Search Results (10,714)

Indoor humidity regulation is important for building comfort, occupant health, and energy saving. Passive moisture-regulating concrete provides a low-energy solution; however, the moisture-regulating performance of thermally modified zeolite concrete and related prediction methods remain insufficiently studied. This study thermally modified natural clinoptilolite zeolite powder and used it as an equal-mass cement replacement. X-ray diffraction, moisture adsorption–desorption tests, and orthogonal design were employed to evaluate the modification effect and mixture parameters. A multilayer perceptron (MLP) enhanced using an adaptation of the Synthetic Minority Over-sampling Technique (SMOTE) and optimized by a mapping-improved Mountain Gazelle Optimizer (MMGO) was developed to predict unit-area moisture absorption. The novelty lies in integrating thermally modified zeolite concrete with an intelligent prediction framework. The results demonstrate improved moisture regulation of concrete and the potential of the proposed model for performance prediction. Further validation with larger independent datasets is required. Full article

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R² Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance. Full article

Night-time low temperature remains a major constraint on thermal stability, crop safety and energy-efficient operation in winter solar greenhouses, especially when heat release and auxiliary heating are triggered only after the indoor temperature has approached a low temperature threshold. This study developed a temperature prediction-based active–passive heat storage and release system integrating Internet of Things monitoring, liquid neural network (LNN)-based multi-horizon temperature forecasting, heat storage and release circulation, and decision-making control. The LNN achieved the best forecasting performance among the tested models, with MAE/RMSE values of 0.620/0.775, 0.683/0.854 and 0.758/0.948 °C for 12 h, 24 h and 48 h forecasts, respectively, and was embedded into the system for prediction-assisted operation. A continuous 30-day winter test was conducted in two consecutive stages: heat storage and release without predictive control (HS-NPC, days 1–15) and with prediction-assisted operation (HS-PC, days 16–30). During the consecutive-stage winter test, HS-PC showed higher daily minimum indoor temperature and night-time mean temperature than HS-NPC by 1.92 °C and 4.29 °C, respectively, while reducing daily exposure below 13 °C and 10 °C by 55.0% and 98.2%. Stage-based equivalent input-energy evaluation indicated reductions of 17.1% and 34.3% for HS-NPC and HS-PC relative to the corresponding TG reference periods, respectively. Because HS-NPC and HS-PC were tested in consecutive weather windows rather than in fully synchronized parallel experiments, these improvements should be interpreted as stage-based operational benefits supported by the TG reference and outdoor environmental statistics, rather than as completely weather-independent causal effects. These results indicate that integrating temperature forecasting with heat storage and release regulation can improve low-temperature buffering and energy-saving operation in winter solar greenhouses, while further synchronized or weather-normalized validation is still needed. Full article

Historic buildings account for a significant proportion of the existing building stock, yet their HVAC systems often operate inefficiently and consume substantial energy. To identify typical HVAC energy use patterns and support energy management, this study used a calibrated EnergyPlus v22.2.0 model with outdoor air temperature as the independent variable and HVAC electricity, sensible heating energy, and sensible cooling energy as the dependent variables within a change-point analysis framework. Latin hypercube sampling (LHS) was applied to five variables within predefined ranges, and 2800 parametric variants were generated from four archetype models. Base load, change-point temperature, and heating and cooling slopes were extracted, and three clustering methods—GMM, K-means, and HDBSCAN—were compared. HVAC electricity was primarily characterized by a 5P change-point model, whereas sensible heating energy and sensible cooling energy were stably represented by 3P heating and 3P cooling models, respectively. K-means performed best overall for HVAC electricity pattern recognition, and both sensible heating energy and sensible cooling energy showed advantages for K-means over HDBSCAN. Robustness analysis indicated high stability for all three energy variables, with sensible heating energy showing the best performance. These findings provide a basis for HVAC operation diagnosis and energy-saving optimization in historic buildings. Full article

