibrosis-related markers, and other molecules. Several of these biomarkers have demonstrated associations with disease severity, arrhythmic burden, or structural progression, although their routine clinical utility remains limited. The increasing relevance of genetic testing and non-invasive tissue characterization—particularly through cardiac imaging techniques—should also be emphasized as part of a multimodal diagnostic strategy in which biomarkers may play a complementary role. Although no single biomarker currently meets the criteria for a standalone diagnostic application, ongoing research into multi-marker panels and novel molecular targets offers promising perspectives. In conclusion, the integration of circulating biomarkers with imaging findings, genetic data, and clinical parameters may open new avenues for improving early detection and supporting personalized therapeutic strategies in patients with suspected ACM. Full article

Leghemoglobin (LegH), a plant-derived hemoprotein, is engineered for the first time as a standalone biocatalyst for carbene N–H insertion. Through semi-rational design, the K65P mutation in the heme pocket significantly enhances catalytic efficiency. Under mild aqueous conditions (PBS buffer, 25 °C), the K65P variant achieves 92% yield in the model reaction between benzylamine and ethyl α-diazoacetate—surpassing wild-type LegH by >1.6-fold in initial reaction rate. The mutant also exhibits markedly improved thermostability. This work establishes engineered LegH as a high-performance, cofactor-free biocatalyst for C–N bond formation, providing a sustainable platform for synthesizing chiral amine derivatives. The catalytic proficiency and inherent stability of the K65P mutant demonstrate the potential of plant hemoproteins in non-natural carbene transfer reactions without requiring immobilization supports. Full article

Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

Background: Carpal tunnel syndrome is a common entrapment neuropathy of the upper limb that has a significant clinical and socioeconomic impact. Sonographic short-axis measurement of the median nerve cross-sectional area is a well-established complement to clinical examination and neurography. This study aimed to evaluate the correlation between the median nerve diameter ratio, distal motor latency, and sensory nerve conduction velocity. Methods: A total of 74 patients (94 hands and 93 evaluations) with carpal tunnel syndrome were examined. Ultrasound was performed using a Siemens Acuson X300 with a 10 MHz linear probe. Median nerve diameters proximal and within the carpal tunnel were measured in a longitudinal scan. The carpal tunnel ratio (proximal diameter/intratunnel diameter) was then calculated and correlated with distal motor latency. Results: No significant correlation was found between distal motor latency and the carpal tunnel ratio (r = 0.018, p = 0.8655). However, a weak, non-significant positive correlation was observed between sensory nerve conduction velocity and carpal tunnel ratio (r = 0.238, p = 0.326). Conclusions: Ultrasound cannot replace electrodiagnostic testing. In this cohort, no statistically significant association was observed between the carpal tunnel ratio and distal motor latency. While our findings do not support the use of this ultrasound parameter as a standalone diagnostic measure, sonographic assessment of the median nerve may still provide complementary information in selected clinical contexts. Full article

Background: Oxytocin (OXT), a neuropeptide involved in social bonding, has been proposed as a potential biomarker for autism spectrum disorder (ASD) due to its role in modulating social behaviors. However, prior studies on peripheral OXT levels in individuals with ASD have yielded inconsistent results, partly due to methodological and developmental variability. This study aimed to compare baseline salivary OXT concentrations between children with ASD and typically developing controls. Methods: In this cross-sectional, case–control study, salivary OXT levels were measured in 35 children aged 6–9 years (18 with ASD, 17 controls) using a standardized ELISA protocol. Samples were collected under controlled conditions and analyzed in duplicate. Between-group differences in raw and log-transformed OXT levels were examined using t-tests. Subgroup analyses were conducted by sex, and correlations with autism symptom severity (Aberrant Behavior Checklist, ABC) were assessed within the ASD group. Results: Children with ASD showed higher mean salivary OXT levels than controls (21.5 pg/mL vs. 14.0 pg/mL), but the difference was not statistically significant (Welch’s t = −1.79, p = 0.088). Log transformation of OXT values confirmed the non-significant group difference (t = 1.68, p = 0.102). Female participants with ASD had significantly higher OXT than female controls (p = 0.048), while no difference was observed among males. OXT levels did not significantly correlate with autism severity (r = −0.04, p = 0.88). Conclusions: Baseline salivary OXT levels do not significantly differ between children with and without ASD and do not correlate with behavioral symptom severity. However, elevated OXT in females with ASD warrants cautious interpretation and further investigation. Salivary OXT may not be a reliable standalone diagnostic biomarker but could have exploratory value for understanding sex-specific neurobiological profiles in autism. Full article

