Search Results (980)

5G electromagnetic field (EMF) measurement is more challenging than measurement in previous cellular generations because 5G New Radio uses time-division duplexing, flexible bandwidths, beam sweeping, massive MIMO, and user-specific traffic beams. As a result, the measured synchronisation signal block (SSB) or PBCH-DMRS level may not directly represent the maximum exposure produced by data transmission. This motivates a combined tutorial and structured survey of existing 5G EMF measurement studies and procedures. This paper reviews the literature on 5G EMF measurement by classifying existing methods into frequency-selective measurement, code-selective measurement, actual exposure assessment, maximum-exposure extrapolation, and network-counter-based assessment. Representative field studies, public measurement reports, and network-data-based studies are compared according to their measurement scenarios, exposure objectives, and limitations. The paper further discusses key uncertainty sources, including beam/gain offset, TDD duty cycle, bandwidth extrapolation, traffic variation, spatial sampling, and equipment-related uncertainty. Finally, open challenges related to FR2 millimetre-wave measurements and reconfigurable propagation environments are discussed. By combining tutorial background with a structured survey, this paper clarifies 5G EMF measurement procedures, maximum-exposure extrapolation, uncertainty sources, and FR2 millimetre-wave measurement challenges. Full article

Commercial building demand response can support sustainable grid operation by providing demand-side flexibility for renewable-rich power systems; however, baseline-based measurement and verification usually report only event-period gross load reduction. Post-event rebound from heating, ventilation, air-conditioning, and lighting controls can cause gross-only reporting, resulting in overstating the net reduction at the building–grid interface. Standard baseline estimators have not been systematically evaluated in the post-event window. This study formulates a rebound-aware net-reduction accounting framework that reports the event-period gross reduction, post-event rebound, and rebound-adjusted net effect under the same eligible-day baseline structure. The framework was evaluated on the paired ComStock 2025 Release 2 Actual Meteorological Year 2018 profiles for commercial buildings in California, Texas, and New York with five baseline estimators and three rebound-window definitions. In a balanced 3960-building sample, the median true gross reduction was 62.7 kWh and the 2 h rebound was 5.49 kWh. The pooled median post-minus-event mean absolute error was −0.209 kWh per 15 min interval. The material gross-as-net reporting risk affected 70.9% of building-event-method cases, but rebound-aware net reporting was conditionally valuable rather than uniformly superior. These results show that rebound diagnostics can improve transparent and auditable demand response reporting for sustainable grid-interactive building programs. Full article

A new generation of smart buildings and precision agriculture is evolving through the integration of cyber–physical systems (CPS), which combine IoT sensors with machine learning (ML). As such, there is an implicit assumption made by researchers in most of these studies that the ML component represents the decision making mechanism within the overall system. Furthermore, most researchers do not articulate the full scope of the cyber–physical feedback loop linking prediction outputs, operational decisions based upon those predictions, actual actuation of the physical plant or farm operation, and subsequent performance evaluations. The outcome of this paper brings out transferable decision support patterns across domains such as the mechanisms which have proven to be effective in scenarios with low number or quality of data measurements. Specifically, we present a review for two CPS domains that benefit intensely from decision support: smart buildings and precision agriculture. We examined how sensing, data processing, ML, and control modules are combined in practice when creating decision support applications. This resulted in a review of the literature to identify architectural patterns, decision objectives, and feedback mechanisms in both domains. This combination insight paves the way for more flexible and more effective decision making applications compatible with different domains. Full article

