Search Results (884)

Background: Short-video platforms (e.g., Douyin, TikTok) have gained immense popularity among young adults, yet their addictive potential and health consequences remain understudied. Unlike gaming addiction, which has been linked to myopia and mental distress, short-video viewing may pose distinct risks due to its fragmented, high-frequency, and pre-sleep usage patterns. This study aimed to investigate the relationships between short-video viewing and multifaceted health outcomes in Chinese young adults, with a focus on dose–response relationships. Methods: A cross-sectional survey was conducted among 328 young adults aged 20–33 years. Participants were categorized into a non-addicted group (daily short-video viewing < 0.5 h, n = 164) and a short-video viewing group (daily viewing ≥ 1 h, n = 164), further divided by duration: 1–2 h (n = 96), 3–4 h (n = 29), 5–6 h (n = 22), and >6 h (n = 17). Outcome measures included the Hospital Anxiety and Depression Scale (HADS), Van Dream Anxiety Scale (VDAS), 36-Item Short-Form Health Survey (SF-36), Ocular Surface Disease Index (OSDI), Internet Addiction Test (IAT), Chinese Internet Addiction Scale—Revised (CIAS-R), and Mobile Phone Addiction Index (MAPI). Linear regression, one-way ANOVA with Tukey post hoc comparisons (or Kruskal–Wallis tests with Dunn’s test for non-normal variables), and dose–response curve fitting were applied. Segmented regression was used to explore potential dose–response patterns. Results: Compared with the non-addicted group, the short-video viewing group had significantly shorter sleep duration, higher HADS, VDAS, OSDI, IAT, CIAS-R, and MAPI scores, and lower SF-36 scores (all p < 0.0001). Clear dose–response relationships were observed: as daily viewing duration increased, sleep duration decreased linearly (r = −0.73, p < 0.001), while OSDI (r = 0.89, p < 0.001) and VDAS (r = 0.68, p < 0.001) increased. Among participants viewing >6 h/day, all (100%) had moderate-to-severe dry eye (OSDI ≥ 23), and 71% (12/17) had probable anxiety/depression (HADS ≥ 11). Notably, MAPI showed the strongest correlation with VDAS (r = 0.77, p < 0.001), indicating that mobile phone addiction was closely associated with nightmare-related anxiety. The SF-36 physical component summary (PCS) was negatively correlated with daily short-video viewing duration (r = −0.62, p < 0.001). Exploratory threshold analyses suggested that viewing > 3 h/day was associated with greater severity of sleep and ocular surface complaints. The dose–response thresholds identified in this study are exploratory, as the number of participants in the higher-duration categories was limited (3–4 h: n = 29; 5–6 h: n = 22; >6 h: n = 17). These breakpoints should be interpreted with caution and validated in future large-scale studies. Conclusions: Short-video viewing is associated in a dose-dependent manner with sleep quality, ocular surface health, and psychological well-being, with effect sizes generally comparable to, and in some domains (e.g., dry eye and dream anxiety) somewhat larger than, those reported for gaming addiction. The strong association between mobile phone addiction (MAPI) and dream anxiety (VDAS) highlights the distinctive association pattern of pre-sleep short-video use. Future interventional studies are warranted to determine the optimal duration limit. Our exploratory thresholds (approximately 3.2 h/day for sleep and 4.0 h/day for OSDI) provide preliminary reference points, but the specific target duration remains to be established through adequately powered studies designed to compare multiple cut-off levels. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC + ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC + ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC + ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

Data centers can provide demand-side flexibility by coordinating computing workloads, cooling systems, and on-site energy resources. However, facility demand varies with information technology (IT) load and cooling operation, making a fixed power usage effectiveness (PUE) or a forecast independent of planned controls inconsistent with dispatch. This paper proposes a control-aware, multi-horizon PUE forecasting framework for coordinated data center demand-side management (DSM) and microgrid dispatch. The key idea of this control-aware approach is to forecast PUE using planned workload and cooling schedules as inputs. A power-consistent Temporal Fusion Transformer (PC-TFT) predicts quantiles of non-IT overhead power and rack inlet temperature from telemetry, weather forecasts, admitted requests, and candidate workload and cooling schedules. Facility power and PUE are derived from the algebraic power balance, ensuring consistency among IT, overhead, and facility power and PUE values no lower than 1. Empirical split conformal calibration and temporally dependent scenarios characterize forecast uncertainty. A trajectory-conditioned piecewise-affine control response map with a recursive thermal state links the forecasts to a risk-informed model predictive controller that coordinates workloads, cooling, photovoltaic generation, battery storage, and grid exchange. The proposed framework is validated through EnergyPlus simulations of a Shenzhen data center, coupled with workload and microgrid simulations. Forecasting performance is compared with persistence and matched-input neural baselines, while dispatch is benchmarked against deterministic and oracle controllers. The results demonstrate improved multi-horizon PUE forecasting accuracy and empirical interval calibration, lower operating cost and peak grid demand, higher renewable energy utilization, and fewer service quality violations. These findings indicate that control-aware, power-balance-constrained probabilistic PUE forecasts can provide a reliable basis for coordinated data center DSM and microgrid dispatch. Full article

