Search Results (2,346)

Background: Contemporary dermatology and cosmetology continue to explore effective strategies for normalizing the function of oily skin, where excessive sebum production and impairment of the hydrolipid barrier pose considerable therapeutic challenges. The aim of the present study was to evaluate the effects of a series of microneedling mesotherapy treatments with a sebum-regulating ampoule on selected biophysical parameters of the skin in individuals with oily skin. Methods: The study included 19 female volunteers aged 18–42 years, who underwent six treatment sessions at three-week intervals. Skin parameters were assessed at baseline, after three sessions, and after six sessions using the MPA system (Courage & Khazaka) equipped with the following probes: Corneometer (hydration), Sebumeter (sebum secretion), pH meter (surface pH), Glossymeter (skin shininess), and Tewameter (transepidermal water loss). Results: After six sessions, hydration significantly increased both in the T-zone (from 43.9 ± 8.0 to 54.0 ± 5.4 AU; +23%) and on the cheeks (from 35.9 ± 8.3 to 55.6 ± 4.8 AU; +55%) (p < 0.001). Sebum secretion decreased markedly, with values in the T-zone falling from 192.2 ± 30.6 to 127.7 ± 27.2 AU (–34%) and on the cheeks from 185.0 ± 36.2 to 114.8 ± 30.1 AU (–38%) (p < 0.001). Skin surface pH showed minor but significant modulation within the physiological range (T-zone: 6.33 ± 0.64 → 6.01 ± 0.17; cheeks: 6.14 ± 0.50 → 6.03 ± 0.17; p = 0.021). TEWL demonstrated a nonsignificant change (T-zone: 17.46 ± 11.31 → 19.09 ± 3.54 g/m²/h; cheeks: 20.89 ± 5.36 → 18.37 ± 2.95 g/m²/h; p > 0.05), while skin gloss remained stable (T-zone: 5.46 ± 1.25 → 5.60 ± 1.16 GU; cheeks: 5.29 ± 1.76 → 4.87 ± 1.20 GU; p > 0.05). Conclusions: Microneedling mesotherapy combined with a sebum-regulating ampoule significantly improved skin hydration and reduced sebum secretion, accompanied by stabilization of skin surface pH. Although changes in TEWL and gloss were not statistically significant, the overall results indicate improved skin condition and balance. Despite the absence of a control group, these findings support the potential of this combined approach as an adjunctive therapy for oily skin. Further controlled studies with larger cohorts are warranted to confirm its efficacy and long-term effects. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

Understanding flow dynamics in open-channel node systems is crucial for designing effective hydraulic engineering solutions and minimizing energy losses. This study investigates how junction geometry—specifically the lateral inflow angle (α = 45° and 60°) and the longitudinal bed slope (I = 0.0011 to 0.0051)—influences the water velocity distribution and hydraulic losses in a rigid-bed Y-shaped open-channel junction. Experiments were performed in a 0.3 m wide and 0.5 m deep rectangular flume, with controlled inflow conditions simulating steady-state discharge scenarios. Flow velocity measurements were obtained using a PEMS 30 electromagnetic velocity probe, which is capable of recording three-dimensional velocity components at a high spatial resolution, and electromagnetic flow meters for discharge control. The results show that a lateral inflow angle of 45° induces stronger flow disturbances and higher local loss coefficients, especially under steeper slope conditions. In contrast, an angle of 60° generates more symmetric velocity fields and reduces energy dissipation at the junction. These findings align with the existing literature and highlight the significance of junction design in hydraulic structures, particularly under high-flow conditions. The experimental data may be used for calibrating one-dimensional hydrodynamic models and optimizing the hydraulic performance of engineered channel outlets, such as those found in hydropower discharge systems or irrigation networks. Full article

The digital transformation of low-voltage distribution networks demands a renewed perspective on both regulatory frameworks and metering technologies. This article explores the intersection between incentive structures and metering technologies, focusing on how smart metering can act as a strategic enabler for flexibility in electricity distribution. Starting with the Spanish regulatory evolution and European benchmarking, the shift from asset-based regulation and how it can be complemented with performance-oriented incentives to support advanced metering functionalities is analyzed. On the technical side, the capabilities of smart meters and the performance of communication protocols (such as PRIME, G3-PLC, and 6LoWPAN) highlighting their suitability for real-time observability and control are examined. The findings identify a way to enhance regulatory frameworks for fully harnessing the operational potential of smart metering systems. This article calls for a hybrid, context-aware approach that integrates regulatory evolution with metering structures innovation to unlock the full value of smart metering in the energy transition. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

