Search Results (72)

The increasing power density of 2.5D and 3D chiplets imposes severe thermal constraints that have a direct impact on the performance and long-term reliability of high-performance computing systems. Stacked and laterally integrated dies, which generate hundreds of watts per package, create localized hotspots and inconsistent temperature fields, major obstacles to scalable heterogeneous integration. Research efforts have addressed these challenges by finite element and compact heat modeling, thermal interface material optimization (TIM), and advanced cooling solutions such as micro-channel liquid cooling and cold racks. While these approaches provide valuable insights, most remain case-specific, focusing on isolated packages or single design variables, and lack a general methodology for assessing thermal feasibility at an early stage. This review consolidates and critically analyzes contributions to thermal modeling at the package level, interposer thermal spreading, thermal characterization of TIMs, and the development of cooling technologies. A comparative review of published studies indicates a consistent threshold: 2.5D stacks are viable under air cooling at approximately 300 W, whereas 3D stacks require liquid or hybrid cooling in conjunction with high-performance thermal interface materials at about 350 W. The investigations identify interposer conductivity, thermal interface material thickness, and hotspot power distribution as the primary sensitivity elements. This study explores Thermal Feasibility Maps (TFMs), defined as multidimensional charts parameterized by architecture, cooling regime, and material stack. TFMs provide a systematic framework for comparing design trade-offs and support architecture cooling co-design in advanced chiplet systems. Full article

Performance breakthroughs and safety assurance of aerospace equipment are critical to the advancement of modern aerospace technology. As a key protective system for the hot-end components of aeroengines, thermal barrier coatings (TBCs) play a vital role in ensuring the safe operation of aeroengines and overall flight safety. To address the core detection technology challenge for micro–nano damage precursors in aerospace TBCs, this study proposes an enhanced detection framework, namely YOLOv11-LADC. Specifically, the framework integrates the LSKA attention mechanism to construct the C2PSA-LA module, thereby enhancing the detection capability for micro–nano damage precursors and adaptability to complex small-sample datasets. Additionally, it introduces deformable convolutions (DeformConv) to build the C3k2-DeformCSP module, which dynamically adapts to the irregular deformations of micro–nano damage precursors while reducing computational complexity. A data augmentation strategy incorporating 19 transformations is employed to expand the dataset to 5140 images. A series of experimental results demonstrates that, compared with the YOLOv11 baseline model, the proposed model achieves a 1.6% improvement in precision (P) and a 2.0% increase in recall (R), while maintaining mAP50 and mAP50-95 at near-constant levels. Meanwhile, the computational complexity (GFLOPs) is reduced to 6.2, validating the superiority of the enhanced framework in terms of detection accuracy and training efficiency. This further confirms the feasibility and practicality of the YOLOv11-LADC algorithm for detecting multi-scale micro–nano damage precursors in aerospace TBCs. Overall, this study provides an effective solution for the intelligent, high-precision, and real-time detection of multi-scale micro–nano damage precursors in aerospace TBCs. Full article

Selective Laser Sintering (SLS) is an Additive Manufacturing (AM) technology that is receiving considerable attention in the scientific and industrial communities due to its great ability to efficiently produce functional and complex parts. The present work aims to fabricate a real prototype via SLS, such as a hose reel for industrial applications, using polyamide 11 (PA11) as a starting material. Characterization of the PA11 powder properties was first carried out from a thermal and morphological viewpoint to determine the powder’s thermal stability by TGA, the sintering window and degree of crystallinity by DSC, and the microstructure by SEM, PSD, and XRD analyses. The results revealed that PA11 has a 45-micron average particle size, circularity close to 1, and a Hausner ratio of 1.17. Together, these parameters ensure that PA11 powder flows smoothly, packs uniformly, and forms dense and defect-free layers during the SLS process, directly contributing to high part quality, dimensional precision, and stable process performance. The printability of the PA11 was optimized for the realization of 3D-printed parts for industrial applications. Finally, the quality of the printed samples and the mechanical and thermal performance were investigated. Several PA11-based parts were fabricated via SLS, showing a high level of complexity and definition, ideal for industrial applications, as confirmed by the predominantly green areas of the colored maps of X-CT. A complete prototypal case for a hose reel was assembled by using the parts realized, and it was chosen as a technological demonstrator to verify the feasibility of PA11 powder in the production of industrial professional components. Full article

