Search Results (282)

Bridge-arm reactors subjected to long-term AC–DC composite currents with multiple harmonics may develop non-uniform winding temperature rise and thermal-expansion mismatch, leading to localized thermal stress concentrations and potential insulation cracking. Unlike previous studies focused mainly on purely AC conditions, this study investigates a ±800 kV dry-type air-core bridge-arm reactor and develops a thermo-mechanical model incorporating AC–DC composite currents and harmonic losses. To mitigate thermal stress concentration, an axially segmented configuration is proposed to relieve the restraint associated with cumulative axial thermal expansion. The results show that a 65% axial segmentation ratio provides the best stress-regulation performance among the investigated cases. Under AC–DC composite conditions containing second- and fifth-order harmonics, the maximum Von Mises stress and maximum first-principal stress decrease by 33.42% and 38.11%, respectively, while the stress distribution becomes more uniform. The analysis is based on a two-dimensional axisymmetric model with one-way thermo-mechanical coupling and excludes long-term cyclic thermal aging and interfacial slip between winding and insulation layers. These findings provide theoretical support for the stress-oriented structural design and reliability assessment of high-capacity bridge-arm reactors. Full article

Background: High-dose-rate interventional radiotherapy (HDR IRT) relies on accurate dose calculations to ensure safe and effective treatment. The AAPM TG-43 formalism, based on homogeneous water assumptions, has long been the clinical standard, but the growing use of patient-specific applicators and heterogeneous materials challenges its accuracy. Materials and Methods: A literature narrative review was performed using PubMed and Scopus to identify studies comparing TG-43 and model-based dose calculation algorithms (MBDCAs), including deterministic methods (ACE, Acuros BV) and Monte Carlo simulations. Thirty-five studies were selected and qualitatively analyzed. Results: TG-43 yielded higher dose compared with MBDCAs, particularly in the presence of tissue heterogeneities, air gaps, shielding materials, and limited scatter conditions. Differences ranged from 2 to 5% in relatively homogeneous settings to more than 10–20% in complex geometries such as superficial mould treatments and head-and-neck IRT. Model-based approaches showed better agreement with Monte Carlo simulations and experimental measurements, especially in contact HDR IRT and applications involving 3D-printed applicators. Conclusion: While TG-43 remains clinically established and widely used, its limitations are increasingly evident in modern personalized HDR IRT. Model-based dose calculation algorithms provide greater dosimetric accuracy and should be progressively integrated into clinical practice, particularly in treatments involving significant heterogeneities. Full article

To meet national and global air quality and climate ceilings, it is essential to provide farm-level decision support tools for mitigating gaseous emissions from agriculture. A major challenge is to develop reliable tools that can be adapted to country-specific conditions, particularly in countries where such tools are currently lacking, and support farmers in assessing emission mitigation measures. To address this challenge, a Prototype Decision Support Tool (PDST) for estimating and mitigating gaseous emissions at the livestock farm scale was developed based on the FarmAC whole-farm model. The PDST supports livestock farm-level assessment of carbon emissions, including CH₄ and CO₂, and nitrogen-related emissions, including N₂O and NH₃. Emissions were estimated using the IPCC 2006 Guidelines, their 2019 Refinement, and the EMEP/EEA 2023 methodology. The PDST was applied to two intensive pig farms in Greece, both with fully slatted housing and outdoor slurry tank storage, and two intensive dairy cattle farms, one in Greece and one in Poland, both using deep-litter housing with solid manure storage. For these farms, the PDST estimated total annual emissions of 1.58 and 1.54 kg CO₂eq per kg of pig live weight and 0.80 and 0.66 kg CO₂-eq per kg of raw milk, respectively. These estimates were consistent with values reported in the literature for comparable production systems and emission sources, supporting the preliminary consistency of the PDST outputs. The PDST can form the software basis to support stakeholders in choosing farm-level practices that specifically reduce emissions. Full article

With the increasing variety of electric vehicles (EVs) joining virtual power plants (VPPs), VPP operators increasingly require precise and tailored models for schedulable EV energy. Based on a publicly available anonymous EV charging power dataset, EV users are clustered through a weighted K-means++ algorithm. Secondly, based on the results of clustering, we analyzed the daily traveling patterns of various types of EVs, including commuting EVs, electric light-duty trucks (ELDTs) and electric tractors (ETs), and then customized the all-day schedulable energy domain model (SEDM) for each category. Subsequently, an optimal bidding strategy for a VPP consisting of diversified-member EVs, air conditionings (ACs), energy storage (ES) and distributed energy resources (DERs) is constructed. By modifying the levels of participation in supplementation and absorption of DERs among VPP members, while integrating considerations such as user comfort, EV defying rate, and seasonal variability, diverse VPP operational frameworks are established. Finally, using the Gurobi solver, the optimal bidding strategies and profit results under different scenarios are derived. The results indicate that (1) increasing the VPP members’ participation in the supplementation and absorption of DERs will bring higher benefits to both the VPP and its members; (2) with the increased sensitivity of users to room temperature and range anxiety, the demand response capacity of AC clusters decreases, reducing EV clusters’ market participation and VPP profits; and (3) among various types of EVs, ELDTs and ETs have a larger battery energy adjustment range, which can fully supplement the output shortfalls of DERs. Therefore, these EVs prove to be a good supplement for the improvement of VPP’s schedule capability and profitability. Full article

