Search Results (239)

High-frequency non-intrusive load monitoring provides detailed harmonic information for appliances’ power disaggregation, and machine-learning approaches have demonstrated good performance in this task. However, these methods provide little transparency regarding the information structure of the aggregate signal. To address this, this paper models NILM as a coding-decoding process and applies information-theoretic measures to quantify uncertainty, recoverability, temporal contribution, and inter-appliance masking effects in aggregate signals. In the analyzed dataset, transfer entropy suggests negligible temporal gains, which is consistent with the observed effectiveness of pointwise models such as Random Forest. Moreover, conditional mutual information emphasizes the asymmetric masking relationships between appliances, with the laptop charger acting as a dominant interferer in the considered measurements. These findings are validated through a Random Forest regression model with minimum Redundancy Maximum Relevance feature selection. The results show that the mutual information between an appliance and the aggregate is a good predictor of disaggregation performance in the examined data, as appliances with high mutual information, such as hair dryer and electric water heater, achieve lower estimation errors, while others, such as iron, are difficult to recover despite stable distributions. This relationship is statistically supported by a strong negative monotonic correlation between normalized mutual information and the disaggregation error (Spearman $r_s=-0.81$, $p=0.015$). Hence, this work demonstrates how information-theoretic analysis can help characterize disaggregation difficulty prior to model training and assess the observability of appliances in high-frequency NILM. Full article

Non-intrusive load monitoring (NILM) is a key enabler for smart-grid applications, yet practical deployment is often hindered by limited appliance-level labels and severe distribution shifts across households and operating conditions. As a result, many deep learning approaches become unreliable in small-sample and out-of-distribution (OOD) settings. In this paper, we propose CR-MAT, a causality-driven representation learning framework for few-shot NILM classification. Instead of relying on large-scale training or heavy data augmentation, CR-MAT injects causal representation learning into multi-appliance task modeling, encouraging the network to learn appliance-discriminative features that are stable across environments while suppressing spurious, domain-specific correlations. We conduct extensive experiments under multiple OOD scenarios and consistently observe improved classification robustness compared with deep NILM baselines. Further analysis indicates that causal representation learning enhances resilience to non-stationary consumption patterns and improves generalization under OOD scenarios. The proposed framework provides a practical route toward reliable NILM classification and supports downstream smart-grid applications such as flexible load control and demand response. Full article

Background/Objectives: The achievement of stable and functional occlusal contacts represents a key objective of orthodontic treatment, particularly in growing patients. Evidence comparing the effectiveness of these two modalities in establishing adequate occlusal contacts in growing patients remains limited. This study aimed to evaluate and compare occlusal contact characteristics following clear aligner therapy (CAT) and fixed orthodontic therapy (FAT). Methods: Twenty-four growing patients (<18 years with permanent dentition) were included in the study and divided into two groups: 12 patients treated with fixed appliances and 12 treated with clear aligners. Post-treatment digital dental scans were analyzed to assess occlusal contacts. Contacts were calculated as the minimum distance between upper and lower arches using a color-map analysis. The following outcomes were evaluated: Maximum Contact Point (MCP), occlusal contact surface (OCS, ≤50 μm from MCP), near occlusal contact surface (NOCS, ≤350 μm), half mm (≤0.5 mm), and one mm (≤1 mm). Total occlusal contacts, antero-posterior distribution, left–right asymmetry, and single-tooth contacts were assessed. Results: The FAT group showed higher total occlusal contact values in OCS compared to the CAT group (p < 0.05). Statistical difference was also observed in the antero-posterior ratio, with FAT presenting fewer anterior contacts in OCS, NOCS, half-mm, and one-mm measurements (p < 0.05). No significant differences were found between groups in terms of left–right asymmetry or post-treatment single-tooth contacts, except for the second premolar, which exhibited higher contacts in the FAT group (p < 0.05). Conclusions: Fixed orthodontic treatment is more effective than aligners in achieving adequate occlusal contacts, with differences limited to tight contacts and antero-posterior occlusal distribution. Full article

