Search Results (15,523)

This work reports the development process of a 12 m continuous hybrid microwave–gas tunnel kiln for white-glazed porcelain firing. The kiln combines 40 conventional 2.45 GHz magnetrons with 10 natural-gas burners and was developed through the GreenWave and CerWave projects. The experimental program was production-oriented rather than statistically designed. Firing cycles from 3.5 to 8 h were evaluated, with 7 h identified as the principal validated condition for white-glazed porcelain based on industrial visual-acceptance criteria. The maximum individual saving was 12.4%, while the average saving was 5.5%. Shorter cycles showed process-development potential but produced colour deviations and, for the shortest times, visible surface defects. Hybrid firing reduced specific energy consumption relative to equivalent gas-only firing, with a maximum individual saving of 14.7% for 5 h firing cycles, yet a fraction exhibited yellowish colour. Energy-cost sensitivity analysis indicates that economic attractiveness depends strongly on the electricity-to-natural-gas price ratio; using the 7 h average energy data, the break-even ratio is approximately 1.3 on an equivalent-energy basis. The study also identifies engineering challenges involving refractory shrinkage, wagon sealing/interlocking, microwave leakage, thermal management and scale-up. Direct temperature measurement of porcelain bodies and a complete techno-economic or environmental assessment were outside the scope of the study. Full article

In water safety planning, drinking water quality is protected by multiple barriers, beginning with source protection before treatment and distribution. Protection requires timely information about conditions beyond directly monitored locations. This study presents a digital twin prototype integrating networked environmental observations, a three-dimensional hydrodynamic water quality model, and source-protection applications at the Brusdalsvatnet reservoir in Norway. The framework connects state estimation for operational monitoring with contamination-scenario analysis and model-informed planning of uncrewed surface vessel sampling. Compared against observations from a single moored station throughout the 2024 season, the modelled water temperature has a root-mean-square error of 1.73 ${}^{\circ }$C near the surface and 2.15 ${}^{\circ }$C averaged across depth, with the largest discrepancies at intermediate depths in summer. The contaminant scenario and sampling route demonstrate analytical capabilities without field validation. The results establish a proof of concept for source-water decision support and identify remaining challenges to making its predictions reliable. Full article

The combined effects of global warming and increasing rainfall threaten the stability of railway embankments in permafrost regions of the Qinghai–Tibet Plateau. An indoor embankment model at a geometric scale of 1:17 was constructed based on a typical section of the Qinghai–Tibet Railway on the Chumar River High Plain. Experiments were conducted in a temperature-controlled environmental chamber using four rainfall classes with different durations, prescribed according to observed seasonal precipitation characteristics. Temperature and moisture variations were monitored at different depths beneath the embankment center, shoulder, and slope toe under rainfall and rain-free conditions. The study quantifies three regulatory effects of rainfall on the hydrothermal field: (1) The “seasonal differentiation effect” on the temperature field—rainfall generally reduces shallow embankment temperature (from April to October, average cooling of 0.8 °C, 0.6 °C, and 0.4 °C at 10 cm depth below the pavement, shoulder, and slope toe, respectively). Maximum cooling occurs in summer (1.8 °C beneath the embankment center in July); in autumn, cooling is observed beneath the embankment center and shoulder; in spring, shallow ground temperatures increased with seasonal warming, while rainfall generally reduced their values relative to the corresponding rain-free condition. (2) The “spatial heterogeneity effect” on the moisture field—rainfall significantly increases shallow moisture content (average increases of 3.1%, 4.3%, and 4.9% at the same locations), with the slope toe showing the largest increase due to surface runoff, while beneath the pavement the “pot-cover effect” intensifies vapor condensation. (3) Rainfall-associated temperature and moisture differences generally decreased with depth. Under the prescribed experimental conditions, moisture differences were relatively small at monitored model depths of 50 cm and greater. The results reveal the hydrothermal response mechanism of permafrost embankments under coupled rainfall and freeze–thaw cycles, providing refined experimental evidence for disease prevention and long-term stability evaluation. Full article