High-precision time synchronization among nodes of the airborne-based pseudolite navigation augmentation positioning system (A-PNAS) is a crucial indicator for ensuring the accuracy of positioning services. Due to the flight characteristics and external factors’ influence, the airborne platform usually undergoes random motion. Therefore, the time-varying effect errors and Doppler effect errors will be introduced into the clock skew measurement results during the time-synchronous processing. In A-PNAS with meter-level positioning accuracy, the time synchronization accuracy (TSA) between nodes usually needs to be within 2 ns. These dynamic errors will have an impact on the TSA between nodes, which cannot be ignored. Based on the analysis of the principle of dynamic error generation and the available sensors, a multi-sensor combination method for correcting dynamic errors is proposed. This method calculates and corrects the dynamic errors based on the motion measurements from sensors. The simulation test results show that the degree of improvement in correcting dynamic errors by this method is basically close to 80%. It can effectively meet the requirements of high-precision time synchronization system and can provide an effective reference for the high-precision time synchronization processing of similar space-based platform collaborative systems. 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

Arsenic pollution in potable water is a significant worldwide health concern. This study systematically evaluates current progress in adsorption technology, the most promising restorative approach, to provide a definitive framework for future research and use. The methodology entailed a rigorous evaluation of 91 peer-reviewed studies (2012–2025), classifying adsorbents into three generations: (1) Natural adsorbents (e.g., agricultural/industrial wastes), characterized by cost-effectiveness but limited capacities (0.1–5 mg/g); (2) Engineered materials (e.g., metal oxides, activated alumina), which provide dependable performance (84–97% removal); and (3) Advanced hybrids (e.g., MOFs, polymer composites), demonstrating remarkable capacities (60–300 mg/g). The primary mechanisms of removal are confirmed to be surface complexation, electrostatic interactions, and redox precipitation. Nevertheless, the critical analysis indicates that despite significant laboratory efficacy, substantial obstacles to field implementation persist, including scalability limitations (approximately 15% of materials are evaluated beyond laboratory scale), stability concerns (e.g., structural collapse of MOFs at extreme pH levels), and elevated costs (e.g., MOFs priced at approximately $230/kg compared to $5/kg for alumina). The research indicates that the discipline must transition from only materials innovation to application science. Primary objectives include the development of economical hybrids (about $50/kg), the establishment of uniform WHO testing standards, and the implementation of AI-optimized systems. The primary objective is to attain sustainable solutions costing less than $0.10 per cubic meter that satisfy worldwide deployment standards via multidisciplinary cooperation. Full article

Remote First Nations communities in Australia experience ongoing energy insecurity due to geographic isolation, reliance on diesel, and uneven consumer protections relative to grid-connected households. This paper analyses evidence on electricity access, infrastructure and practical experience along with initiatives for improving existing infrastructure; highlights government policies, funding frameworks and regulation; demonstrates the benefits of community-led projects; provides geographic and demographic insights; and relevels key challenges along with pathways for effective solutions. Drawing on existing program experience, case studies and recent reforms (including First Nations–focused strategies and off-grid consumer-protection initiatives), this paper demonstrates that community energy systems featuring solar-battery systems can significantly improve reliability and affordability by reducing reliance on diesel generators and delivering tangible household benefits. The analyses reveal that there is an ongoing gap in protecting off-grid consumers. Hence, this work proposes a practical agenda to improve electricity supply systems for First Nations community energy systems through advanced community microgrids (including long-duration storage), intelligent energy management and monitoring systems, rights-aligned consumer mechanisms for customers with prepaid metering systems, fit-for-purpose regulation, innovative blended finance (e.g., Energy-as-a-Service and impact investment) and on-country workforce development. Overall, this paper contributes to a perspective for an integrated framework that couples technical performance with equity, cultural authority and energy sovereignty, offering a replicable pathway for reliable, affordable and clean electricity for remote First Nations communities. Full article