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability. Full article

Non-Intrusive Load Monitoring (NILM) aims to disaggregate a household’s total aggregated power consumption into appliance-level usage, enabling intelligent energy management without the need for intrusive metering. While deep learning has improved NILM significantly, existing NILM models struggle to capture load patterns across both longer time intervals and subtle timings for appliances involving brief or overlapping usage patterns. In this paper, we propose a novel GRU+BERT hybrid architecture, exploring both unidirectional (GRU+BERT) and bidirectional (Bi-GRU+BERT) variants. Our model combines Gated Recurrent Units (GRUs) to capture sequential temporal dependencies with Bidirectional Encoder Representations from Transformers (BERT), which is a transformer-based model that captures rich contextual information across the sequence. The bidirectional variant (Bi-GRU+BERT) processes input sequences in both forward (past to future) and backward (future to past) directions, enabling the model to learn relationships between power consumption values at different time steps more effectively. The unidirectional variant (GRU+BERT) provides an alternative suited for appliances with structured, sequential multi-phase usage patterns, such as dishwashers. By placing the Bi-GRU or GRU layer before BERT, our models first capture local time-based load patterns and then use BERT’s self-attention to understand the broader contextual relationships. This design addresses key limitations of both standalone recurrent and transformer-based models, offering improved performance on transient and irregular appliance loads. Evaluated on the UK-DALE and REDD datasets, the proposed Bi-GRU+BERT and GRU+BERT models show competitive performance compared to several state-of-the-art NILM models while maintaining a comparable model size and training time, demonstrating their practical applicability for real-time energy disaggregation, including potential edge and cloud deployment scenarios. Full article

Background/Objectives: This study aimed to investigate the association between the position of impacted mandibular third molars and the morphology of the lingual cortical bone using cone beam computed tomography (CBCT), and to determine how impaction depth and angulation influence the risk of lingual cortical perforation. Methods: CBCT scans of 120 impacted mandibular third molars from 71 adult patients were retrospectively evaluated. Teeth were classified based on Pell & Gregory’s and Winter’s classifications. Lingual cortical morphology was categorized as undercut, parallel, slanted, or round. The relationship between the root apex and the lingual plate was classified as non-contact, contact, or perforating. Linear measurements included cortical lingual bone thickness and the distance from the apex to the outer surface of the lingual cortex. Results: Lingual bone morphology showed significant associations with both impaction depth and angulation, with parallel morphology more common in deeper and more angulated impactions. Lingual cortical perforation was observed in approximately 30% of the teeth, predominantly at the apex, with horizontal and deeply impacted molars (Class II, Level C) representing the highest-risk configurations. Although cortical thickness and apex-to-cortex distance were significantly smaller in apically perforated cases, no definitive threshold could be established, and these parameters were insufficient as standalone predictors at the cementoenamel junction or mid-root levels. Conclusions: Tooth angulation and impaction depth are significant predictors of lingual bone morphology and perforation risk. CBCT imaging is therefore recommended beyond low-risk cases (Level A, Class I, vertical) to improve preoperative planning, strengthen informed consent, and guide surgical strategies aimed at minimizing complications and enhancing patient safety. Full article

The increasing demand for high-efficiency cooling technologies necessitates improved methods to prevent degradation and ensure reliable operation of lithium–ion batteries. Conventional PCM (phase change material)-based cooling systems are limited by low thermal conductivity and uneven phase change processes, which lead to non-uniform thermal distribution and diminished performance. In response to these challenges, this study introduces a hybrid thermal management system that combines an indirect liquid-cooling structure with multiple cooling channel configurations within a PCM-based battery pack. Numerical simulations were conducted to systematically assess the thermal performance of the proposed design. Experimental validation with various cooling media showed that PCM achieved the greatest reduction in temperature (47%) and the longest isothermal duration (56 min) under air-cooled conditions, surpassing thermally conductive adhesive (40%) and silicone oil (26%) for temperature decrease. Vertical temperature differentials were effectively reduced, staying below only 2 °C for silicone oil and reaching a maximum of 4 °C for PCM. Phase change evaluation indicated that after 30 min of operation, only 37% of the PCM volume had melted, highlighting localized constraints in heat transfer. Comparative analysis among four liquid-cooling channel arrangements (A–D) and a standalone PCM system demonstrated that configuration D exhibited the highest cooling capability, lowering the battery surface temperature by as much as 9 °C (17.8%). Flow rate analysis determined that increases above 0.2 L/min resulted in only modest thermal improvements (<1 °C), with 0.108 L/min identified as the most efficient rate. Relative to PCM-only designs, the advanced hybrid cooling system achieved significantly enhanced thermal regulation and temperature uniformity, underscoring its promise as a superior solution for lithium–ion battery thermal management. Full article