Driven by the increasing energy density of battery energy storage systems, immersion cooling (IC) has emerged as a promising approach for mitigating thermal runaway (TR) hazards. In this study, overcharge-induced TR tests were conducted on commercial 314 Ah lithium iron phosphate batteries in an accelerating rate calorimeter to compare their thermal, pressure, mass loss, and gas venting responses under air cooling (AC) and static ester-based immersion cooling. For the two cells tested, internal short circuit onset occurred at 1150 s under AC and 1377 s under IC, while TR was triggered at 1232 and 1404 s, respectively. The peak surface temperature decreased from 422.4 °C under AC to 302.4 °C under IC, and the maximum surface temperature difference was reduced by approximately 31%. The maximum chamber pressure rise rate decreased from 3.12 to 1.68 kPa/s, although a higher late-stage cumulative pressure was observed under IC within the sealed ARC chamber. Battery mass loss decreased from 1014.2 g (18.27%) under AC to 845.2 g (15.24%) under IC. In addition, the CO₂ fraction in the post-cooling gas mixture increased from 30.4% to 38.4%, while the H₂ fraction decreased from 43.6% to 36.9%. Based on the modified Le Chatelier calculation, the estimated lower explosive limit increased from 6.16% to 7.18%, suggesting lower composition-based ignitability under the adopted assumptions. Overall, the tested static ester-based immersion cooling configuration delayed TR evolution, reduced peak thermal response and mass loss, and moderated the transient pressure rise under the present experimental conditions. These findings provide experimental reference data for the thermal-safety design of large-capacity battery energy storage systems. Full article

Background: Trichosporon asahii is an emerging opportunistic yeast of growing concern in nosocomial settings, particularly in immunocompromised critically ill patients. Outbreaks in intensive care units remain infrequently reported, and environmental reservoirs are seldom fully characterized. Methods: A prospective outbreak investigation was conducted from 16 August to 29 October 2024 at a 496-bed high-complexity university hospital in Cali, Colombia. Case definitions distinguished healthcare-associated infection (HCAI) from colonization. Active surveillance included clinical cultures, environmental sampling of surfaces, biomedical equipment, and air conditioning duct systems. Microbiological identification was performed using MALDI-ToF mass spectrometry. Results: Nine cases were identified among 74 patients (6.76% attack rate); five were HCAIs, and four were colonizations. Overall mortality was 44%, though 0% was attributable to T. asahii. Primary risk factors included prolonged hospitalization, invasive devices, and broad-spectrum antibiotics. While environmental cultures were negative, maintenance records revealed unscheduled air duct cleaning and intermittent AC failures in the affected unit. Conclusions: Epidemiological evidence suggests that air conditioning malfunctions and temperature fluctuations facilitated fungal dispersal. The outbreak was contained through unit closure, hydrogen peroxide vaporization, and reinforced hand hygiene, highlighting the necessity of rigorous ventilation maintenance in high-complexity units. Full article

For multivariate-input-driven multi-step temperature forecasting in air-conditioning systems, existing models often rely on a single temporal receptive field, limiting the amount of temporal information captured and hindering the effective characterization of multi-scale dynamic features. Moreover, accurately capturing condition-dependent variations in the contributions of different variables to the prediction target remains challenging, resulting in persistent error accumulation over long forecasting horizons. To address these issues, this paper proposes a Multi-scale Interactive Recalibrated Mamba (MIRA-Mamba) forecasting framework. The multi-scale temporal interaction embedding module extracts and fuses dynamic features from different temporal receptive fields, enhancing the representation of multivariate coupling relationships and alleviating information loss caused by single-scale representations. The bidirectional state–space recalibration encoder employs bidirectional Mamba to capture long-range temporal dependencies and incorporates a feature contribution recalibration mechanism to adaptively adjust variable weights, thereby strengthening high-contribution variables and suppressing interference from low-contribution variables. In addition, a private dataset, Hisense-AC, is constructed from real air-conditioning operation logs collected from 15 cities over half a year. Experimental results show that MIRA-Mamba achieves the lowest forecasting errors across all four operating modes on the Hisense-AC dataset, with a maximum MSE reduction of 5.4% compared with the best-performing baseline. On six public time-series datasets, MIRA-Mamba obtains the best MSE in 17 out of 24 forecasting settings, demonstrating its effectiveness in real HVAC scenarios and its generalization capability on public benchmarks. Full article

