Search Results (7,414)

Stroke is one of the leading causes of disability worldwide. Its sequelae require early, intensive, and repetitive rehabilitation, but is often ineffective due to a lack of patient motivation. Gamification has been incorporated in recent years as a response to this issue. The aim of incorporating games is to motivate patients to perform therapeutic exercises. This study presents PACTUS, a new version of a gamified device for stroke neurorehabilitation. Using a series of colored cards, a touchscreen station, and a sensorized handle with an RGB sensor, patients can interact with three games specifically programmed to work on different areas of neurorehabilitation. In addition to presenting the technical design (including energy consumption and sensor signal processing), the results of an observational study conducted with neurological patients, healthy older adults, and caregivers (who also completed the System Usability Scale) are also presented. This usability, safety, and satisfaction study provided an assessment of the device for future iterations. The inclusion of the experiences of the three groups (patients, caregivers, and older adults) provided a more comprehensive and integrated view of the device, enriching our understanding of its strengths and limitations. Although the results were preliminarily positive, areas for improvement were identified. Full article

Despite the health concerns regarding alcohol and its link to cancer, moderate consumption of red wine has been associated with healthy aging and longevity, defined as up to one drink per day for women and two drinks per day for men (approximately 142 mL or 5 oz per drink). Previous research has revealed the health benefits of red wine, particularly in relation to cardiovascular disease. However, the influence of genetic factors on these benefits remains to be elucidated. In this study, we explored genes linked to red wine and created a curated gene set that intersects with those related to centenarians, which are markers of exceptional longevity. By analyzing literature from over 190 databases, we identified and validated a curated list of 43 genes associated with red wine and centenarians. We conducted gene set enrichment analysis as well as enrichment analysis of diseases and their tissue distributions. The results suggest that these genes play a crucial role in stress response and apoptosis, which are essential for cell survival and renewal. Additionally, these genes were enriched in pathways associated with smooth muscle cell proliferation, neuroinflammation, nucleotide excision repair, and lipoprotein metabolism (false discovery rate, FDR < 3 × 10⁻⁷). Gene set enrichment analysis indicated significant tissue distribution in the gastrointestinal, cardiovascular, and respiratory systems. Furthermore, the disease–gene enrichment analysis pointed to associations with diseases related to tissues and organs, including cardiovascular disease (heart disease and stroke), type 2 diabetes, gastrointestinal diseases and metabolic diseases, immune diseases, and cancer (FDR < 9.37 × 10⁻⁶); notably, cardiovascular diseases, diabetes, and cancer are leading causes of death, suggesting that these genes may be protective against those diseases. Our review of the literature indicates that individuals who do not currently drink alcohol should not be encouraged to start. However, we propose that moderate consumption of red wine, especially for middle-aged to older adults after 40 years old, can provide significant health benefits due to its components and the positive effects of hormesis. Although further research is necessary to uncover additional genes, this study provides the first genetic overview of the health benefits of red wine, emphasizing its potential in supporting healthy aging and longevity. Full article

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. 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

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

This study focuses on optimizing the machinability of Al-Fe-Cu (8000 series) alloys by developing new compositions with varying Fe and Cu contents and evaluating their mechanical, microstructural, and energy performance. For this purpose, 6061 Al alloy was melted in an induction furnace and cast into molds, and samples containing 2.5% and 5% Fe were produced. Microstructural features were analyzed using Python-based image processing, while Specific Energy Consumption (SEC) theory was applied to assess machining efficiency. An alloy with 2.5% Fe and 2.64% Cu showed superior mechanical properties and the lowest energy consumption. Increasing cutting speed and depth of cut notably decreased SEC. Machine learning (ML) analysis confirmed strong predictive capability, with R² values above 0.80 for all models. Decision Tree (DT) achieved the highest accuracy for SEC prediction (R² = 0.98634, MAE = 0.02209, MSE = 0.00104), whereas XGBoost (XGB) performed best for SCEC (R² = 0.96533, MAE = 0.25578, MSE = 0.10178). Response Surface Methodology (RSM) optimization further validated the significant influence of machining parameters on SEC and specific cutting energy consumption (SCEC). Overall, the integration of machine learning (ML), response surface methodology (RSM), and energy equations provides a comprehensive approach to improve the machinability and energy efficiency of 8000 series alloys, offering practical insights for industrial applications. Full article