Background/Objectives: Deciding between active surveillance and treatment for prostate cancer patients often requires accurate risk assessment of prostate tumors detected with multi-parametric MRI. Conventionally, radiologists visually inspect MRI and use scoring procedures such as PI-RADS to help assess the scans. More recently, artificial intelligence (AI) applied to MRI has allowed for supplementation and is complementary to clinical assessment. However, AI is computationally expensive and severely saps scarce energy and water resources and requires special processing components, requiring alternate approaches that require less computation and fewer resources. The novel, simpler spectral/statistics approach that mimics color vision was previously successfully applied in a number of retrospective pilot studies of bi-parametric MRI of prostate cancer. The novel approach needs far fewer resources, is less computationally intensive, and is simpler than artificial intelligence to evaluate prostate tumors. However, these earlier spectral/statistics pilot studies required intervention by an analyst and too much time for implementation in future large patient studies that are needed to validate the novel approach. This retrospective pilot study further developed, applied, and tested new automation tools to expedite simpler spectral statistical techniques that need fewer resources to evaluate prostate tumors on multi-parametric MRI. Methods: Automated spatial registration, automated prostate organ segmentation, automated blob generation and selection for spectral signatures derived from the apparent diffusion coefficient, high-B-value DWI, and T2 MRI were performed on 76 consecutive patients in the PI-CAI cohort in this retrospective pilot study. The signal-to-clutter ratio (SCR) was computed using target signatures and the processed statistical metrics of the registered prostate bi-parametric MRI. The processed SCR, spectral/spatial features of blobs and clinical metrics predict clinically significant prostate cancer using multivariate logistic regression. The proposed method was assessed using the area under the curve (AUC) from the receiver operating characteristic curve. Results: AUC values of >0.90 were achieved by combining the SCR with blob and clinical metrics. Increasing the number of non-congruent, independent variables resulted in higher AUC scores. Restricting analysis to blob volumes > 0.1 cm³ achieved higher AUC values. The additional total savings in time by applying the new automation tools reduced the processing time by 80 to 170 min for 10 patients. Implementing the new automation tools resulted in an overall processing time of 40 to 80 min per 10 patients. Conclusions: Automating the spectral/statistics approach resulted in AUCs not inferior to those obtained from AI. The automation achieved sufficiently high AUCs and also reduced processing times, warranting future assessments in large patient cohorts. Full article

The current paper concerns the interplay of the circular economy (CE) and global energy threats facing the hospitality industry. Despite the growing emphasis on CE implementation within sustainable hospitality, little empirical evidence explains how CE practices enhance hotels’ green energy resilience amid escalating global energy threats and resource uncertainty, particularly in emerging markets. Addressing this research gap, this paper investigates how the 6R CE model (Redesign, Reduce, Reuse, Recycle, Recover, and Rethink) strengthens green energy resilience in sustainable hotels while examining the moderating roles of policy clarity and regulations together with the influences of stakeholder collaboration and management commitment within the Saudi Arabian hospitality sector. The study adopted a four-wave temporally separated quantitative survey design, collecting data from 360 managers working in Saudi sustainable hotels across four time intervals. The proposed conceptual model was evaluated using partial least squares structural equation modeling (PLS-SEM), complemented by mediation and moderation analyses. The findings demonstrate that global energy threats strongly stimulate CE adoption (β = 0.848, p < 0.001), while CE practices substantially improve green energy resilience (β = 0.719, p < 0.001). The results further reveal a strong indirect effect through the Strength of CE Implementation (β = 0.813, p < 0.001), highlighting implementation maturity as a critical mechanism through which circular initiatives translate into resilience outcomes. The proposed model explained 55% of the variance in CE implementation and demonstrated substantial predictive capability for energy resilience. Policy clarity and regulations significantly strengthen the CE–resilience relationship, while stakeholder collaboration and management commitment further enhance these relationships. The results further identify the 6R circularity framework as an effective strategic pathway through which sustainable hotels can mitigate global energy threats while enhancing organizational resilience. This study contributes to the CE and sustainable hospitality literature by providing one of the first empirical models linking CE implementation with green energy resilience under conditions of global energy uncertainty, offering actionable implications for policymakers, hotel managers, and the implementation of Saudi Vision 2030. Full article

Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle in two ways that matter in industry: it is tuned isothermally, so as the windings heat, the rotor-resistance drift detunes the field orientation and corrupts torque; and it treats the loss-optimal flux as a quasi-static set-point, so an abrupt load rise from a light-load, low-flux condition forces a slow flux rebuild that throttles torque. This paper proposes a thermally adaptive economic model predictive controller (TA-EMPC) that retains the energy optimum of LMC while removing both weaknesses. A temperature-coupled total-loss model (machine copper and core loss plus inverter conduction and switching loss) is minimized over a finite horizon subject to a torque-delivery constraint; a reduced-order two-node thermal observer updates the loss-defining resistances online without a temperature sensor; and a load-demand-aware flux-reservation term pre-magnetizes the machine ahead of anticipated torque rises. In simulations on a representative 7.5 kW drive, TA-EMPC matched the energy of static LMC to within 0.3% across pump, conveyor, and fast-cycling duty profiles—both saving 1.4–2.3% of cycle energy relative to rated-flux FOC, and up to about 14.7 efficiency points at very light load—while, unlike LMC, holding the steady torque error below 0.5% when the winding temperature rose by about 95 °C, to a hot steady state near 115 °C (a stator-resistance increase of roughly 37%) (against an 8% error for the non-adaptive scheme) and reducing the torque undershoot during a light-to-heavy load step from about 23% to near zero. All quantitative results reported in this work are obtained entirely in simulation. A per-step operation-count analysis—not an on-target timing measurement—indicates that the condensed quadratic-program formulation with move blocking is executable within the 100 µs sampling interval on a production digital signal controller for the chosen control horizon; experimental validation on a loaded dynamometer bench, together with on-target timing measurement, is identified as future work. The contribution is thus energy-efficient operation delivered with the torque robustness that loss minimization alone does not provide. Full article

The design of ultra-low-temperature refrigeration systems has gained increasing importance in recent years, driven by the growing demand for very-low-temperature storage cabinets for many applications, such as the preservation of vaccines and biological materials. In the first part of this study, the thermal design of both two-stage and three-stage cascade refrigeration systems was developed using the Engineering Equation Solver (EES). The systems were evaluated under evaporator temperatures of −85 °C, −80 °C, and −75 °C and ambient temperatures ranging from −5 °C to 40 °C. For the two-stage configuration, the R170/R161 refrigerant pair was assessed as an alternative to the conventional R170/R290 combination. In the three-stage configuration, the performance of the R1150/R170/R290 and R1150/R170/R161 refrigerant combinations was analyzed. The results indicate that under identical operating conditions, the three-stage system demonstrated lower compressor power consumption and reduced exergy destruction compared to the two-stage configuration, while achieving higher coefficients of performance (COPs) and exergy efficiency. In the second part of the study, long-term dynamic simulations of energy consumption for both two-stage and three-stage systems were carried out using TRNSYS 18 under varying ambient temperature conditions. The simulations were performed for hospital installation rooms located in three different cities: Muğla (Turkey), Milan (Italy), and Warsaw (Poland). The results of the dynamic simulations indicate that the use of R161 leads to significant energy savings compared to R290. Specifically, when comparing the three-stage cascade system using R1150/R170/R161 with the conventional two-stage R170/R290 system, energy consumption reductions of 20.5% in Warsaw, 23.6% in Milan, and 26.6% in Muğla were achieved. Full article

Many airports now consider implementing urban-cooling measures as proven strategies to reduce energy use and improve environmental conditions. Studies have also shown that reflective surfaces on roofs and grounds can have beneficial effects on thermal environment and airport workers’ productivity. To date, no studies have been undertaken to specifically assess whether any effects on aircraft performance would also result from implementing these measures. In this exploratory study, high-resolution micrometeorological modeling and remote-sensing analysis were undertaken to quantify the potential reductions in fuel use and, thus, emissions from aircraft taxiing on cooler surfaces. The results suggest small but non-zero benefits in terms of emissions and takeoff-roll distances. Using the Dallas–Ft. Worth International Airport (DFW) as a case study and Boeing 737 aircraft type as an example, it is found that if taxiways and shoulders albedo is increased to 0.35, an average of 2.3 kg CO₂ can be saved per single taxi-out or taxi-in operation around midday and about 1.5 kg CO₂ earlier in the morning or later in the evening. It is also found that even though runways albedo remains unchanged, cooler air advected over runways from modified taxiways can reduce the takeoff-roll distance by an average of 3% around midday that tapers off to an average of 1% early in the morning or late evening. Indeed, these are small effects per single taxi-in or taxi-out operation but when scaled by some 2000 arrivals and departures per day at DFW, saving 4000–5000 kg CO₂ per day, and if further scaled by the number of eligible airports, the impacts become significant. Furthermore, the effects reported here are from taxiway and shoulder modifications alone; if combined with the effects from cool roofs and other cool ground surfaces at terminals, tarmacs, ramps, and parking areas, the benefits will add up significantly. Limitations in this study, that would be addressed in future work, include simplifying assumptions regarding aircraft-engine performance, specifications, and aircraft types mix and operations. Full article