This paper presents a multi-frequency electro-thermal Digital Model framework for accurate mapping and prediction of near-field electromagnetic (EM) power dissipation and localized temperature rise in high-frequency planar microwave structures. The proposed methodology integrates 3D full-wave computational simulations in CST Studio Suite with non-invasive transient infrared (IR) thermography using an Indium Tin Oxide (ITO) coated Polyethylene Terephthalate (PET) thin-film transducer. A high-gain 4 × 4 microstrip patch antenna array operating nominally at 12.00 GHz serves as the experimental benchmark. Numerical and physical evaluations were conducted across five discrete operating frequencies (7.10, 8.05, 9.05, 11.14, and 12.00) GHz at near-field evaluation distances of 1 mm, 16 mm, and 29 mm under a continuous-wave microwave (MW) excitation over a 50 s exposure duration. Transient surface temperature dynamics were recorded using a calibrated Teledyne FLIR A700 LWIR radiometric sensor. Quantitative line profile validation yields an average maximum temperature rise error of 20.75%, an average spatial center shift of 2.70 mm, and an average spatial spread (FWHM) deviation of 21.29%. These moderate discrepancies are physically attributed to evaluating free-space electric field intensity (|E|²) within the numerical domain versus measuring resistive heat generation on the physical ITO film. The developed Digital Model framework provides a robust foundation for real-time thermal monitoring, localized hotspot suppression, and predictive thermal management in advanced phased arrays and high-power MW systems. Full article

Comminution is the most energy-intensive stage of mineral processing, and microwave-assisted comminution (MAC) can reduce grinding energy by selectively heating microwave-absorbing minerals within transparent gangue, generating thermal microcracks that improve liberation. MAC performance, however, depends on the ore mineralogy and surface, which fixed-parameter operation cannot accommodate. An integrated mechatronic “intelligent crusher” is presented unifying actuation (microwave source, feed system, adjustable crusher geometry), sensing (thermal infrared and hyperspectral imaging, HSI), and control (offline reinforcement learning). HSI-derived mineralogical features and infrared thermal features form the state of a behavior-regularized actor–critic (BRAC) controller trained offline on logged operating data to adjust the power, exposure, feed rate, and crusher setting. A two-dimensional coupled electromagnetic–thermal–mechanical finite-element study underpins the process model. It is executed with temperature-independent dielectric properties in a single staggered coupling pass, and so calibrates the damage law qualitatively rather than predicting stress quantitatively. It reproduces cracking thresholds from the literature and shows that thermal gradients decay with exposure time as (1 + t/$\tau$)^−0.57, so that damage at a constant dose falls from 0.63 to 0.02 as exposure lengthens from 0.25 to 16 s. On this basis, the phenomenological damage law, which had been exposure-insensitive, is corrected. On the FEA-calibrated simulator, the BRAC policy reduces the mean size targeting error by 62% (1.43 to 0.54 mm) and the total specific energy by 4.2% (6.50 to 6.22 kWh/t), averaged over five training seeds, relative to fixed-parameter operation, outperforms rule-based and behavior-cloning baselines, and generalizes to a simulated ore batch excluded from the training. The framework establishes a validated control architecture for adaptive MAC ahead of three-dimensional model extension and experimental deployment. Full article

Greywater can be a valuable source of energy, which is often wasted when discharged into the sewer system. The use of drain water heat recovery (DWHR) exchangers in buildings allows for the recovery of this energy. With this in mind, a new horizontal shower heat exchanger solution was developed, and a prototype was built. This device was tested in the laboratory under conditions similar to those of normal shower use. The obtained results allowed the determination of the temperature-based effectiveness for a wide range of variables. The experimental results also formed the basis for a Life Cycle Cost analysis of various domestic hot water system variants and for determining the environmental impact of implementing the tested exchanger. Depending on the system configuration, the DWHR temperature-based effectiveness ranged from 24% to 29% (Configuration 1), from 29% to 40% (Configuration 2), and from 38% to 57% in Configuration 3. The tests also confirmed that the new exchanger solution is financially viable. For two-person households with average water consumption per bath, the payback period does not exceed 7 years, and for a four-person family, it is reduced to approximately 3 years. A key aspect of the research was the demonstration of a significant reduction in operational CO₂ emissions resulting from the implementation of this DWHR exchanger in the building. This study highlights the importance of selecting the optimal configuration for connecting the exchanger to the sanitary system and confirms the need for continuous development and research into wastewater heat recovery technologies. Full article