High-intensity functional training (HIFT) has grown in popularity in the past several decades, yet previous research has largely focused on the dietary habits and body composition of elite HIFT athletes and utilized only quantitative study designs, potentially limiting our understanding of typical HIFT athletes. This study aimed to comprehensively describe the common dietary habits and body composition of HIFT athletes. Data were only analyzed descriptively. Among 62 HIFT athletes (age: 36 ± 11.7 years), we estimated body fat percentage (BF%) using a Siri 3-compartment model, and we assessed dietary habits, dietary supplement (DS) use, and open-response rationales for DS use/disuse via an online questionnaire. Qualitative data from open-response questions were coded and grouped via inductive thematic analysis. Body composition varied among both male (n = 36, BF% = 6.5–27.6%) and female participants (n = 26, BF% = 10.6–37.6%). Most participants reported regular consumption of lean meats and home-cooked meals, yet few participants (~20%) regularly consumed the recommended twice daily servings of dairy, fruits, vegetables, and whole grains. Most (77.4%) HIFT athletes reported DS use, with the average HIFT athlete using approximately six DS; dairy protein, creatine, caffeine, and electrolyte drinks were the most reported DS. Improving health, recovery, and nutrient intake were common reasons for using DS, whereas a lack of noticeable results was the most common reason for discontinuation. Some HIFT athletes may rely on DS to address nutrient gaps rather than whole foods. Full article

Existing research on live-streaming e-commerce consumption behavior is mostly limited by a single disciplinary framework, unable to systematically parse the mechanism of macro-policies and cultural values on intergenerational consumer psychology. This study takes ASEAN cross-border live-streaming e-commerce as a scenario, integrates theories of economics, political science, and sociology, and constructs an innovative three-layer analysis model of “macroeconomic system–meso-market–micro-behavior” based on multi-source data from 2020 to 2024. It empirically explores the formation mechanism of intergenerational differences in impulse buying. The results show that the behavior differences of different groups are significantly driven by income gradient, cross-border policies (tariff adjustment and consumer protection regulations), and collectivism/individualism cultural orientations. The innovative contribution of this study is reflected in three aspects: Firstly, it breaks through the limitation of a single discipline, and for the first time, it incorporates structural variables such as policy synergy effect and family structure change into the theoretical framework of impulse buying, quantifying and revealing the differentiated impact of institutional heterogeneity in ASEAN markets on intergenerational behavior. Secondly, it reconstructs the transmission path of “cultural values–family structure–intergenerational behavior” and finds that the inhibitory effect of collectivism on impulse buying tends to weaken with age. Thirdly, it proposes a “policy instrument–generational response” matching model and verifies the heterogeneous impact of the same policy (such as tariff reduction) on different generations. This study fills the gaps in related research and can provide empirical support for ASEAN enterprises to formulate stratified marketing strategies and for policymakers to optimize cross-border e-commerce regulation. which is of great significance to promote the sustainable development of the regional live-broadcast e-commerce ecology. Full article