Heat pumps are core equipment for efficient low-grade thermal energy utilization and low-carbon transformation of the energy structure, offering significant energy-saving potential in building heating and industrial waste heat recovery. This paper reviews the research progress and technical challenges of compression, absorption, and adsorption heat pumps as well as nanofluid-enhanced heat transfer technology and elastocaloric heat pump systems. Air source heat pumps can delay frosting through variable frequency, heat storage, and waste heat recovery. However, accurate prediction models for performance degradation under extreme cold conditions are lacking. Although ground source and water source heat pumps exhibit significant energy efficiency advantages, ground source systems may suffer from performance degradation due to underground thermal imbalance. The application of water source systems is strictly constrained by water resource conditions. Driven by low-grade waste heat, absorption heat pumps employing traditional working pairs suffer from crystallization, corrosion, or high rectification energy consumption. The COP of a single-effect cycle under 80~100 °C waste heat is only 1.2~1.9, while hybrid cycles can reach approximately 3.2 at 120~150 °C. Although adsorption heat pumps achieve significantly improved performance under continuous heat recovery cycles, the full-scale power density of novel adsorbents such as metal–organic frameworks is inferior to the power density of traditional silica gel. Moreover, under off-design conditions, the performance drops by 23~48% compared to theoretical values. Nanofluids can enhance heat transfer, but the long-term effects of particle agglomeration at high temperatures on pump power consumption and system compatibility remain to be systematically evaluated. Elastocaloric heat pump systems can achieve refrigerant-free cooling, but current prototypes still cannot compete with traditional vapor compression systems in long-cycle fatigue reliability and power density. Current heat pump technologies generally face challenges such as insufficient adaptability to extreme conditions, bottlenecks in working fluids and materials, and a lack of long-term validation. Future research must construct a multi-source coupling optimization system, address common problems in working fluids and materials, promote long-term validation and kilowatt-level prototype demonstrations, and drive the large-scale deployment and engineering application of heat pump technology toward high efficiency, intelligence, and high reliability. Full article

Simulation-dependent energy analysis has long dominated building retrofit research, yet this paradigm presents substantial barriers for non-expert building owners who lack technical software proficiency and detailed building documentation-a challenge compounded by the “curse of dimensionality” when multivariate analysis requires thousands of samples beyond available empirical records. Leveraging retrofit data accumulated through Korea’s Green Remodeling programs since 2017, this study proposes a Copula-Genetic Algorithm (Copula-GA) integrated framework that enables rational retrofit decision-making with minimal user inputs (construction year, floor area, structural type). From 178 documented retrofit cases, Gaussian copula-based multivariate sampling generated 10,000 synthetic records while preserving inter-variable dependency structures. Building physics constraints addressing vintage-thermal performance and capacity-efficiency relationships filtered implausible combinations, yielding 9898 valid cases with correlation matrix fidelity confirmed by a Frobenius norm deviation of 0.043. Evolutionary clustering employing a composite fitness function of Silhouette coefficient (0.68) and Davies-Bouldin Index (0.52) identified K = 16 as the optimal partition, categorizing outcomes into four reference model archetypes: Lightweight Structure (Type A, 27.0% reduction, 15.7-year payback), Masonry Structure (Type B, 29.0%, 14.8 years), RC Structure (Type C, 30.7%, 13.4 years), and Mixed Structure (Type D, 30.9%, 13.1 years). The proposed Copula-GA framework bridges the gap between advanced energy optimization methodologies and practical accessibility for non-expert building owners. By transforming limited empirical samples into reliable reference models, this research supports building-sector decarbonization. Using three minimal inputs, a building can be matched to one of the 16 standard models to obtain its expected saving rate, payback period, and recommended measures without detailed simulation. Full article

To promote the sustainable development of the textile industry and address the critical challenges of high water consumption and salt pollution in cotton dyeing, this study developed a short-process cationic dyeing method using 2,3-epoxypropyltrimethylammonium chloride (GTA). Unlike traditional cationic dyeing, this approach eliminates the intermediate drying step by adding reactive dyes directly into the same bath after modification, achieving salt-free dyeing and significant water savings. The effects of key parameters, including GTA concentration, NaOH concentration, modification temperature and time, dyeing bath ratio, holding time, and dyeing temperature, were systematically investigated. The optimal dyeing conditions for C.I. Reactive Red 195, Reactive Black 5, and Reactive Blue 19 were determined as follows: GTA concentration of 40 g/L, NaOH concentration of 1 g/L, modification at 90 °C for 50 min, dyeing at 60 °C with a bath ratio of 1:15, and holding times of 30, 20, and 40 min, respectively. After modification, thermal stability and crystallinity decreased slightly (from 60.51% to 58.18%), while hydrophilicity showed no significant change. This short-process cationic dyeing method saves time, energy, and water while maintaining dyeing performance comparable to conventional methods. It provides a practical and sustainable new approach for salt-free cotton dyeing. Full article