Air cycle systems, once largely replaced by vapour-compression technologies due to efficiency concerns, are now re-emerging as a viable and sustainable alternative for highly dynamic thermal applications and excel in ultra-low temperature. By using air as the working fluid, these systems eliminate the need for synthetic refrigerants and comply naturally with evolving environmental regulations. This study presents the conceptual design and simulation-based analysis of a novel air cycle machine developed for advanced automotive testing environments. The system is intended to replicate a wide range of climatic conditions—from deep winter to peak summer—through the use of fast-responding turbomachinery and a flexible control strategy. A central focus is placed on the radial turbine, which is designed and evaluated using a modular, open source framework that integrates geometry generation, off-design CFD simulation, and performance mapping. The study outlines a potential operating strategy based on these simulations and discusses a control architecture combining lookup tables with zone-specific PID tuning. While the results are theoretical, they demonstrate the feasibility and flexibility of the proposed approach, particularly the turbine’s role within the system. Full article

This article presents the accumulated technical and scientific knowledge from energy performance upgrade work in emblematic and essential municipal and public buildings in Crete and the Greek islands, such as the Venetian historical building Loggia, which is used as the Heraklion City Hall, the Natural History Museum of Crete, Pancretan Stadium, the municipal swimming pool of the municipality of Minoa Pediadas, the indoor sports hall in Leros, primary schools, high schools and a cultural center. Each one of the aforementioned buildings has a distinct use, thus covering almost all different categories of municipal or public buildings and facilities. The applied energy performance upgrade process in general terms is: (1) Mapping of the current situation, regarding the existing infrastructure and final energy consumption. (2) Formulation and sizing of the proposed passive measures and calculation of the new indoor heating and cooling loads. (3) Selection, sizing and siting of the proposed active measures and calculation of the new expecting energy sources consumption. (4) Sizing and siting of power and heat production systems from renewable energy sources (RES). Through the work accomplished and presented in this article, practically all the most technically and economically feasible passive and active measures were studied: insulation of opaque surfaces, opening overhangs, natural ventilation, replacement of openings, daylighting solar tubes, open-loop geo-exchange plants, refrigerant or water distribution networks, air-to-water heat pumps, solar thermal collectors, lighting systems, automation systems, photovoltaics etc. The main results of the research showed energy savings through passive and active systems that can exceed 70%, depending mainly on the existing energy performance of the facility. By introducing photovoltaic plants operating under the net-metering mode, energy performance upgrades up to zero-energy facilities can be achieved. The payback periods range from 12 to 45 years. The setup budgets of the presented projects range from a few hundred thousand euros to 7 million euros. Full article

This study analyzed how external forcings, such as meteorological conditions and inflows, influence the average water surface temperature (WST) of the urban Lake Paranoá, Brasília-Brazil, using both in situ measurements and remote sensing estimates over a 40-year period. The temperature model calibrated for Lake Paranoá with no time lag (0-day delay) presented the following metrics: R² = 0.92, RMSE = 0.59 °C, demonstrating the feasibility of obtaining reliable thermal estimates from remote sensing even in urban water bodies. Simple and multiple regression analyses were applied to identify the main external drivers of WST across different temporal scales. A warming trend of 0.036 °C/yr in lake surface temperature was observed, higher than the concurrent increase in air temperature (0.026 °C/yr), suggesting enhanced thermal stratification that may impact water quality. The most influential variables on WST were air temperature, relative humidity, and wind speed, with varying degrees of influence depending on the time scale considered (daily, monthly, annual or seasonal). Remote sensing proved to be essential for overcoming the limitations of traditional monitoring, such as temporal gaps and limited spatial coverage, and allowed detailed mapping of thermal patterns throughout the lake. Integrating these data into hydrodynamic models enhances their diagnostic, predictive, and decision-support capabilities in the context of climate change. Full article