Hydrogels comprise three-dimensional networks of hydrophilic polymers and have attracted considerable interest in various sectors, including the biomedical, pharmaceutical, agricultural, and food industries. These materials offer significant benefits for food packaging applications, such as high mechanical strength and excellent water absorption capacity, thereby contributing to the extension of product shelf life. Therefore, the aim of this study is to compare the performance of citric acid and glutaraldehyde as crosslinking agents in gelatine-based hydrogels reinforced with cellulose nanocrystals (CNC), contributing to the development of safe and environmentally responsible materials. The hydrogels were prepared using the casting method and characterised in terms of their physical, mechanical, and structural properties. The results indicated that hydrogels crosslinked with glutaraldehyde exhibited higher opacity, lower transparency, and greater mechanical strength, whereas those crosslinked with citric acid demonstrated improved clarity, reduced water permeability, and enhanced swelling capacity. The incorporation of CNC further improved mechanical strength, reduced weight loss, and altered both surface homogeneity and optical properties. Microstructural results obtained by SEM were consistent with the mechanical properties evaluated (TS, %E, and EM). The Gel-ca hydrogel displayed the highest elongation value (98%), reflecting better cohesion within the polymeric matrix. In contrast, films incorporating CNC exhibited greater roughness and cracking, which correlated with increased rigidity and mechanical strength, as evidenced by the high Young’s modulus (420 MPa in Gel-ga-CNC2). These findings suggest that the heterogeneity and porosity induced by CNC limit the mobility of polymer chains, resulting in less flexible and more rigid structures. Additionally, the DSC analysis revealed that gelatine hydrogels did not exhibit a well-defined Tg, due to the predominance of crystalline domains. Systems crosslinked with citric acid showed greater thermal stability (higher Tm and ΔH_m values), while those crosslinked with glutaraldehyde, although mechanically stronger, exhibited lower thermal stability. These results confirm the decisive effect of the crosslinking agent and CNC incorporation on the structural and thermal behaviour of hydrogels. In this context, the application of hydrogels in packaged products represents an eco-friendly alternative that enhances product presentation. This research supports the reduction in plastic consumption whilst promoting the principles of a circular economy and facilitating the development of materials with lower environmental impact. Full article

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

Demand-side management has been demonstrated as an efficient and feasible method to unlock the flexibility on the demand side and support the flexible regulation of power systems. In integrated energy systems (IES) of buildings, through energy storage systems (ESS) and demand response methods, the utilization rate of renewable energy can be effectively improved, and the stability of the grid can be enhanced. However, the traditional energy usage methods of IES have limited responsiveness to the power system. Moreover, existing flexible energy usage strategies based on demand response rarely consider the impact of ESS in IES on energy usage strategies. Addressing the aforementioned issues, this paper proposes a flexible energy usage strategy based on ESS and demand-side management. This strategy takes into account the daily energy production and consumption of IES, as well as the relationship between user load and the grid, forming a hierarchical scheduling mechanism for energy usage. To fully explore the impact of ESS capacity on flexible energy usage scheduling strategies, the scheduling role of ESS is quantified in terms of photovoltaic utilization rate, responsiveness, and overall cost. The results indicate that implementing the flexible energy scheduling strategy in the system increases the annual PV self-consumption by 35.29%. With higher ESS capacity, the PV self-consumption rate (SCR) can be maximized, improving by up to 4.07%. The system’s response capability is enhanced after adopting the scheduling strategy and improves further with increasing ESS capacity. Regarding costs, although applying this strategy leads to a rise in ESS operational loss costs during its functioning phase, the overall system costs decrease by approximately 65.13%, with a capacity-based variation of about 1.48%. Full article

Background: Metabolic flexibility, the ability to efficiently switch between fuel sources in response to changing nutrient availability and energy demands, is recognized as a key determinant of metabolic health. In a recent randomized controlled human feeding trial, overweight individuals receiving American black elderberry juice (EBJ) demonstrated improvements in multiple clinical indices of metabolic flexibility, but the mechanisms of action were unexplored. The objective of this study was to utilize RNA sequencing to examine how EBJ modulates the transcriptional response to fasting and feeding, focusing on pathways related to metabolic flexibility. Methods: Overweight or obese adults (BMI > 25 kg/m²) without chronic illnesses were randomized to a 5-week crossover study protocol with two 1-week periods of twice-daily EBJ or placebo (PL) separated by a washout period. RNA sequencing was performed on peripheral blood mononuclear cells from 10 participants to assess transcriptomic responses collected at fasting (pre-meal) and postprandial (120 min post-meal) states during a meal-challenge test. Results: The fasted-to-fed transition for EBJ showed 234 differentially expressed genes following EBJ consumption compared to 59 genes following PL, with 44 genes shared between interventions. EBJ supplementation showed significantly higher enrichment of several metabolic pathways including insulin, FoxO, and PI3K–Akt signaling. KEGG pathway analysis showed 27 significant pathways related to metabolic flexibility compared to 7 for PL. Conclusions: Our findings indicate that short-term elderberry juice consumption may promote metabolic flexibility in overweight adults. Full article