Non-thermal technologies (NTTs) such as ultrasound (US), pulsed electric fields (PEF), high-pressure processing (HPP), cold plasma (CP), and pulsed light (PL) are emerging as versatile tools in food fermentation, offering microbial control and process enhancement without the detrimental heat effects of conventional methods. Operating at ambient low temperatures, these techniques preserve heat-sensitive compounds, modulate microbial activity, and improve mass transfer, enabling both quality retention and functional enrichment. Recent studies highlight their potential to stimulate metabolic pathways and enhance the release of bioactive compounds, opening new opportunities for fermented food production. The bibliometric analysis of the recent literature further reveals a growing interest in NTT applications in fermentation, with HPP and PEF showing the highest industrial maturity. Each technology exhibits distinct mechanisms and optimal niches across upstream, midstream, and downstream stages: HPP for uniform volumetric treatment, US for fermentation intensification, CP for surface-selective oxidative chemistry, PEF for membrane permeability control, and PL for rapid, residue-free decontamination. While the degree of industrial readiness varies, critical barriers such as scale-up limitations, high capital costs, energy distribution uniformity, process standardization, and techno-economic feasibility remain to be overcome. Beyond technical aspects, the successful commercialization of NTTs will also depend on addressing regulatory approval pathways, ensuring consumer trust and acceptance, and demonstrating their contribution to sustainability goals through lower energy use, reduced food waste, and environmentally responsible processing. Strategic, stand-alone, or hybrid applications of NTTs can therefore act not only as technological alternatives but also as enablers of a more sustainable, consumer-centered, and innovation-driven food system. Full article

Air compressors are vital across various sectors—automotive, manufacturing, buildings, and healthcare—as they provide pressurized air for air suspension systems in vehicles, supply power pneumatic machines throughout industrial production lines, and support non-clinical infrastructure within hospital environments, including pneumatic control systems, isolation room pressurization, and laboratory equipment operation. Ensuring that such components are reliable is critical, as unexpected failures can disrupt facility functions and compromise patient safety. Predictive maintenance (PdM) has emerged as a key factor in enhancing the reliability and operational efficiency of medical devices by leveraging sensor data and artificial intelligence (AI)-based algorithms to detect component degradation before functional failures occur. In this paper, a predictive maintenance solution for condition monitoring and fault prediction for the exhaust valve, bearings, water pump, and radiator of an air compressor is presented, by comparing a hybrid deep neural network (DNN) as a feature extractor and a support vector machine (SVM) for condition classification: a pure DNN classifier as well as a standalone SVM model. Additionally, each model was trained and validated on three devices—NVIDIA T4 GPU, Raspberry Pi 4 Model B, and NVIDIA Jetson Nano—and performance reports in terms of latency, energy consumption, and CO₂ emissions are presented. Moreover, three model agnostic explainable AI (XAI) methods were employed to increase the transparency of the hybrid model’s final decision: Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDP). The hybrid model achieves on average 98.71%, 99.25%, 98.78%, and 99.01% performance in terms of accuracy, precision, recall, and F1-score across all devices Additionally, the DNN baseline and SVM model achieve on average 93.2%, 88.33%, 90.45%, and 89.37%, as well as 93.34%, 88.11%, 95. 41%, and 91.62% performance in terms of accuracy, precision, recall, and F1-score across all devices. The integration of XAI methods within the PdM pipeline offers enhanced transparency, interpretability, and trustworthiness of predictive outcomes, thereby facilitating informed decision-making among maintenance personnel. Full article

Entity resolution (ER) is a fundamental data integration process hindered by its quadratic computational complexity, making naive comparisons infeasible for large datasets. Blocking (or partitioning) is the foundational strategy to overcome this, traditionally using methods like K-Means clustering to group similar records. However, with the rise of deep learning and high-dimensional vector embeddings, the ER task has evolved into a vector similarity search problem. This review traces the evolution of K-Means from a direct, standalone blocking algorithm into a core partitioning engine within modern Approximate Nearest Neighbor (ANN) indexes. We analyze how its role has been adapted and optimized in partition-based systems like the Inverted File (IVF) system and Google’s SCANN, which are now central to scalable, embedding-based ER. By examining the architectural principles and trade-offs of these methods and contrasting them with non-partitioning alternatives like HNSW, this paper provides a coherent narrative on the journey of K-Means from a simple clustering tool to a critical component for scaling modern ER workflows. Full article