Rapid growth in artificial intelligence, machine learning, and high-performance computing has substantially increased data centre rack power densities, resulting in higher heat generation and more demanding cooling requirements. As water remains widely used in many cooling systems, understanding the relationship between cooling technologies and water consumption is essential for improving cooling efficiency and sustainability. This paper presents a survey of reported water usage effectiveness (WUE) across 83 data centre entries, providing a combined dataset that links WUE with heat-rejection categories. The reported data shows that 23 of these data centres exceed 0.4 L/kWh, which is a sustainability target specified by the Climate Neutral Data Centre Pact for new data centres in water-stressed regions using potable water. Dry facilities employing closed-loop liquid cooling require essentially no water, while evaporative systems typically report water usage effectiveness values up to 2.5 L/kWh. Reported WUE is a facility-level operational metric, set by the proportion of the IT heat load rejected by evaporation, which depends on the heat-rejection topology, ambient wet-bulb conditions, and operating set points. A higher server-side heat transfer coefficient permits a higher coolant supply temperature for a given chip temperature limit, widening the range of ambient conditions under which heat can be rejected without evaporative assistance. Server-side heat transfer is therefore an enabling condition for low WUE rather than a determinant of it. One-dimensional heat transfer models are developed to estimate heat transfer coefficients for different server-level cooling mechanisms widely used for cooling servers within data centres, including air cooling, single-phase immersion cooling, direct liquid cooling, and two-phase immersion cooling. Air cooling, with the lowest heat transfer coefficient, remains widely used in small-scale facilities, whereas direct liquid cooling and two-phase immersion cooling achieve coefficients up to three orders of magnitude higher and are increasingly deployed in high-density installations. These coefficients are used to derive an equivalent evaporative water demand, an upper-bound estimate of the water that would be evaporated in rejecting the heat each mechanism removes; it shares the units of reported WUE but describes thermal capability rather than facility water consumption. Full article

The increasing complexity of modern power distribution systems, through rising demand, integration of distributed generation, and the scaling of electric vehicles, requires improved demand-side management strategies for effective and reliable operation. This study presents a DSM framework that integrates EV load coordination, DG placement, and load shifting techniques to achieve both economic and reliability improvements in distribution networks. The proposed approach is implemented on the IEEE 33-bus and Cairo 59-bus networks to capture practical operating conditions with higher loading and multiple laterals. The methodology is used to reschedule commercial loads while prioritizing cost minimization and maintaining system constraints. EV load charging demand and DG units are optimally allocated to support voltage stability and reduce power losses. Reliability performance was evaluated using the Customer Total Average Interruption Duration Index, using interruption frequency and duration. The results show that the integrated DSM approach significantly reduces operational cost, peak demand, and energy losses while enhancing system operational cost and reliability. The coordinated interaction between EVs, DGs, and flexible loads proves effective in maintaining a balance between economic efficiency and reliable power delivery. This work highlights the potential of broad sustainability-oriented DSM strategies in supporting the transition toward smarter and more sustainable distribution systems. This ensures that electricity is available to all the consumers at all times just by tweaking the usage pattern of the commercial consumers slightly. This will ensure lesser power losses in the system and better utilization of the available energy thereby creating a sustainable power distribution for the connected consumers. This work aligns with SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities). Full article