Steel bridge deck pavements in coastal hot–humid regions are often exposed to the combined effects of moisture, salt, and temperature, which can accelerate material deterioration and shorten service life. To clarify the durability behavior of typical paving materials under such conditions, a comparative study was conducted on three asphalt mixtures used for steel bridge deck pavements: epoxy asphalt mixture (EA-10), dense-graded asphalt mixture (AC-13), and stone mastic asphalt mixture (SMA-10). The mixtures were subjected to hygrothermal salt-water cycling using a mixed chloride-sulfate solution, and their durability was evaluated through air void content, indirect tensile strength, and four-point bending fatigue tests. The results showed varying degrees of deterioration. The air void content of AC-13 increased by about 41.4% after 28 d at 60 °C, suggesting greater susceptibility to internal void damage under severe conditioning. The indirect tensile strength also decreased with wet–dry cycling; at 60 °C and 28 d, the strength retention of EA-10 remained 76.9%, higher than those of AC-13 and SMA-10. After conditioning at 60 °C, the fitted slope of fatigue life for SMA-10 reached −0.0052, compared with −0.0044 for AC-13 and 0.0027 for EA-10, indicating that SMA-10 was the most sensitive to hygrothermal salt attack, whereas EA-10 was the least affected. Overall, the resistance to hygrothermal salt-water damage followed the order EA-10 > AC-13 > SMA-10. The findings help clarify the durability behavior of steel bridge deck paving materials in coastal environments and provide support for durability-oriented material selection. Full article

On-site withstand voltage testing is essential for evaluating insulation performance and detecting defects in UHV AC equipment; however, existing safety distance criteria are mainly based on empirical experience or extrapolated from low-altitude and lower-voltage conditions, limiting their applicability. To address this issue, a systematic framework for safety distance calculation and altitude correction is developed. The selection principles and circuit configuration of the test system are analyzed to clarify the constraints between power capacity and tuning under high-voltage, large-capacity conditions. Based on air-gap discharge characteristics, a minimum safety distance model is established for the 1000 kV main transformer with respect to grounded structures and personnel. Meteorological factors and proximity effects are further incorporated to propose correction methods and on-site zoning strategies. Results indicate that a baseline safety distance of approximately 10 m is appropriate at altitudes up to 1000 m, and the model captures the nonlinear degradation of insulation strength in long air gaps at higher altitudes. A case study at 3620 m yields a minimum safety distance of 16.4 m, providing a quantitative basis for safe UHV AC on-site testing under varying altitude conditions. Full article

The integration of advanced additive manufacturing technologies, particularly 3D printing (3DP), also known as Additive Construction (AC), could influence a shift in the construction industry towards improved efficiency and automation. This research evaluated the effect on hardened properties of two different concrete mixes for use in 3DP based on the presence or absence of alkaline-resistant (AR) glass fibers. Furthermore, three different curing methods were evaluated: air-curing, plastic-covered curing, and spray-curing. Concrete beams were printed for flexural testing, and cores were taken from other depositions to evaluate compressive strength and split-tensile strength. An analysis of the size and location of cracks on the beams after curing was performed for the different mixes and curing methods. For beams without fibers, plastic-covered curing produced the highest flexural modulus values, and air-curing produced the lowest flexural modulus values. Plastic-cured beams with fibers had higher flexural modulus values than the air-cured beams with fibers. However, the spray-cured beams with fibers produced somewhat anomalous results, with one flexural modulus value being larger than those of the plastic-cured beams, and the other flexural modulus value being less than those of the air-cured beams. All 28-day compressive strengths and split-tensile strengths across mixes and curing conditions fell within a small band ranging between ~19.3–22.1 MPa and ~1.7–2.0 MPa (~2800–3200 psi, and 240–290 psi), respectively. There was a large amount of scatter in some of the tests. It appears that neither the presence of the AR-glass fibers, nor the type of curing had a large influence on compressive strength or split-tensile strength. Results showed that the addition of fibers and the use of the plastic during curing significantly reduced the occurrence, the width, and the depth of cracks as a result resulting from the curing process. Plastic-curing was the most effective curing method for minimizing the occurrence of cracks. Any cracks that formed during plastic-curing were extremely fine and had little or no effect on mechanical properties. Full article