Background/Objectives: Transverse maxillary deficiency in growing patients can be treated using rapid maxillary expansion (RME) or slow maxillary expansion (SME) with spring-based appliances, such as the Leaf Expander (LE), but their comparative dentoskeletal effects remain debated. This study evaluated the transverse dentoskeletal outcomes of LE-based SME versus conventional RME. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO. Electronic searches were performed in PubMed, Scopus, Embase, Web of Science, and Cochrane Library up to 9 January 2026. Randomized controlled trials (RCTs) comparing LE-based SME and RME in skeletally immature patients were included. Primary outcomes were transverse maxillary change; secondary outcomes included dentoalveolar side effects. Risk of bias was assessed using the RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. When possible, a meta-analysis was performed using standardized mean differences and a random-effects model. Results: Four RCTs met the inclusion criteria. Both SME and RME achieved significant transverse expansion. Meta-analysis showed no statistically significant differences between protocols for inter-canine distance, inter-second deciduous molar distance, inter-first permanent molar distance, or basal maxillary width. Intergroup differences varied by anatomical site and measurement method: RME showed greater anterior dental and skeletal transverse gains, whereas SME achieved comparable intermolar expansion with greater molar distorotation. Three-dimensional analyses indicated similar morphological enlargement. Risk of bias ranged from low to high; the certainty of evidence was low to very low for most transverse parameters and moderate only for molar distorotation. Conclusions: Both LE-based SME and RME effectively correct transverse maxillary deficiency. Quantitative synthesis showed comparable overall transverse expansion, with differences mainly related to the distribution and biomechanical pattern of dentoskeletal effects rather than the absolute amount of expansion achieved. Appliance selection should be guided by biomechanical features and individual treatment objectives. Further high-quality RCTs with standardized three-dimensional protocols and longer follow-up are needed. Full article

The increasing deployment of Internet of Things (IoT) sensing infrastructures and distributed renewable energy resources is enabling the emergence of Renewable Energy Communities (RECs), which require intelligent, adaptive, and decentralized energy management strategies. This study proposes a sensor-driven reinforcement learning (RL) framework for the coordinated management of residential RECs, aiming to jointly optimize thermal comfort, economic savings, and environmental sustainability. Each household is equipped with a network of IoT sensors monitoring indoor temperature, energy production and consumption, battery state of charge, and user presence, which collectively define a discretized state space for a tabular Q-learning agent controlling heating systems and programmable appliances. A stochastic simulation environment is developed to realistically reproduce weather variability, building thermal dynamics, user activity profiles, and photovoltaic generation. To address the instability typical of multi-agent learning, a two-stage training strategy is adopted: agents are first pre-trained at single-house level using synthetic sensor data and are subsequently deployed within the full community, where coordination is achieved through shared reward components without explicit inter-agent communication. Performance is evaluated on a heterogeneous Renewable Energy Community (REC) composed of eleven households, including both prosumers and consumers. The simulation results show that the proposed approach significantly outperforms rule-based control strategies, achieving lower energy consumption, improved thermal comfort stability, and higher global reward. Moreover, pre-trained agents maintain stable and cooperative behavior when operating concurrently at community level, with limited sensitivity to exploration. These findings demonstrate that sensor-driven, lightweight reinforcement learning represents a viable and scalable solution for decentralized energy management in IoT-enabled Renewable Energy Communities. Full article

Background/Objectives: Clinical and scientific professionalization in orthodontics requires a comprehensive understanding of the biomechanical principles governing force generation and distribution produced by orthodontic appliances, beyond purely esthetic considerations. In this context, finite element analysis (FEA) has emerged as a fundamental computational tool for the detailed evaluation of the biomechanical behavior of the dentoalveolar system. The aim of this study was to map and synthesize the available scientific evidence on the application of FEA in the assessment of force distribution, tooth movement, and the mechanical response of periodontal tissues during orthodontic treatment. Methods: Original studies published between 2020 and 2025 that relied exclusively on computational simulations using FEA were included. Eligible studies addressed orthodontic biomechanics, including tooth movement, appliance–tooth–periodontium interactions, or the mechanical evaluation of orthodontic attachments. Clinical studies, narrative reviews, and articles without finite element modeling were excluded. A systematic literature search was conducted in the PubMed and ScienceDirect databases to answer the following question: Which FEA methodologies have been used to evaluate the biomechanical behavior of orthodontic appliances? Results: Data were categorized according to key biomechanical variables. The findings indicate an increasing use of FEA as a supportive tool in orthodontic research. However, significant limitations were identified, including lack of methodological standardization, limited model validation, and insufficient correlation between computational outcomes and clinical evidence. Conclusions: Currently, FEA in orthodontics is used predominantly for descriptive purposes, particularly for visualizing stress and strain distributions. Greater standardization and validation are required to enhance its translational applicability in clinical relevance. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