A major challenge in the optimization of thermal performance of opaque envelope components is that the calculation workload of energy consumption simulations of full factorial combinations leads to a prohibitive increase as the factors and levels multiply. Nevertheless, a reliable method for designing calculation schemes to reduce the calculation workload without sacrificing the accuracy of the results is still lacking. Accordingly, a building energy consumption simulation scheme (BECSS) based on a uniform design method (UDM) was proposed, and its feasibility was verified. A multiobjective optimization model (MOM) with the life cycle cost (LCC) and life cycle carbon emission (LCCE) as optimization objectives was established, and it was solved using the non-dominated sorting genetic algorithm-II (NSGA-II). Furthermore, an entropy-based TOPSIS method was introduced to calculate the optimal insulation thickness (OIT) of opaque building envelopes in severe cold and cold zones. The proposed framework was demonstrated through a case study of a typical residential building in Xuzhou. Extruded polystyrene panels (XPS) were used as insulation materials, and coal-fired boiler (CFB), gas-fired boiler (GFB), and air source heat pump (ASHP) were used as heat sources. Compared with the results specified by the current energy conservation standards, the MOM could achieve a balance between energy savings and environmental benefits. The LCCs change by −3.16–11.34%; nevertheless, the thermal performance of the external wall improves by 42.32–49.31%, while that of the roof improves by 6.46–21.59%; the building energy consumption (BEC) levels and the LCCEs are reduced by 25.24–30.16% and 19.25–24.74%, respectively. The indicator weights determined via the entropy weight method underscore the necessity of incorporating environmental performance indicators in the optimization of thermal performance of opaque building envelope components. Full article

Leaf disease diagnosis needs models that are accurate enough for agronomic use yet small enough for constrained computing settings. This study examines a late-attention MobileNetV2 design in which one Convolutional Block Attention Module (CBAM) is inserted between the last MobileNetV2 convolutional map and global average pooling. The experiments use 54,306 controlled-background PlantVillage images spanning 38 classes. Under a uniform saved-model re-evaluation, MobileNetV2 + CBAM obtained 97.17% accuracy and 97.15% weighted F1-score, whereas MobileNetV2 obtained 96.78% and 96.73%. On the converted models, paired testing gave a 0.64-percentage-point accuracy advantage for the CBAM variant (95% CI: 0.31–0.96; exact McNemar $p<0.001$). The proposed network has 4.02 million parameters, costs 0.604 GFLOPs (about 0.302 GMACs), and yields a 4.07 MiB dynamic-range-quantized TensorFlow Lite file with 96.70% accuracy. Batch-one inference on an Intel i7-11800H CPU with TensorFlow Lite/XNNPACK and eight threads reached a median of 45.42 ms (P95: 102.54 ms), excluding preprocessing. Grad-CAM inspection illustrates both lesion-centered activation and unresolved shared errors. The evidence therefore supports a compact accuracy–cost compromise for the tested conditions, while field robustness, energy use, repeated training runs, and target-device behavior remain open validation requirements. Full article

To achieve the European Union’s zero-emission building targets and overcome the limitations of slow, costly on-site construction, off-site prefabrication is vital. However, existing research often neglects real-world constructability and circularity constraints. This study presents a systematic technology supply mapping and a performance-oriented taxonomy—single-function envelopes, multi-functional integrated systems, and stand-alone installations—of prefabricated renovation solutions, focusing predominantly on Southern European contexts. Applying a structured three-step screening protocol across the literature, industry databases, and European project repositories, technologies were evaluated through a multi-dimensional framework capturing market readiness, functional integration, structural constraints, and circularity indicators. Results reveal a strongly polarized market where passive envelope systems dominate commercial availability, while multi-functional active solutions remain largely confined to experimental prototype stages. Furthermore, most systems target low-to-mid-rise residential buildings, show limited compatibility with complex architectural geometries, and exhibit low integration of circular criteria due to conventional material reliance. This study concludes that bridging the research-to-market gap requires interface standardization, demand aggregation through Green Public Procurement, and localized regulatory adaptations for envelope thickness. Ultimately, this taxonomy establishes a rigorous diagnostic framework and a solid foundation for a future quantitative Prefabrication Readiness Index (PRI). Full article