Polymer concretes with fiber reinforcement have many desirable properties when compared to cementitious concretes, including rapid development of mechanical properties, excellent bond to concrete and other substrates, high tensile strength, and resistance to abrasion and aggressive chemical environments. However, their use as a structural material has been limited, in part, by temperature-dependent mechanical properties, affecting bond and development of steel reinforcement, deformation of structural elements, and section capacity of structural members. This research investigated the development of mechanical properties for a commercially available polymer concrete with fiber reinforcement to determine the effects of temperature on compressive strength, elastic modulus, modulus of rupture, and pull-out bond strength. The compressive, flexural, and bond strengths of the tested polymer concrete with fiber reinforcement were over 70% of their 7 d values within 4 h after mixing when cured at laboratory temperature, demonstrating the rapid development of mechanical properties that is possible with polymer binders. The average 7 d compressive strength across the experimental program was 62.2 MPa at 25 °C, with the measured elastic modulus and modulus of rupture roughly half and three times that of estimated values using established code relationships and the measured compressive strength, respectively. The pull-out bond strength at 25 °C was found to be similar to non-proprietary ultra-high performance and polymethyl methacrylate concretes, and the variation in mechanical properties with temperature was roughly linear and independent of the mechanical property tested when normalized by the value at laboratory temperature. This limited test series supports the structural use of polymer concrete with fiber reinforcement, when in-service temperature is expressly considered in the design process, although further testing is needed to develop rational design procedures. Full article

Thermally activated four-dimensional printing allows additively manufactured structures to change shape with temperature, providing potential for adaptive polymeric systems. In soft thermoplastic polyurethane (TPU) composites, this is hindered by inadequate thermal transitions, reliance in conductive fillers, solvent-based processing, and the poor printability of very soft TPUs. Here, a thermally reversible switching transition, Joule-heating capability, and pellet-extrusion printability were incorporated into a soft TPU through scalable, solvent-free melt compounding with a commercial masterbatch (MB) of single-walled carbon nanotubes (SWCNTs) in a polyol-ester blend. MB dilution produced composites containing approximately [range-units=single]13$\mathrm{wt}.\%$ SWCNTs and [range-units=single]927$\mathrm{wt}.\%$ polyol-ester blend. All composites were thermally stable with a polyol-ester-related melting transition near 54${}^{\circ }\mathrm{C}$ identified by differential scanning calorimetry.ncreasing the MB content enhanced the complex viscosity and viscoelastic moduli while preserving shear-thinning behavior. Conductive atomic force microscopy showed heterogeneous surface-connected current pathways. And mean bulk electrical conductivity increased from 1.09$\mathrm{S}$/$\mathrm{m}$ to 18.15$\mathrm{S}$/$\mathrm{m}$ as MB content increased from the lowest to the highest loading. The most conductive formulation exhibited voltage-dependent Joule heating, reaching an initial heating rate of 61.8${}^{\circ }\mathrm{C}$/$\min$ at 22$\mathrm{V}$. The cycle-averaged shape-fixity ratios for vacuum-molded samples were 87.290.7 over three cycles, whereas cycle-averaged shape-recovery ratios were 82.685.2 over the two reported recovery cycles. A deformable lattice was produced by fused granulate fabrication (FGF) and exhibited temporary-shape retention and recovery toward its original geometry during external reheating. Collectively, the results demonstrate a melt-processable route to conductive TPU composites, combining electrothermal heating capability with thermally activated shape-memory functionality for future 4D-printing applications. Full article

Molecular dynamics simulations were employed to investigate the effects of cutting speed and cutting depth on the nanocutting behavior of carbon nanotube (CNT)-reinforced Ni composites. The results show that increasing cutting speed from 50 to 200 m/s reduces the average tangential cutting force by approximately 16.5%, while the cutting temperature increases by 26.7% at a cutting distance of 200 Å. Cutting depth produces a stronger influence on subsurface deformation, with deeper cutting accompanied by increased stress localization, structural disorder, and dislocation activity. In contrast, dislocation evolution exhibits a non-monotonic dependence on cutting speed, indicating that thermal and mechanical effects act concurrently under different cutting conditions. Pronounced changes in local deformation and dislocation behavior are also observed near the CNT–matrix region. These results characterize the coupled thermomechanical and defect responses of the selected CNT–Ni system under different nanocutting conditions. Full article