Background and Clinical Significance: Clear aligner therapy has become a widely used orthodontic treatment, particularly among adults seeking esthetic and comfortable alternatives to fixed appliances. Achieving a stable and functional occlusion remains one of the primary objectives in orthodontics. The T-Scan digital occlusal analysis system offers an innovative and objective method for quantifying occlusal contact distribution and timing, thereby improving diagnostic accuracy and follow-up. This report aims to present a clinical case demonstrating the use of the T-Scan Novus system for evaluating occlusal balance before and after clear aligner therapy, highlighting its role in documenting short-term functional occlusal changes. Case presentation: A 42-year-old female patient with Class II malocclusion, deep bite, and anterior crowding was treated with Smilers^® clear aligners over nine months (18 aligners). Digital occlusal analysis was performed before treatment and one month after treatment. Pre-treatment analysis demonstrated a pronounced asymmetry in occlusal force distribution, with left-side dominance (67.9%) compared with the right side (32.1%). One month after treatment, occlusal forces were more evenly distributed (52.4% left, 47.6% right). Occlusion time decreased to 0.25 s and disocclusion time to 0.08 s, falling within commonly reported physiological ranges. Conclusions: Within the limitations of a single-case design and short-term follow-up, digital occlusal analysis using the T-Scan Novus system enabled objective documentation of occlusal force distribution and timing changes after clear aligner therapy. These findings are descriptive and hypothesis-generating and should be interpreted cautiously. Full article

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article

Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak institutional reporting, and the dominance of informal sector activity. We present the first nationwide assessment of e-waste generation and Random Forest-based national forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires across all 16 districts, targeting households, institutions, enterprises, and informal actors. The study documented devices in use, storage, and disposal across the following six categories: ICT, appliances, lighting, batteries, medical, and other electronics. Population growth and device adoption simulations were combined with lifespan distributions and a Random Forest model trained on survey and simulated historical data to construct e-waste flows and forecast quantities through to 2050, including disposal fate probabilities for repurposing versus discarding. The results showed sharp spatial disparities, with Western Urban (Freetown) averaging about 10 kg per capita compared to 1.8 kg per capita in rural areas. Long-term district patterns were highly concentrated: 50-year annual averages indicated that Western Area Urban contributes 15.3% of national totals, followed by Bo (12.7%) and Western Area Rural (12.1%), with the top five districts contributing 59.1%. By 2050, total national e-waste entering reuse and disposal pathways was projected to reach 23.4 kilo tons per year (kt yr⁻¹) with a 95% uncertainty interval (UI) of 11–42 kt yr⁻¹ (and a 99% interval extending to 50 kt yr⁻¹), corresponding to 0.9–3.4 kg/capita/year. Household appliances dominated total mass, ICT devices exhibited high reuse rates, and batteries showed minimal reuse despite high hazard potential. These findings provide critical evidence for e-waste policy, regulation, and infrastructure planning in data-scarce regions. Full article

Microplastics (MPs) are pervasive environmental pollutants, widely distributed from aquatic ecosystems to the terrestrial food chain, and represent a potential route of human exposure. Although several reviews have addressed MP contamination, a critical synthesis focusing on pathways through which consumer goods directly enter food and beverages, along with corresponding industry and regulatory responses, is lacking. This review fills this gap by proposing the direct release of MPs from common sources such as food packaging, kitchen utensils, and household appliances, linking the release mechanisms to human health risks. The release mechanisms of MPs under thermal stress, mechanical abrasion, chemical leaching, and environmental factors, as well as a risk-driven framework for MP release, are summarized. Human exposure through ingestion is the predominant route, while inhalation and dermal contact are additional pathways. In vitro and animal studies have associated MP exposure to inflammatory responses and oxidative stress, neurotoxicity, and genomic instability as endpoints, though direct causal evidence in humans remains lacking, and extrapolation from model systems necessitates caution. This review revealed that dietary intake from kitchen sources is the primary pathway for MP exposure, higher than the inhalation pathway. Most importantly, this review critically sheds light on the initiatives that should be taken by industries with respect to global strategies and new policies to alleviate these challenges. However, while there has been an upsurge in research commenced in this area, there are still research gaps that need to be addressed to explore food matrices such as dairy products, meat, and wine in the context of the supply chain. In conclusion, we pointed out the challenges that limit this research with the aim of improving standardization; research approaches and a risk assessment framework to protect health; and the key differences between MP and nanoplastic (NP) detection, toxicity, and regulatory strategies, underscoring the need for size-resolved risk assessments. Full article