The rapid integration of Generative Artificial Intelligence (GenAI) technologies into educational environments has generated new opportunities for developing intelligent systems capable of monitoring learner interactions, modeling learning behaviors, and supporting adaptive educational decision-making. As learners increasingly engage with AI-powered tools for content generation, information retrieval, problem solving, and knowledge construction, educational platforms require robust analytics architectures that can transform human–AI interaction data into actionable insights for instructors, administrators, and learning support systems. This paper proposes a computational architecture for learning behavior analytics in AI-enhanced educational environments. The architecture integrates multiple analytical components, including learner interaction monitoring, behavioral data aggregation, AI utilization profiling, performance-related indicator analysis, and decision-support modules for adaptive intervention and learner support. The proposed framework is designed to capture measurable dimensions of AI-assisted learning behavior, enabling educational systems to identify usage patterns, model learner engagement, and generate analytics-driven recommendations for instructional improvement. The architecture adopts a data-driven approach in which behavioral indicators derived from learner interactions with GenAI tools are processed through learning analytics mechanisms to support predictive modeling, learner classification, and intelligent feedback generation. The framework further incorporates dashboards and reporting components that facilitate real-time monitoring of AI-assisted learning activities and provide evidence-based insights for educational stakeholders. To demonstrate the applicability of the proposed architecture, a pilot implementation was conducted using learner interaction and perception data collected from higher education students. Preliminary analytical results indicate that task-specific AI utilization behaviors provide meaningful behavioral signals that can be incorporated into learner modeling and adaptive learning analytics processes. These findings support the feasibility of integrating GenAI interaction data into intelligent educational systems for monitoring and decision-support purposes. The proposed architecture contributes to the development of next-generation educational technologies by providing a scalable framework for learning behavior analytics, human–AI interaction modeling, and intelligent educational decision support. The study offers practical implications for the design of adaptive learning platforms, educational data analytics systems, and AI-enabled learning environments that support effective and responsible human–AI collaboration. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

Accurate energy efficiency prediction is fundamental to green computing and low-carbon 6G infrastructure. Data centers represent a challenging testbed due to their high energy density and complex device interactions. Existing methods either ignore physical laws or fail to capture spatial dependencies among heterogeneous devices. To address these limitations, this paper proposes a hybrid framework integrating Physics-Informed Neural Networks (PINNs) with Graph Neural Networks (GNNs) for Power Usage Effectiveness (PUE) prediction. Two algorithmic variants are developed: Physically Decomposed PINN-GNN (PDPG) and Unified End-to-End PINN-GNN (UEPG). Physical regularization constraints derived from practical energy and thermal principles are embedded into model training to improve the physical rationality of the prediction results. Validated on real-world data center datasets, the proposed method achieves superior accuracy and robustness over mainstream baselines, providing reliable support for cooling and resource management. Full article

The increasing integration of distributed photovoltaics and wind power in rural grids necessitates enhanced peak-regulation flexibility, for which small Francis turbines offer a promising solution. This study investigates the effect of long–short blade number on the hydraulic performance and energy losses of a representative rural small Francis turbine under constant runner material usage. Five configurations (N = 13–17) are evaluated using SST-DES-based CFD simulations and entropy production theory. At the rated condition, the N = 15 configuration achieves the highest overall efficiency of 93.65%, exceeding N = 17 by 0.19 percentage points and N = 16 by 0.61 percentage points; at high-flow conditions, it maintains a similar advantage of approximately 0.26 percentage points over N = 17. In the low-flow region, the N = 17 scheme exhibits slightly higher efficiencies, with advantages of 0.85 percentage points over N = 15. The N = 15 scheme demonstrates more uniform velocity streamlines, gentler pressure gradients, and smaller high-entropy-production regions across all flow components, particularly at the rated point. Overall, N = 15 provides the best balance between rated-point performance and off-design stability and is recommended for engineering applications. Full article

It is well-known nowadays that the usage of conventional opaque photovoltaics in agricultural practices has negative effects on crops growth, mainly due to the shading effect they cause. On the other hand, most of the emerging semi-transparent solar cells do not demonstrate the appropriate optical characteristics and scalability to attain their viable integration in agriculture, undermining their commercialization. This study deals with the development of wavelength-selective semi-transparent dye-sensitized solar cells (DSSCs) using the scalable slot-die deposition method. These devices are designed to provide high transparency in the photosynthetically active radiation (PAR) region and effectively exploit the near-ultraviolet to blue-visible spectrum for power production, simultaneously protecting cultivations from harmful short-wavelength irradiation. To this aim, a new quinoline-based dye and a highly transparent iodine-free electrolyte were employed in DSSCs, giving an external quantum efficiency of 70% for wavelengths up to 500 nm and a PAR transmittance on the level of 50% (55% crop growth factor). Additionally, the light-to-electricity conversion efficiency of these devices is high for both front- and rear-side illumination under all-weather irradiation conditions (up to 94% bifaciality factor). Finally, two new figures-of-merit (greenhouse compatibility factor, agrivoltaic performance factor) are introduced to quantify the balance of photovoltaic performance and agronomic functionality. Full article