Variable renewable energy sources and cross-zonal trades stress transmission grids, pushing them toward thermal limits. This systematic review, reported in accordance with PRISMA 2020, examines how demand-side response (DSR) can provide relief at the transmission scale. We screened peer-reviewed literature and operator documentation, from 2010 to 2025, indexed in Web of Science, Scopus, and IEEE Xplore; organized remedial actions across supply, network, and demand/storage levers; and categorized operational attributes (time to effect, spatial targeting, activation lead times, telemetry, and measurement and verification). Few reviewed sources explicitly link DSR to transmission congestion relief, highlighting the gap between its mature use in frequency and adequacy services and its still-limited, location-specific application on the grid. We identify feasibility conditions, including assets downstream of the binding interface, minute-scale activation, and feeder-grade baselines with rebound accounting. This implies the following design requirements: TSO–DSO eligibility registries and conflict resolution, portfolio mapping to power-flow sensitivities, and co-optimization with redispatch, HVDC, topology control, and storage within a security-constrained optimal-power-flow framework. No full-text risk-of-bias assessment or meta-analysis was undertaken; the review used English-only title/abstract screening. Registration: none. Funding: none. Full article

In this work, we present the first report on the synthesis via the sol–gel method of a high-entropy (Fe_0.2Co_0.2Cu_0.2Ni_0.2Mn_0.2)Nb₂O₆ with columbite–orthorhombic structure. Polyvinylpyrrolidone (PVP), ammonium niobium oxalate, and equimolar amounts of Fe, Co, Cu, Ni, and Mn ions were used. The refinement of the XRD pattern showed the presence of niobate crystallites with an average size of 48.4 nm and a fraction of 7.6 wt% of a spinel-like phase. At temperatures below 5 K, the DC and AC magnetometry results revealed the presence of a ferromagnetic-like phase due to the niobate phase. The Mössbauer spectrum at 300 K showed a paramagnetic and two magnetically ordered components corresponding to the niobate and the spinel-like phases, respectively. The spectral components were typical of Fe³⁺, indicating the presence of cation vacancies. The elemental mapping obtained from EDS measurements showed compositional homogeneity. The XRF measurements confirmed a composition consistent with nominal values. These results confirm the feasibility of synthesizing entropy-stabilized columbite oxides via the sol–gel route, opening new opportunities for the design of multifunctional ceramics with tunable structural and magnetic properties for high-performance thermal barrier coatings and energy conversion applications. Full article

Bio-waste from potato shell agro-waste-based photocatalyst is introduced using potato shell integrated with Fe₃O₄ nanoparticles as a novel photocatalyst for photo-Fenton oxidation reaction. The catalyst was prepared via thermal activation of biochar, followed by co-precipitation of magnetite nanoparticles, resulting in a stable and reusable material. X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques augmented with the energy dispersive X-ray spectroscopy (EDX) analysis with elemental mapping were used to assess the prepared sample. The prepared material, PtS200–Fe₃O₄, is then applied for oxidizing Procion Blue dye using biochar-supported magnetite catalyst. The oxidation process was evaluated under varying operational parameters, including pH, temperature, catalyst loading, oxidant dosage, and dye concentration. Results revealed that the system achieved complete dye removal within 20 min at 60 °C and pH 3, demonstrating the strong catalytic activity of the composite. Furthermore, the kinetic modeling is evaluated and the data confirmed that the degradation followed first-order kinetics. Also, the thermodynamic parameters indicated low activation energy with PtS200–Fe₃O₄ composite in advanced oxidation processes. The system sustainability is also assessed, and the reusability test verified that the catalyst retained over 70% efficiency after six consecutive cycles, highlighting its durability. The study confirms the feasibility of using biochar-supported magnetite as a cost-effective, eco-friendly, and efficient catalyst for the treatment of textile effluents and other dye-contaminated wastewater. Full article