Background: An ambient-temperature eligibility criterion of ≥22 °C was treated as an operational lower boundary spanning moderate or mildly warm to hot conditions, not as a universal physiological threshold for heat stress. This systematic review and three-level meta-analysis evaluated acute carbohydrate–electrolyte (CHO-E) supplementation versus a water or non-caloric placebo during exercise at these temperatures. Methods: Following PROSPERO registration (CRD420261410953), five databases were searched to May 2026 for randomized controlled trials. Nested effect sizes were synthesized using three-level random-effects models (Hedges’ g), and certainty was evaluated with RoB 2 and GRADE. Results: A secondary omnibus synthesis included 32 effect sizes from 21 studies (N = 316) and yielded a small positive average estimate (g = 0.37, 95% CI: 0.21 to 0.53; p < 0.001; I² = 46.7%), although the 95% prediction interval crossed the null (−0.21 to 0.96). Primary outcome-domain analysis identified a positive average estimate for aerobic endurance (g = 0.45, 95% CI: 0.26 to 0.64), whereas evidence for anaerobic endurance, explosive power, and strength remained inconclusive. Post hoc threshold sensitivity analyses retained positive mean estimates after excluding trials at 22–25 °C (>25 °C: g = 0.41, 95% CI: 0.23 to 0.59) and at ≥28 °C (g = 0.36, 95% CI: 0.11 to 0.61), but both prediction intervals crossed zero. Neither ambient temperature nor exposure duration was a significant linear moderator. Conclusions: Acute CHO-E supplementation was associated with a small average performance difference, concentrated in aerobic endurance, within the specified ambient-temperature range. Because the trials compared treatments within the same environmental condition rather than hot with thermoneutral exercise, these results do not establish that CHO-E offsets heat-induced performance decrements. Full article

Forest fires significantly disturb the global ecosystem, profoundly impacting carbon cycling, biodiversity, and socio-economic sustainability. Understanding the seasonal patterns of forest fire occurrence is crucial for developing effective prevention and control strategies that support sustainable forest management and disaster risk reduction. However, research on the seasonal dynamics of forest fires in the tropical forests of China’s Hainan Island remains scarce, and the underlying driving mechanisms, particularly in relation to the distinct dry and rainy seasons, are still insufficiently understood. In this study, we analyzed the seasonal patterns and driving factors of forest fires on Hainan Island, a tropical region, from 2000 to 2022, and compared the applicability of emerging machine learning algorithms and traditional Logistic Regression algorithms in constructing tropical forest fire prediction models for the dry and rainy seasons. The results show that tropical forest fires in Hainan Island are primarily concentrated from January to May, peaking in April, with a lagged effect relative to the dry-season months (November to April of the following year). Average temperature and average rainfall were identified as common important predictors of fire occurrence probability in both seasons, with the Normalized Difference Vegetation Index additionally included in only the dry season. The predictive accuracy of the Random Forest model consistently surpassed that of the traditional Logistic Regression model in both seasons. Additionally, our models enable the generation of accurate forest fire probability maps, highlighting risk areas in Hainan Island during both seasons and providing new insights for wildfire prevention strategies and sustainable forest management in tropical regions. Full article

Light-emitting diode (LED) sources for high-end display and professional lighting must maintain color-point accuracy and color-rendering stability over a wide correlated color temperature (CCT) range. Three-color mixing places the color point on the Planckian locus, but non-synchronous thermal drift of the channels induces CCT shift, ${\mathrm{D}}_{\mathrm{u}\mathrm{v}}$ instability, and color-rendering degradation during long-term operation. This paper proposes a green–azure–warm-white (GAW) mixing and temperature-compensation method based on the hybrid genetic–particle swarm optimization (HGPSO) algorithm. For each channel, three-Gaussian chromaticity–temperature and spectral-power-distribution–temperature models are established, and the Duv tolerance is converted into per-channel duty-cycle bounds that shrink the HGPSO search space, enabling lookup-table-based real-time compensation on an embedded MCU. Within the 30–90 °C solder-pad range, compensation reduced the average CCT deviation at 65 °C and 85 °C from 115 K and 199 K to 14.3 K and 15.8 K and the average |${\mathrm{D}}_{\mathrm{u}\mathrm{v}}$| from 0.00167 and 0.0023 to within 0.001. The average ${\mathrm{R}}_{\mathrm{f}}$ deviation fell from 0.58% and 1.28% to 0.44% and 0.51% and the average ${\mathrm{R}}_{\mathrm{g}}$ deviation from 1.83% and 2.69% to 0.27% and 0.32%. The luminous-efficacy deviation remains within ±5% below 4500 K (maximum −8.05% at 6500 K). Cross-vendor validation confirms the portability of the method for long-term color consistency in display and lighting systems. Full article