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this research gap by investigating Chongqing EV users’ V2G acceptance, behavioral intention, and influencing mechanisms to provide support for V2G promotion. It targets EV owners in Chongqing’s downtown areas, collecting 295 valid questionnaires, covering users’ demographics, travel-charging habits, and subjective attitudes. Based on technology acceptance and usage theories, it constructs a structural equation model (SEM) with perceived usefulness, ease of use, economic viability, and technological risk as latent variables to analyze their impacts on behavioral intention. Results show that perceived usefulness, perceived ease of use, and economic benefits positively affect behavioral intention, while technology risk perception exerts a negative effect; users with fixed commutes, low-range anxiety, and home charging piles are more receptive, and 70% support V2G but worry about battery wear and plug-in duration. Its innovation lies in integrating EV charging–discharging and travel patterns into the analysis, and its findings enrich new energy technology acceptance theory and provide a theoretical basis for transportation-energy system coordinated planning and V2G development. Full article

This work presents a power-friendly framework for efficient manufacturing test of dynamic voltage and frequency scaling (DVFS)-based 3D Systems-on-Chip (SoCs). The proposed approach employs a through-silicon via (TSV)-based inter-layer test architecture to enable compact and high-speed delivery of test data across stacked dies, while a bus-based space- and time-division multiplexing (SDM/TDM) mechanism distributes test data within each layer. Based on this architecture, a test access mechanism (TAM) optimization method is introduced to reduce TSV usage, improve bandwidth utilization, and minimize test application time. The optimization process uses effective pruning criteria to limit the exploration of inefficient TAM configurations while preserving promising design alternatives. In addition, advanced test-scheduling methods are developed to exploit TDM-based parallelism and flexibility, while explicitly enforcing power constraints at the SoC, layer, and voltage-island levels. These scheduling methods combine fast heuristic construction with metaheuristic optimization techniques to improve solution quality without excessive computational cost. Experimental evaluation on artificial DVFS-based 3D SoC instances demonstrates that the proposed framework achieves reductions in TSV count and effectively limits the overall test time compared with baseline approaches. The results confirm that jointly considering TAM design, DVFS-aware scheduling, and power constraints provides an effective and scalable solution for testing complex DVFS-based 3D SoCs. Full article

Agricultural carbon emissions represent a major source of greenhouse gases and play a critical role in achieving global climate mitigation and sustainable agricultural development targets. In China, the rapid transformation of agricultural production systems has led to substantial spatial heterogeneity in emission patterns and driving mechanisms of agricultural carbon emissions, while the underlying processes at the county scale remain insufficiently understood. This study investigated the spatiotemporal evolution and potential influencing factors of agricultural carbon emissions at the county level from 2000 to 2022 in Guangdong Province, China. First, agricultural carbon emissions were estimated based on a multi-source accounting framework covering land management, crop cultivation, animal production, and straw burning based on internationally recognized emission accounting methods and IPCC global warming potentials. Then, spatial clustering characteristics were analyzed using local spatial autocorrelation (LISA) to identify heterogeneous emission patterns. Finally, an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) was employed to quantify the nonlinear effects and relative contributions of multiple socioeconomic and agricultural drivers. The results showed that agricultural carbon emissions in Guangdong Province exhibited a fluctuating but overall decreasing trend, declining from 50.89 Mt CO₂-eq in 2000 to 39.24 Mt CO₂-eq in 2022, with an overall reduction of 22.9%. High-emission clusters were primarily concentrated in western and northern Guangdong, while low-emission areas were mainly located in the Pearl River Delta (PRD). The RF models demonstrated satisfactory predictive performance, with spatial cross-validated R² values ranging from 0.75 to 0.91 across different years. SHAP analysis suggested that ploughing area, fertilizer and pesticide usage, agricultural machinery power, and primary industry GDP were the dominant factors associated with agricultural carbon emissions, whereas urbanization consistently showed a negative association. Furthermore, these drivers exhibited pronounced nonlinear responses and distinct regional heterogeneity, particularly between the PRD and the western and northern parts of Guangdong Province. These findings suggested that agricultural carbon emissions are jointly influenced by agricultural production intensity, mechanization, and socioeconomic transition and can provide a scientific basis for developing region-specific low-carbon agricultural policies and promoting the sustainable transformation of agricultural systems. Full article