Comets are chemically rich and thermally extreme, spanning surface temperatures from ~50 K in the Oort Cloud to >1000 K for sungrazing bodies. These conditions may support key steps of prebiotic chemistry, including the synthesis of nucleic acid precursors. This study present a thermodynamic evaluation of seven candidate reactions, producing nitrogenous bases, sugars, nucleosides, and nucleotides, across the cometary temperature spectrum, 50–1000 K. Purine nucleobase synthesis, including adenine formation via aminoacetonitrile polymerization and HCN polymerization, is strongly exergonic at all temperatures. Sugar formation from formaldehyde is also exergonic, while intermediate pathways, e.g., 2-aminooxazole synthesis, become thermodynamically viable only above ~700 K. Nucleoside formation is thermodynamically neutral at low T but becomes favorable at elevated temperatures, whereas phosphorylation to AMP, i.e., adenosine-monophosphate, a nucleotide serving as a critical regulator of cellular energy status, remains highly endergonic under the entire T range studied. My analysis suggests that, under standard-state assumptions, comets can thermodynamically support formation routes of nitrogenous bases and simple sugars but not a complete nucleotide assembly. This supports a dual-phase origin scenario, where comets act as molecular reservoirs, with further polymerization and biological activation occurring post-delivery on planetary surfaces. Importantly, these findings represent purely thermodynamic assessments under standard-state assumptions and do not address kinetic barriers, catalytic influences, or adsorption effects on ice or mineral surfaces. The results should therefore be viewed as a baseline map of feasibility, subject to modifications in more complex chemical environments. Full article

The increasing frequency and intensity of wildfires pose severe risks to ecosystems, infrastructure, and human safety. In wildland–urban interface (WUI) areas, nearby vegetation strongly influences building ignition risk through flame contact and radiant heat exposure. However, limited research has leveraged Unmanned Aerial Systems (UAS) remote sensing (RS) to capture species-specific vegetation geometry and predict thermal responses during ignition events This study proposes a two-stage framework integrating UAS-based multispectral (MS) imagery, LiDAR data, and Fire Dynamics Simulator (FDS) modeling to estimate the maximum temperature (T) and heat flux (HF) of outdoor vegetation, focusing on Syzygium smithii (Lilly Pilly). The study data was collected at a plant nursery at Queensland, Australia. A total of 72 commercially available outdoor vegetation samples were classified into 11 classes based on pixel counts. In the first stage, ensemble learning and watershed segmentation were employed to segment target vegetation patches. Vegetation UAS-LiDAR point cloud delineation was performed using Raycloudtools, then projected onto a 2D raster to generate instance ID maps. The delineated point clouds associated with the target vegetation were filtered using georeferenced vegetation patches. In the second stage, cone-shaped synthetic models of Lilly Pilly were simulated in FDS, and the resulting data from the sensor grid placed near the vegetation in the simulation environment were used to train an XGBoost model to predict T and HF based on vegetation height (H) and crown diameter (D). The point cloud delineation successfully extracted all Lilly Pilly vegetation within the test region. The thermal response prediction model demonstrated high accuracy, achieving an RMSE of 0.0547 °C and R² of 0.9971 for T, and an RMSE of 0.1372 kW/m² with an R² of 0.9933 for HF. This study demonstrates the framework’s feasibility using a single vegetation species under controlled ignition simulation conditions and establishes a scalable foundation for extending its applicability to diverse vegetation types and environmental conditions. Full article