The alternative fulfillment mechanism for ecological and environmental damage liability represents a pivotal institutional innovation. Its principal function is to enable liable parties—under exceptional circumstances—to flexibly fulfill their ecological and environmental damage liability. This study adopts an empirical research method to systematically investigate the judicial application of the alternative performance mechanism. Findings reveal that under the guidance of the restorative justice concept, courts increasingly subsume diverse alternative performance modalities under the category of “alternative restoration”, leading to the gradual distortion of the alternative restoration centered on the concept of “equivalent restoration” into a “universal solution” for ecological damage cases. This practical tendency significantly weakens the actual effectiveness of ecological restoration. The purpose of this study is to establish a dual mechanism of alternative fulfillment—distinguishing between alternative restoration and alternative compensation—based on ecological principles and within the framework of the dual responsibilities of “restoration—compensation” for ecological environmental damage. The effectiveness of the dual mechanism is quantitatively evaluated using the AHP-fuzzy comprehensive evaluation. The evaluation results show that, after distinguishing between the two types of alternative responsibilities, the comprehensive evaluation scores for ecological damage remedy in cases of ecological destruction, water pollution, and soil pollution increased from 5.155 to 8.935, from 6.406 to 9.116, and from 6.137 to 9.108, respectively, effectively correcting the functional deviation of the current single-dimensional remedy model. This study clarifies the independent application criteria of alternative restoration and alternative compensation, and provides targeted optimization paths for the judicial application of ecological damage remedy. Full article

The spreading and film formation of organic polymer solutions on liquid surfaces are key processes in coating, printing, and interfacial processing. However, the mechanisms by which aqueous polymers regulate spreading kinetics and film morphology are not yet fully understood. In this study, the free spreading of Poly(3-hexylthiophene-2,5-diyl) (P3HT)/chlorobenzene solution on poly(vinyl alcohol) (PVA) aqueous surface was employed as a model system to investigate how PVA concentration, molecular weight, degree of hydrolysis, and temperature collectively govern spreading behavior and film formation. Video recording was used to monitor the evolution of the spreading and front-edge morphology, while step-profilometry, UV–visible absorption spectroscopy, and atomic force microscopy were employed to characterize the resulting films in terms of thickness distribution, optical uniformity, and surface roughness. The results reveal that PVA can significantly regulate both the spreading kinetics of P3HT/chlorobenzene droplets and the final film morphology. PVA concentration exhibited a non-monotonic effect on spreading behavior, with intermediate concentrations favoring larger spreading areas and more continuous films. Increasing the PVA molecular weight altered the concentration-dependent spreading window and enhanced asymmetry at the spreading front, whereas reducing the degree of hydrolysis decreased interfacial tension and thereby increased the thermodynamic driving force for spreading, yet the actual spreading rate remained constrained by molecular diffusion, interfacial adsorption, and chain-segment rearrangement. Temperature and a saturated chlorobenzene vapor atmosphere further modulated the interplay among solvent evaporation, interfacial driving force, and viscous dissipation. Under optimized conditions, the resulting P3HT films displayed uniform thickness profiles, consistent optical absorption, and nanoscale surface roughness, and could be repeatedly transferred, assembled into well-defined multilayer structures, and printed onto flexible and curved substrates. These findings demonstrate that PVA aqueous subphase provides a tunable low-shear route for transferable P3HT thin-film fabrication and suggests its potential applicability to other polymer film-forming systems. Full article

The construction industry continues to face high levels of accidents despite the use of various safety training approaches, highlighting the need for more effective and responsive methods. This study examines the role of Generative Artificial Intelligence (GenAI) in potentially improving construction safety training by exploring the development of training practices and identifying the shortcomings of existing approaches. A systematic literature review (SLR) was conducted to analyse safety training methods and emerging GenAI applications, followed by validation interviews with industry experts in South Australia to ensure practical relevance. Emergent findings show that safety training has progressed through three main stages: instructor-led, digital and GenAI-enabled. However, instructor-led and digital approaches remain limited by non-interactive learning, limited flexibility to different learner needs, lack of real-time feedback and weak alignment with actual site conditions. In contrast, GenAI offers opportunities to support more interactive, personalised and context-aware training through technologies such as large language models (LLMs), adaptive learning systems, computer vision and scenario generation. Despite these benefits, significant challenges related to data quality, system reliability, ethical concerns and organisational readiness continue to affect implementation. Based on these findings, the study develops an integrated framework that links training evolution, key challenges and GenAI capabilities, providing practical guidance to improve safety training in construction. Full article