The presence of iron(II) impurities hinders the leaching and purification of magnesium ores. The most viable technological solution is to oxidise Fe²⁺ to Fe³⁺, which then precipitates as goethite. However, this process requires the development of new catalysts for use in highly acidic media. This study demonstrates that the Pt^0.25/CeO₂⁵/TiO₂ catalyst is highly effective at oxidising Fe²⁺ in 0.3 M HNO₃, outperforming samples supported on pure titanium dioxide or cerium dioxide. The rate constant is 0.304 L/(mol·min) at room temperature. Notably, the activation energy is only 10.7 kJ/mol, which is over five times lower than that of the non-catalytic process. This result was achieved by selectively depositing platinum particles onto highly dispersed cerium dioxide particles (average size ~5 nm), synthesised using the sol–gel method, and then depositing them onto titanium dioxide. Using a hybrid mixed oxide support ensures a high density of oxygen vacancies at the interface between the platinum (Pt) and mixed cerium and titanium dioxide phases. The formation of abundant adsorbed oxygen species enhances the catalytic activity of the Pt^0.25/CeO₂⁵/TiO₂ catalyst. The catalyst Pt^0.25/CeO₂⁵/TiO₂ also reaches a plateau in terms of efficiency after a durability experiment, which is the highest among the other samples. Full article

Federated learning enables collaborative deep neural network training across distributed clients without sharing raw data, yet its performance degrades substantially when local data distributions are non-identically distributed (non-IID). Existing aggregation strategies either treat all clients uniformly or require expensive bi-level optimisation, failing to explicitly leverage the structural similarity among client data distributions. We propose DAPA (Distribution-Aware Personalised Aggregation), a personalised federated deep learning framework that adaptively tailors the global aggregation to each client based on inter-client distribution similarity. DAPA operates in three stages. In the sketch stage, each client computes a lightweight distribution sketch that summarises its local label and feature statistics through class-conditional moment vectors extracted from the deep network’s penultimate layer. In the affinity stage, the server constructs a pairwise client affinity matrix from these sketches using an approximate Wasserstein distance and derives personalised aggregation weight vectors via a softmax-temperature mechanism. In the clustering stage, a hierarchical two-level aggregation combines models within automatically discovered client clusters and then blends across clusters with adaptive mixing coefficients. To safeguard privacy, the distribution sketches are protected with a calibrated Gaussian mechanism that satisfies Rényi differential privacy. Theoretical analysis establishes a convergence bound showing that DAPA achieves a tighter error floor than uniform averaging under distribution heterogeneity. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet under Dirichlet-controlled non-IID partitions demonstrate that DAPA outperforms nine state-of-the-art baselines, improving average test accuracy by 2.4 to 6.8 percentage points while maintaining competitive communication efficiency. Ablation studies confirm the contribution of each component, and privacy analysis verifies that the accuracy gain persists under strict differential privacy budgets. These findings advance the application of deep learning in privacy-sensitive distributed environments and offer a principled data-mining-based approach to characterising client heterogeneity in federated systems. Full article

Ambient temperature fluctuations of system environments can significantly degrade the stability of fiber-based phase-stabilized RF transfer systems. In this work, the underlying mechanism of such degradation is systematically investigated. It is shown that, in conventional single-ended architectures, temperature-dependent phase variations originating from the laser source and local RF components are directly accumulated in the phase-locked loop, resulting in pronounced instability. To address this issue, a symmetric dual-carrier architecture is proposed by introducing independent optical carriers at both the central and remote sites. This configuration transforms the system into a differential reference scheme, where temperature-induced phase perturbations exhibit common-mode characteristics and can be effectively suppressed. Meanwhile, the reduced reliance on erbium-doped fiber amplifier (EDFA) in the return path mitigates amplified spontaneous emission noise and temperature-sensitive gain fluctuations. Experimental results demonstrate that, under ambient temperature variations of approximately 7 °C, the link delay fluctuation is reduced from over 15 ps to about 1.2 ps. The corresponding frequency stability reaches a modified Allan deviation of 6.70 × 10⁻¹⁹ at an averaging time of 10,000 s without strict temperature control. These results validate the effectiveness of the proposed approach for robust high-stability frequency transfer. Full article