Sprayers for vineyards with solution recovery represent an important innovation, offering several advantages, the most important being the efficient use of pesticides and environmental protection. This paper presents the experimental equipment designed to study the treatment process of grapevine foliage, the applied research methods, and the results of optimizing key technological parameters (hydraulic pressure p of the working solution, speed V of the airflow at the nozzle outlet) and design parameters (surface area S of the central orifice of the diffuser) in different growth stages of grapevines with varying foliar density ρ, the response function being the recovery rate of the working solution. The construction of the SVE 1500 (Experimental model, manufactured at the Institute of Agricultural Technology “Mecagro”, Chisinau, Republic of Moldova) vineyard sprayer with solution recovery is presented, along with test results obtained in field conditions, which demonstrated that the experimental model of our machine ensures a 38% reduction in working solution consumption during the active vegetation phase while maintaining treatment quality in compliance with agrotechnical requirements. The SVE 1500 machine can be towed with a sufficient turning radius for use in modern vineyard plantations. Construction documentation has been developed for the production and delivery of the experimental batch of SVE 1500 machines to agricultural enterprises. Full article

Gastronomic tourism is a strategic tool for territorial development, as it promotes cultural heritage, supports local economies and encourages environmentally responsible consumption. This study attempts to analyse the evolution of key gastronomic products through digital marketing tools, reflecting on the need to know this real data in order to carry out sustainable territorial and tourism planning. To do so, it uses a methodology based on the analysis of data obtained through Google Trends, taking as a reference a set of terms related to seafood, traditional meats and wines with designation of origin. The study examines the seasonal patterns and geographical distribution of interest in these terms, evaluating their impact both inside and outside Galicia as a replicable methodological case. The results show significant differences between categories. In addition, there is a generalised decrease in the search for gastronomic terms, which may indicate a reduction in the relative weight of this element as a factor in the creation of the image of the territories. In conclusion, the article demonstrates the capacity of this methodology to propose more sustainable tourism, territorial and economic planning strategies based on the transformation of qualitative imaginaries into quantitative data and trends. Full article

This paper studies joint data collection and wireless power transfer in a UAV-assisted IoT network. A rotary-wing UAV follows a fly–hover–communicate cycle. At each hover, it simultaneously receives uplink data in full-duplex mode while delivering radio-frequency energy to nearby devices. Using a realistic propulsion-power model and a nonlinear energy-harvesting model, we formulate trajectory and hover control as a multi-objective optimization problem that maximizes the aggregate data rate and total harvested energy while minimizing the UAV’s energy consumption over the mission. To enable flexible trade-offs among these objectives under time-varying conditions, we propose a dynamic, state-adaptive weighting mechanism that generates environment-conditioned weights online, which is integrated into an enhanced deep deterministic policy gradient (DDPG) framework. The resulting dynamic-weight MODDPG (DW-MODDPG) policy adaptively adjusts the UAV’s trajectory and hover strategy in response to real-time variations in data demand and energy status. Simulation results demonstrate that DW-MODDPG achieves superior overall performance and a more favorable balance among the three objectives. Compared with the fixed-weight baseline, our algorithm increases total harvested energy by up to 13.8% and the sum data rate by up to 5.4% while maintaining comparable or even lower UAV energy consumption. Full article