With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth uncertainties and fails to fully utilize the flexibility of demand-side resources during the planning phase. This paper proposes a robust planning method for PIESs considering dynamic demand response and multi-timescale uncertainties. First, an energy flow framework encompassing cooling, heating, electricity, gas, and hydrogen is constructed. To overcome the limitations of traditional fixed-boundary DR, a dynamic elastic DR mechanism featuring transferable, substitutable, and curtailable types is established. Transferable demand boundaries are defined by a price–demand elasticity matrix, and actual responses are dynamically adjusted in synergy with system power balance conditions for optimal configuration. Second, multivariate dynamic time warping and hierarchical clustering algorithms derive typical daily scenarios accounting for short-term uncertainties. Finally, information gap decision theory characterizes long-term load growth uncertainty, constructing a robust planning model addressing both timescales. Case studies show that flexible resources and demand response reduce lifecycle cost by 55.24% and carbon emissions by 47.75%. The proposed demand response method further cuts costs by 153,800 yuan and emissions by 11.36%. The robust planning method synergistically addresses multi-timescale uncertainties, ensuring economy while maximizing resilience to uncertain fluctuations. Full article

Against the backdrop of China’s dual-carbon goals and the development of new power systems, the large-scale integration of renewable energy has intensified system regulation requirements and imposed higher demands on the low-carbon performance and flexibility of electricity market clearing mechanisms. To address the inability of conventional static carbon emission factors to accurately reflect the actual emission levels of coal-fired units, this paper proposes a joint energy and frequency regulation ancillary service clearing model incorporating dynamic carbon emission factors. First, a Transformer-based dynamic carbon emission factor model is developed using features such as unit output, load rate, start-up and shutdown status, and unit type to characterize the dynamic variation in the carbon emission intensity of coal-fired units. Second, a coordinated day-ahead and intraday market clearing model is established to jointly optimize unit commitment, generation scheduling, frequency regulation capacity allocation, energy storage operation, and renewable energy accommodation, thereby achieving coordinated improvements in economic efficiency, low-carbon performance, and operational flexibility. Case studies based on actual data from a provincial power grid in southern China demonstrate that the proposed model increases the renewable energy accommodation rate by 2.01%, reduces the total system cost by 1.51%, lowers total carbon emissions by 3.52%, and decreases carbon emission intensity by 5.15%. The results confirm that incorporating dynamic carbon emission factors into joint market clearing can effectively improve both the economic performance and emission reduction capability of the power system. Full article

With the increasing penetration of behind-the-meter photovoltaic generation and distributed energy storage, distribution system operators usually observe only the net load at the point of common coupling, while the actual user load and energy storage charging/discharging power are difficult to measure directly. To address this problem, this paper proposes a mode-aware constrained inverse optimization method for behind-the-meter distributed energy storage power estimation under fixed time-of-use tariffs. The proposed method uses net load, photovoltaic power, and tariff information as inputs and estimates the hidden user load, storage power, SOC trajectory, and dominant storage arbitrage mode. A mode-aware joint representation model is developed by introducing single-cycle and dual-cycle charge–discharge templates, daily action intensity factors, mode weights, and local correction terms. In addition, power limits, SOC dynamics, SOC bounds, daily energy balance constraints, tariff-response consistency, and mode selection penalty are incorporated into the inverse optimization framework to improve the physical feasibility and interpretability of the estimation results. Case studies are conducted using a 40-day hybrid dataset with a 1 h sampling interval and a 70%/30% training/testing split. The dataset is constructed from park-level user load and photovoltaic data, while the storage power profile is reconstructed according to typical time-of-use arbitrage operation. For the main dual-cycle testing case, the NRMSEs of storage power, user load, and net load are 14.75%, 3.90%, and 3.76%, respectively. The results show that the proposed method can recover the main variation trend of hidden storage power under the studied fixed time-of-use tariff scenario and provides a preliminary basis for park-level storage monitoring and flexible resource perception. Full article