Energy Communities have emerged as a key mechanism for promoting citizen participation in the energy transition. In Greece, recent legislation replaced the virtual net-metering scheme with a virtual net-billing framework, introducing new economic and regulatory conditions for shared renewable energy investments. This study develops an optimization tool for determining the optimal PV system size and Demand Response actions for individual EC members under this new framework. The model is constructed to align closely with the current regulatory and legal context, incorporating technical, economic, and policy-related constraints. It uses real electricity production and consumption data from existing Greek ECs, as well as 2024 Day Ahead Market prices, grid fees, and surcharges. The results emphasize the importance of customized sizing strategies and suggest that policy refinements may be needed to ensure equitable participation and maximize community-level benefits. Full article

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations. Full article

When relying exclusively on inter-satellite links for autonomous orbit determination, it cannot suppress or eliminate the constellation overall rotation, rendering it incapable of determining its spatial orientation relative to terrestrial and celestial reference frames. To address these limitations, an autonomous orbit determination method for BeiDou Satellites is proposed by integrating satellite-to-ground, inter-satellite, and space-based orientation observations. This study introduces space-based orientation data between navigation satellites to provide inertial frame orientation references for the BeiDou constellation, while utilizing ground-based anchor stations to establish orientation references in the Earth-fixed frame. The results demonstrate that (1) In a 90-day autonomous operation within the inertial frame, the combined use of inter-satellite links and space-based orientation data achieves a 3D orbit position accuracy of 0.45 m. (2) In semi-autonomous operation, with Earth rotation parameter (ERP) updates every three days from ground stations, the 3D orbit determination accuracy reaches the decimeter level; using long-term predicted ERPs in conjunction with satellite-to-ground data, meter-level accuracy is maintained. (3) When the space-based orientation measurement noise is limited to 5 milliarcseconds, the accuracies of polar motion parameters xp and yp reach 2.23 milliarcseconds and 3.55 milliarcseconds, respectively, while the UT1–UTC parameter achieves an accuracy of 0.42 milliseconds. This work provides critical technical support for flexible autonomous navigation of the BeiDou system when the ground control stations are destroyed in the wartime and contributes to the independent determination of ERP within China. Full article

Accurate very short-term load forecasting (VSTLF) is critical to ensure a secure operation of power systems under increasing uncertainty due to renewables. This study proposes an eXtreme Gradient Boosting (XGBoost)-based VSTLF model that incorporates day-ahead load forecasts (DALF) results and load variation features. DALF results provide trend information for the target time, while load variation, the difference in historical electric load, captures residual patterns. The load reconstitution method is also adapted to mitigate the forecasting uncertainty caused by behind-the-meter (BTM) photovoltaic (PV) generation. Input features for the proposed VSTLF model are selected using Kendall’s tau correlation coefficient and a feature importance score to remove irrelevant variables. A case study with real data from the Korean power system confirms the proposed model’s high forecasting accuracy and robustness. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

Smart meters (SMs) are essential components of modern smart grids, enabling real-time and accurate monitoring of electricity consumption. However, their evaluation is often hindered by proprietary communication protocols and the high cost of commercial testing tools. This study presents a low-cost, open-source experimental platform for smart meter validation, using a microcontroller and light sensor to detect optical pulses emitted by standard SMs. This non-intrusive approach circumvents proprietary restrictions while enabling transparent and reproducible comparisons. A case study was conducted comparing the static meter GAMA 300 model, manufactured by Elgama-Elektronika Ltd. (Vilnius, Lithuania), which is a closed-source commercial meter, with theTexas Instruments EVM430-F67641 evaluation module, manufactured by Texas Instruments Inc. (Dallas, TX, USA), which serves as an open-source reference design. Statistical analyses—based on confidence intervals and ANOVA—revealed a mean deviation of less than 1.5% between the devices, confirming the platform’s reliability. The system supports indirect power monitoring without hardware modification or access to internal data, making it suitable for both educational and applied contexts. Compared to existing tools, it offers enhanced accessibility, modularity, and open-source compatibility. Its scalable design supports IoT and environmental sensor integration, aligning with Internet of Energy (IoE) principles. The platform facilitates transparent, reproducible, and cost-effective smart meter evaluations, supporting the advancement of intelligent energy systems. Full article