Although rectal temperature is reliable, its measurement requires manual handling and causes stress to animals. IRT provides a non-contact alternative but often ignores bilateral eye temperature differences. This study presents an E-S-YOLO11n model for the automated detection of the binocular regions of sheep, which achieves remarkable performance with a precision of 98.2%, recall of 98.5%, mAP@0.5 of 99.40%, F₁ score of 98.35%, FPS of 322.58 frame/s, parameters of 7.27 M, model size of 3.97 MB, and GFLOPs of 1.38. Right and left eye temperatures exhibit a strong correlation (r = 0.8076, p < 0.0001), However, the eye temperatures show only very weak correlation with rectal temperature (right eye: r = 0.0852; left eye: r = −0.0359), and neither figure reaches statistical significance. Rectal temperature is 7.37% and 7.69% higher than the right and left eye temperatures, respectively. Additionally, the right eye temperature is slightly higher than the left eye (p < 0.01). The study demonstrates the feasibility of combining IRT and deep learning for non-invasive eye temperature monitoring, although environmental factors may limit it as a proxy for rectal temperature. These results support the development of efficient thermal monitoring tools for precision animal husbandry. Full article

In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data. A cross-modal attention mechanism, environment-guided modality weighting strategy, and decoupled recognition heads are incorporated to enhance the model’s robustness against small targets, intermodal variations, and environmental disturbances. Evaluated on a high-complexity multimodal field dataset, the proposed model significantly outperforms mainstream methods across four key metrics, precision, recall, F1-score, and mAP@50, achieving 91.5% precision, 89.2% recall, 90.3% F1-score, and 88.0% mAP@50. These results represent an improvement of over 6% compared to representative models such as YOLOv8 and DETR. Additional ablation studies confirm the critical contributions of key modules, particularly under challenging scenarios such as low light, strong reflections, and sensor data noise. Moreover, deployment tests conducted on the Jetson Xavier edge device demonstrate the feasibility of real-world application, with the model achieving a 25.7 FPS inference speed and a compact size of 48.3 MB, thus balancing accuracy and lightweight design. This study provides an efficient, intelligent, and scalable AI solution for pest surveillance and biological control, contributing to precision pest management in agricultural ecosystems. Full article

Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational stages of DCE-MRI for LRTs, including thermal ablation, transarterial chemoembolization (TACE), and transarterial radioembolization (TARE). Preclinical and early-phase studies suggest that DCE-MRI may offer valuable insights into HCC tumor microvasculature, treatment response, and therapy planning. In thermal ablation therapies, DCE-MRI provides a quantitative measurement of tumor microvasculature and perfusion, which can guide more effective energy delivery and estimation of ablation margins. For TACE, DCE-MRI parameters are proving their potential to describe treatment efficacy and predict recurrence, especially when combined with adjuvant therapies. In ⁹⁰Y TARE, DCE-MRI shows promise for refining dosimetry planning by mapping tumor blood flow to improve microsphere distribution. However, despite these promising applications, there remains a profound gap between early investigational studies and clinical translation. Current quantitative DCE-MRI research is largely confined to phantom models and initial feasibility assessments, with robust retrospective data notably lacking and prospective clinical trials yet to be initiated. With continued development, DCE-MRI has the potential to personalize LRT treatment approaches and serve as an important tool to enhance patient outcomes for HCC. Full article

To achieve high-performance forgings of the C83600 tin bronze valve body with a uniform structure that is free from forging defects, rheological data were collected via hot compression experiments. Subsequently, an Arrhenius constitutive model incorporating strain compensation was established. The correlation coefficient, root mean square error, and mean relative error between the predicted values of the model and the experimental results were 0.99326, 5.1898, and 4.022%, respectively, which validated the model’s capability to accurately describe the rheological behavior of C83600. Using this model, the rheological data were incorporated into the Deform material library to enhance its database. A thermal processing map for C83600 under various deformation conditions was then developed. This map indicates that the material demonstrates excellent thermal working stability when the deformation temperature ranges from 850 to 900 K and the strain rate varies between 0.0067 and 0.0483 s⁻¹. Furthermore, numerical simulations were conducted to analyze the forging process, focusing on regions of stress concentration where the average strain rate aligns with the optimal parameters derived from the thermal processing map. This alignment not only verifies the reliability of the hot working map but also confirms the feasibility of the forging process through trial production. Full article