Pumped hydro storage remains one of the most established technologies for balancing supply and demand in electricity markets with high shares of renewable energy. This paper investigates the short-term economic optimization of a pumped hydro storage plant operating under real day-ahead market conditions. A Mixed-Integer Linear Programming model is used to optimize hourly dispatch decisions based on actual day-ahead electricity prices in Slovenia for the year 2024. The model accounts for technical constraints, including turbine and pump capacities, round-trip efficiency, energy storage limits, and restricted startup frequencies. The simulation results show that pumped hydro storage can achieve a positive market-based operating result by responding effectively to price volatility and frequent negative pricing events. Seasonal variations reveal higher revenues during summer months due to solar overproduction. The findings confirm the potential of pumped hydro storage to enhance grid flexibility and support the implementation of national energy transition objectives. By linking large-scale energy storage operations with renewable energy integration, grid flexibility, and market-based dispatch, the study also contributes to the technical and economic dimensions of sustainable energy system development. Full article

Integrated sizing and shape optimization of structural layouts often encounters inherent computational difficulties under nonlinear structural responses and transient buckling criteria. These challenges primarily stem from disjointed sub-problems and localized numerical constraints. This work proposes an integrated optimization methodology utilizing a unified flexible multibody dynamics (FMBD) architecture, with the globally convergent method of moving asymptotes (GCMMA) serving as the mathematical programming solver. By leveraging time-domain dynamic relaxation, complex structural phenomena—including localized post-buckling trajectories and structure-mechanism structural transitions—are mapped into standard kinematic displacement bounds, which are subsequently resolved via the gradient-based solver. Comparative analyses against classic static benchmarks demonstrate that the method’s dynamic and geometric nonlinear characteristics allow the design to naturally circumvent various failure modes associated with ideal results under actual loading, yielding outcomes that better align with engineering requirements. Furthermore, the use of displacement constraints avoids the overly restrictive limitations that buckling criteria often impose on the design space; the ability to simultaneously accommodate and rapidly implement both structural and mechanical configurations expands the optimization space, resulting in significantly lighter structures and mechanisms. This method offers a versatile, stable, and complementary computational pathway for the conceptual design and early-stage exploration of integrated size and shape optimization for structures and mechanisms. Full article

This study presents a nuanced understanding of how butterflies may respond to climate change, suggesting that although many species could experience noticeable contractions in their climatic niches, a substantial number may still have the potential to considerably expand their geographic ranges. We modeled the distributions of 200 butterfly species across Asia using the MaxEnt algorithm. Habitat suitability maps were produced for both current conditions and future scenarios under moderate (SSP245) and high (SSP585) greenhouse gas emissions. To analyze niche dynamics, we projected the realized niches of species onto principal component environmental spaces, which allowed us to quantify key processes including niche stability, expansion, unfilling, abandonment, and pioneering. These indices were then mapped spatially to identify potential changes in climatic niche configurations and geographic distributions in response to climate change. Our findings indicate that by 2070, under the moderate-emissions scenario (SSP245), 185 species are projected to show expansion in suitable habitat by an average of 38%, while 15 species may face an average decline of 11%. Under the high-emissions scenario (SSP585), 184 species are projected to show increases in suitable habitat by 55% on average, with 16 species showing an average decrease of 13%. Additionally, species are predicted to maintain approximately 63% of their current climatic niche stability under SSP585. Niche expansion averages 12%, reflecting potential range growth, whereas unfilling accounts for around 16%, indicating possible loss of some currently suitable areas. Niche abandonment or niche contraction, representing climatic spaces no longer projected to be occupied, comprises about 8%, and pioneering into entirely new climatic conditions remains limited at roughly 1%. The modeled presence of niche unfilling and abandonment suggests that butterflies may retain considerable ecological flexibility, potentially allowing them to shift into new environments under changing climate conditions. This projected adaptability may enhance their resilience by facilitating potential movement into novel or previously unoccupied habitats, though actual range shifts will depend on factors beyond climatic suitability alone. Full article