In a closed-inventory large language model (LLM) system such as Online-CADCOM, which recommends engineering tools from a verified inventory, we measure inventory non-compliance, that is, a mention-level event in which the model recommends a tool not present in the verified inventory. We use this inventory-relative sense of hallucination throughout: an out-of-inventory mention may be a fabricated tool or a real commercial tool absent from the curated inventory, so the metric reports inventory non-compliance rather than factual fabrication. We evaluate a three-mechanism mitigation stack consisting of database-grounded context injection, fixed vocabulary constraints, and enforced JavaScript Object Notation (JSON) output across three commercial LLM providers (OpenAI, Anthropic, Google), two model generations, and two output modes (standard and reasoning), totaling 6912 Application Programming Interface (API) calls over 12 configurations. Under a recall-equalized detector adopted as the primary metric, the inventory non-compliance rate, which we denote the hallucination rate (HR) following common usage, decreases from roughly 69–80% to 4–13% under the full architecture. The cross-provider average is similar across the two generations tested (8.5% Generation 1 (Gen1), 6.9% Generation 2 (Gen2)), although per-provider directions diverge. We also examine the C3 configuration, in which only JSON output enforcement is active without grounding. A naive detector reports a large hallucination increase over the unconstrained baseline (+10.1 percentage points (pp) Gen1, +15.1 pp Gen2), but we show this gap is largely a detection-format artifact: structured JSON fields make out-of-inventory tools easy to extract, whereas the same real tools are frequently missed in free text. Under a recall-equalized detector the gap narrows to +2.6 pp (Gen1) and +4.8 pp (Gen2) and remains statistically significant only for two current-generation models, indicating a small, current-generation effect rather than a universal one. Reasoning-mode models provide no statistically significant improvement under architectural constraints. A frequency-weighted audit shows that the majority of remaining out-of-inventory mentions correspond to real engineering tools absent from the platform’s inventory. Under the full architecture, roughly half of responses (pooled $P_{\mathrm{any}}\approx 49.5\%$) still contain at least one such mention, indicating that handling unseen tools remains an open challenge for closed-inventory recommendation systems. Our evidence comes from a single engineering platform with four related electronic-design and power-electronics domains, so the findings characterize this setting rather than recommendation domains in general. Full article

Vertical-axis wind turbines (VAWTs) are recognized as a viable option for wind energy farms due to their compact design and suitability for different wind settings, such as urban and offshore environments. VAWT wind farms have been studied with respect to various turbine spacing and configurations that demonstrate that the VAWT wind farm is well-suited for improving efficiency while requiring less land, compared to horizontal-axis wind turbines (HAWTs). Moreover, the use of combined dual-rotor configurations has recently given attention as a passive strategy to enhance the aerodynamic performance of VAWTs. Despite these advances, the optimal arrangement of VAWT farms, including inter-turbine distances, clustering configurations, and land-use efficiency of such dual rotors, has yet to be explored. This study investigates different clustering scenarios, including vertically aligned pairs and staggered clusters of three turbines, to evaluate their impact on power capture and land usage for a dual-rotor straight-blade vertical-axis wind turbine (DR-SBVAWT). The 2D-dimensional transient (URANS) numerical simulations are conducted using the k-$\omega$ SST turbulence model. Performance indices, namely, total power coefficient and improvement relative to standalone turbines, are analyzed. Wake effects are investigated through detailed velocity contour plots of the wind field. Results reveal that a DR-SBVAWT turbine arrangement can enhance wind farm performance by approximately 25% for two turbines and about 20% for three staggered turbines, with required spacing of 1.5 D and 2.5–3 D, respectively (Here, D is the outer diameter of the DR-SBVAWT). The study overall provides insights into the optimal placement and configuration of DR-SBVAWTs for maximizing energy output while minimizing land usage, offering guidance for the design of more efficient VAWT farms. Full article