A novel Fe-based alloy, designated as AMSD, was designed using a machine-learning-assisted high-throughput strategy, and it was successfully fabricated by laser powder bed fusion (LPBF) additive manufacturing without crack formation. This work systematically investigated the effects of post-process cooling rates on the microstructure and mechanical performance of the LPBF-fabricated AMSD alloy. After solution treatment at 1200 °C for 2 h, two cooling conditions, namely air cooling (AC) and water quenching (WQ), were applied, followed by aging at 500 °C for 24 h. It was found that the as-built (AB) alloy exhibited a typical cellular structure, epitaxial columnar grains, and a continuous intercellular segregation network. Post-processing eliminated the segregation network and promoted a more homogeneous microstructure with multiscale precipitates. Compared with AC condition, WQ preserved a finer and denser population of grain-boundary borides and achieved a superior strength–ductility balance, with a UTS of 1072 ± 15 MPa and an elongation of 18.2 ± 0.3% achieved. In contrast, the AC sample exhibited a higher UTS of 1436 ± 45 MPa but lower ductility. These results demonstrate that post-process cooling rates play a key role in regulating precipitate evolution and mechanical performance in LPBF Fe-based alloys. Full article

Accurate prediction of air conditioning (AC) loads at the feeder level is imperative for the effective implementation of targeted demand response and effective peak load management. However, it is challenged by data heterogeneity and scarcity, especially for new feeders. Traditional methods often fail under such conditions. This paper proposes a meta-learning-based Dynamic Hierarchical Forecasting Framework (DHFF) explicitly designed for efficient few-shot load forecasting. A core gating network dynamically fuses predictions from a global unified model and a feeder specific model, adapting based on context. The framework’s effectiveness was validated through dual testing scenarios. Outstanding performance was achieved in data-rich environments, with an RMSE of 0.0135, improving upon a strong LSTM baseline by nearly 80%. Furthermore, rigorous few-shot experiments confirmed its primary design goal: under extreme data scarcity (e.g., 5% data), DHFF demonstrated superior accuracy, improving up to 7.01% over standard approaches by intelligently leveraging generalized knowledge. These results validate DHFF as an adaptive, high-performing solution across both data-scarce and data-rich feeder forecasting scenarios. Full article

In future active distribution networks with high penetrations of renewable energy, flexible loads are expected to play an increasingly important role as reserve resources to support the sustainable and reliable operation of power grids. Accurate evaluation of flexible load reserve capacity is therefore essential for reliable reserve scheduling. Existing research mainly focuses on the operational characteristics and physical constraints of flexible loads, while insufficiently accounting for user response willingness and the uncertainty of user decision-making behavior, which may lead to biased reserve capacity assessments and impair the sustainability of reserve supply in actual grid operation. To address this issue, this paper proposes a results-oriented reserve capacity evaluation method for flexible loads that explicitly incorporates user response willingness. Specifically, a fuzzy logic system is developed to quantitatively characterize the response willingness of electric vehicle (EV) and air-conditioning (AC) users under multiple influencing factors. Then, a probabilistic modeling approach for user decision-making behavior is established using the theory of planned behavior, enabling explicit representation of behavioral uncertainty. Furthermore, a comprehensive reserve capacity evaluation framework for flexible loads is constructed by integrating user willingness states, sustainable response duration, and operational power constraints. Finally, the case studies demonstrate that the proposed method can effectively improve the objectivity of flexible load reserve capacity assessments while maintaining high user participation willingness, thus supporting the long-term sustainable application of flexible loads as grid reserve resources. Full article

This study applies a dynamic strategic foresight to examine how Unmanned Aerial Systems (UAS)-based Advanced Air Mobility (AAM), supported by Advanced Communication Systems (ACS), can be integrated into a coherent System of Systems (SoS) for sustainable and effective Disaster Management (DM). These three domains (AAM, ACS, and DM) form a strongly coupled Internet of Things (IoT) triad within an integrated SoS. Using lessons learned from previous or running research projects of the contributing authors, i.e., SUDEM, REGUAS, 5G!Drones, and ETHER, the foresight identifies key enablers—including resilient 5G/6G communication architectures, interoperable data fusion frameworks, and UAS-supported situational awareness. It highlights structural challenges such as fragmented standards, limited cross-agency data integration, and gaps in ACS redundancy for emergency operations. The resulting roadmap outlines development priorities for ACS-enabled AAM, from unified communication protocols and hybrid TN-NTN architectures to education and capacity-building for digital-centric DM. Practically, the findings suggest that policymakers should prioritise harmonised regulatory frameworks for AAM-ACS interoperability and invest in global data exchange standards, while system designers should incorporate redundant communication layers and modular SoS architectures to ensure operational continuity under extreme conditions. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article