The increasing penetration of renewable generation and flexible loads has made modern power systems operate under highly variable and diverse conditions. For power-system planning studies, static power-system analysis plays an important role in characterizing the security and stability behavior of these operating conditions. In such planning tasks, annual or long-term hourly datasets are often needed to capture temporal variations in renewable generation, load, and power-flow patterns, but performing power-flow-based static analysis for every operating condition can be computationally expensive, especially for targets that require repeated power-flow-based calculations. Therefore, an effective operating-condition reduction framework is needed to select a compact yet representative subset and reconstruct the overall static-analysis profile required for variation trends and distribution analysis. To address this problem, this paper proposes a problem-driven scenario reduction framework based on batch-attention-based self-supervision feature selection (A-SFS) for simplifying large-scale power-flow-based static analysis. Instead of clustering operating conditions only according to their geometric similarity in the original feature space, the proposed framework incorporates the downstream static-analysis target into the reduction process. Target values are first computed for only a small portion of the operating-condition dataset, and A-SFS is then used to learn target-relevant features and their importance weights. Based on the learned weighted feature space, all operating conditions are clustered using weighted K-means++, and the actual operating condition closest to each cluster centroid is selected as the representative scenario. The downstream target evaluation is then performed only on these representative scenarios, and their target values are assigned to the operating conditions within the same clusters to reconstruct the overall target-value profile of the full dataset. The proposed framework is validated on a yearly RTS-GMLC operating-condition dataset using two representative static-analysis targets, namely load margin and the minimum singular value of the power-flow Jacobian, ${\sigma }_{min}$. The results show that the proposed target-aware clustering framework can effectively reconstruct the overall static-analysis profile of the full operating-condition dataset while preserving the relative ranking of different operating conditions. In the best-M comparison, the proposed method achieves MAPEs of 19.56% for load margin and 12.85% for ${\sigma }_{min}$, with corresponding Spearman coefficients of 0.8380 and 0.8755, respectively. Full article

As the core platform supporting diverse user-side loads, the distribution network plays a critical role in the development of new power systems. To address the significant variations in terminal users’ power supply reliability requirements, this article proposes a two-stage distribution network planning method that optimally coordinates economic efficiency and reliability. First, a multi-type load-specific reliability evaluation index system is established. Using the K-means clustering algorithm, combined with geographic coordinates and load attribute feature matrices, a precision power supply zoning scheme is implemented. Second, considering the diverse demands of different zones, a two-stage distribution network planning model is developed. Finally, the model is solved using a binary particle swarm optimization algorithm (BPSO), and simulation verification is conducted using a case study of an actual distribution network in a certain area of Tianjin. The results indicate that, compared to traditional single-objective planning schemes, the proposed method achieves an effective balance between economic efficiency and reliability. While ensuring the power supply level for critical users, it enhances the flexibility and science of distribution network planning, thereby providing a decision-making reference for the grid-based planning and construction of a smart distribution network. Full article

Background/Objectives: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has been associated with potential clinical and economic benefits; however, its adoption among patients remains limited in some healthcare settings. This study examined patients’ intention to use RM services by applying an extended Technology Acceptance Model (TAM) that incorporates perceived effectiveness (PE), perceived barriers (PB), perceived threat (PT), and economic considerations, as well as the influence of socioeconomic factors. Methods: A cross-sectional survey was conducted among 104 patients with CIEDs in Taiwan using validated questionnaires. Structural equation modeling (SEM) was employed to examine the relationships among the proposed constructs. The association between intention to use and actual service utilization was explored. The correlations between sociodemographic factors and the constructs were analyzed using analysis of variance (ANOVA). Results: SEM showed that perceived effectiveness (PE), perceived usefulness (PU) and perceived ease of use (PEOU) were significantly associated with intention to use RM services, with economic considerations also having a significant contribution. Intention to use RM services further predicted actual adoption. However, PB and PT did not moderate these relationships. Sociodemographic factors influenced RM acceptance, with younger, more educated, employed, higher-income, and professionally employed patients reporting stronger perceptions and greater intention to use RM. Conclusions: This study reinforces the TAM framework in the context of health-related technology adoption. Overall, the adoption of RM services is complex and shaped by psychological, economic, and demographic factors, highlighting the need for user-friendly design, targeted education on clinical benefits, and flexible pricing and reimbursement strategies to improve equitable and sustained use. Full article