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses. Full article

Hydrogen is widely regarded as a promising clean energy carrier, and blending hydrogen into existing natural gas pipelines is considered a cost-effective and practical pathway for large-scale deployment. Supplying hydrogen-enriched natural gas to buildings requires careful consideration of the safe operation of pipelines and appliances without introducing new risks. In this study, on-site demonstrations and experimental tests were conducted in two high-rise buildings in Weifang to evaluate the impact of hydrogen addition on high-rise building natural gas distribution systems. The results indicate that hydrogen blending up to 20% by volume does not cause stratification in building risers and leads only to a relatively minor increase in additional pressure, approximately 0.56 Pa/m for every 10% increase in hydrogen addition. While hydrogen addition may increase leakage primarily in aging indoor gas systems, gas meter leakage rates under a 10% hydrogen blend remain below 3 mL/h, satisfying safety requirements. In addition, in-service domestic gas alarms remain effective under hydrogen ratios of 0–20%, with average response times of approximately 19–20 s. These findings help clarify the safety performance of hydrogen-blended natural gas in high-rise building distribution systems and provide practical adjustment measures to support future hydrogen injection projects. Full article

The objective of the present study was to investigate whether orthodontic treatment alters the morphology and bone mineral density (BMD) distribution of the mandibular condyle in growing adolescent patients. Cone-beam computed tomography (CBCT) images were retrospectively analyzed for 29 patients (10 males and 19 females, aged 12.5 to 17.0 years) treated with full fixed orthodontic appliances. The right and left mandibular condyles were digitally isolated. For the internal control sample, the basal cortical bone (CB) at both mandibular first molar sites was also digitally dissected. A frequency plot of the CBCT gray values, proportional to BMD, was analyzed to calculate the mean and the 5th percentile of low and high gray values (Low₅ and High₅). Morphological changes in the condylar surface were assessed based on temporomandibular joint osteoarthritis (TMJOA) counts. Lateral cephalometric radiographs were used to measure facial morphology parameters and classify skeletal patterns. The cervical vertebral gray values of the same patients were compared. No radiographic signs of TMJ disorder were observed with no significant difference in TMJOA counts between before and after treatment (p = 0.56). The volume, mean and Low₅ gray values of the mandibular condyle, facial morphology parameters, and cervical vertebral gray values significantly increased following orthodontic treatment (p < 0.05). Skeletal Class II patients exhibited greater changes in mean, Low₅, and High₅ mandibular condyle gray values compared to their Class I patients (p < 0.05), whereas cervical vertebral gray values were not significantly influenced by skeletal classification (p > 0.19). The findings suggest that orthodontic treatment, combined with natural patient growth, contributes to nonpathological condylar alterations in adolescent patients. Full article

The evolution of Smart Grids enabled the deployment of intelligent and decentralized energy management solutions at the residential level. This work presents a comprehensive Smart Home architecture that integrates real-time energy monitoring, appliance-level consumption analysis, and environmental data acquisition using smart metering technologies and distributed IoT sensors. All collected data are structured into a scalable infrastructure that supports advanced Artificial Intelligence (AI) methods, including Large Language Models (LLMs) and machine learning, enabling predictive analysis, personalized energy recommendations, and natural language interaction. Proposed architecture is experimentally validated through a case study on a domestic refrigerator. Two series of tests were conducted. In the first phase, extreme usage scenarios were evaluated: one with intensive usage and another with highly restricted usage. In the second phase, normal usage scenarios were tested without AI feedback and with AI recommendations following them whenever possible. Under the extreme scenarios, AI-assisted interaction resulted in a reduction in daily energy consumption of about 81.4%. In the normal usage scenarios, AI assistance resulted in a reduction of around 13.6%. These results confirm that integrating AI-driven behavioral optimization within Smart Home environments significantly improves energy efficiency, reduces electrical stress, and promotes more sustainable energy usage. Full article