Search Results (2,604)

The striped eel catfish Plotosus lineatus is a venomous Indo-Pacific species that has rapidly expanded within the Mediterranean Sea following its Lessepsian migration through the Suez Canal. Understanding its current distribution and future invasion potential is essential for evaluating ecological risks under ongoing climate change. In this study, ensemble species distribution models were applied to predict the present and future habitat suitability of P. lineatus across the Mediterranean Sea under the CMIP6 SSP2-4.5 climate scenario. Georeferenced occurrence records were compiled from published literature and validated databases. A total of 21 environmental variables were initially evaluated, and after multicollinearity filtering using Pearson correlation (|r| < 0.7) and Variance Inflation Factor (VIF < 10) analyses, 11 environmental predictors obtained from the Bio-ORACLE v3 database were retained for modelling. Nine modelling algorithms were initially evaluated, and six high-performing models were integrated into the final unweighted ensemble model based on predefined AUC and TSS performance criteria. Minimum seawater temperature was identified as the dominant factor controlling habitat suitability, indicating that winter thermal conditions currently represent the principal ecological barrier limiting the Mediterranean distribution of the species. Current projections identified the Levantine Basin as the principal invasion hotspot. Future projections, based on mean environmental conditions for the 2020–2100 period under the CMIP6 SSP2-4.5 scenario, indicated a substantial increase in suitable habitats toward the central Mediterranean, including southern Italy and the Aegean region. These findings suggest that future climatic conditions may facilitate the expansion of P. lineatus into additional areas of the Mediterranean Sea, highlighting the need for continued monitoring and regional management. Full article

Synthetic fibre composites deliver high mechanical performance at a substantial energy and carbon cost, motivating a shift to renewable reinforcements. Flax and hemp are credible candidates, but their uptake is constrained by thermal stability and inherent combustibility, and their performance depends strongly on how completely the laminate is consolidated during manufacture. Vacuum-Assisted Resin Infusion Moulding (VARIM) yields low-void, well-consolidated laminates, yet the thermal and acoustic behaviour of VARIM-processed flax and hemp composites has not been compared directly. This study evaluates unidirectional flax/epoxy and hemp/epoxy laminates, each consisting of five plies produced under identical VARIM conditions, using TGA, DSC, limiting oxygen index (LOI) and UL 94 HB testing, SEM, and four-microphone impedance tube transmission loss (TL) measurements (ASTM E2611). Both laminates remained thermally stable to approximately 200–220 °C. Hemp/epoxy recorded a higher 5 wt.% degradation temperature (222 versus 207 °C), char residue (10.22 versus 9.29 wt.%) and average TL (13.22 versus 11.56 dB, 250–2000 Hz), while both laminates returned an identical LOI of 21.54% and a UL 94 HB rating. The fibre-governed properties therefore differed between the two laminates whereas the flammability response, governed by the shared epoxy matrix, did not. The glass transition temperature also differed (71.9 versus 65.3 °C) but is attributed to a small difference in degree of cure rather than to the reinforcement. The results provide baseline data for selecting VARIM-processed bio-composites for thermal management and passive noise control. The findings support Sustainable Development Goals (SDGs) 9, 12, and 13 through the development of sustainable, low-carbon bio-composite materials. Full article

Cementitious materials are the most widely used construction materials worldwide. Beyond their structural function, they are increasingly recognized as multifunctional materials capable of contributing to thermal energy management and improving the energy performance of buildings. This review provides a comprehensive assessment of cement-based materials for thermal energy storage (TES), covering the fundamental mechanisms of heat transfer, the thermophysical properties governing thermal performance, and the principal TES technologies applicable to cementitious composites. Particular attention is given to sensible heat storage in conventional cement-based materials and latent heat storage achieved through the incorporation of phase change materials (PCMs), including PCM classification, encapsulation techniques, incorporation methods, thermal performance, and current limitations. The review also examines recent advances in multifunctional cementitious composites incorporating lightweight and porous aggregates, recycled materials, nanomaterials, carbon-based additives, fibers, and other functional constituents that enable simultaneous enhancement of thermal energy storage, heat transfer, mechanical performance, durability, and sustainability. The interactions and trade-offs between thermal, mechanical, and durability-related properties are critically discussed to identify the most promising material design strategies for practical applications. Finally, the review highlights the major scientific and technological challenges and outlines future research directions toward intelligent, low-carbon, and energy-efficient multifunctional cementitious materials for next-generation buildings. Full article

Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R² = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R² of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture. Full article

The study was conducted to establish the thermoneutral zone (TNZ) of Murrah buffalo calves under precisely regulated environmental conditions. Six healthy calves (8–10 months) were exposed to progressively decreasing (24–15 °C; Temperature-Humidity Index (THI): 72.45–58.98) and increasing (25–40 °C; THI: 73.49–90.32) ambient temperatures with 3 °C intervals in a psychrometric chamber. Each exposure was maintained for 10 consecutive days under cyclic conditions (12 h exposure and 12 h thermoneutral recovery). Physiological responses and body surface temperature (BST) were recorded, and blood samples were collected on day 10 at 1500 h. Segmented regression analysis was performed using the SegReg (Oosterbaan, 2017) software program to identify lower and upper critical temperatures (LCT, UCT) and corresponding THI thresholds. LCTs ranged from 18.06 to 19.14 °C across different responsive parameters, with breakpoints (BPs) at 18.60 °C (SE_BP = 0.634) for pulse rate (PR), 18.15 °C (SE_BP = 0.945) for haemoglobin, 18.15 °C (SE_BP = 0.189) for lymphocytes, 18.24 °C (SE_BP = 0.404) for granulocytes, 19.14 °C (SE_BP = 1.09) for reactive oxygen species (ROS), 18.15 °C (SE_BP = 23.9) for superoxide dismutase (SOD), and 18.06 °C (SE_BP = 4.2) for cortisol; no distinct LCT was detected for respiratory rate (RR), rectal temperature (RT), total leukocyte count (TLC), or heat shock protein (HSP) 70/HSP90 expression. BST at different locations exhibited an LCT between ~20 and 21 °C. UCTs for PR, RR and RT were observed at 28.9 °C (SE_BP = 0.892), 28.6 °C (SE_BP = 0.843) and 29.2 °C (SE_BP = 1.16), respectively. Neither BST nor erythrocytic parameters exhibited a distinct UCT; however, BST increased progressively with increasing exposure temperature. TLC, lymphocyte and granulocyte % showed UCTs of 28.15 °C (SE_BP = 0.753), 28.15 °C (SE_BP = 0.497) and 32.65 °C (SE_BP = 14.20), respectively. The UCT for cortisol was observed at 28.15 °C (SE_BP = 5.52), whereas ROS and SOD showed UCTs of 28.75 °C (SE_BP = 4.67) and 34.15 °C (SE_BP = 6.55), respectively. HSP70 and HSP90 exhibited breakpoints at 35.35 °C (SE_BP = 1.32) and 34.30 °C (SE_BP = 2.11), respectively. Integration of key physiological and systemic responses (excluding BST) indicated a TNZ ranging from 19.14 °C to 28.15 °C. These findings provide a multi-level characterization of thermophysiological responses in buffalo calves and support the use of integrated biological indicators for defining thermoneutral conditions under controlled environments. The estimated TNZ of 19.14–28.15 °C provides a practical reference for managing Murrah buffalo calves. Producers should monitor housing temperature and consider ventilation, shade and air movement when temperatures approach or exceed ~28 °C while providing protection from cold when temperatures fall below ~19 °C. This may help reduce thermal strain and maintain normal physiological function. Full article

Hybrid electric vehicle (HEV) front-face styling is jointly constrained by functional requirements for engine intake, radiator cooling, and thermal management and by demands for brand identity and emotional expression. Existing Kansei engineering studies have largely focused on whole-vehicle exteriors or generic electrified vehicles, paying insufficient attention to the functional boundaries of HEV front grilles. Moreover, culturally informed automotive styling often relies on designers’ subjective associations and lacks a coherent design pathway. To address these gaps, this study proposes a rough-set-driven Kansei design method for HEV front faces under cultural-semantic constraints. First, an entropy-weighted neighborhood rough-set method is used to identify key Kansei requirements. A rough-set-induced hybrid-kernel prediction model is then constructed by combining rough-set indiscernibility relations with nonlinear similarity, thereby mapping discrete front-face morphological features to users’ Kansei evaluations and predicting the performance of different morphological combinations. Finally, the resulting design knowledge is integrated with the structural characteristics of traditional motifs to generate culturally oriented front-face concepts. Results identified power, premium quality, and approachability as the three key Kansei requirements for HEV front faces. The proposed rough-set-induced hybrid-kernel support vector regression (RSIHK-SVR) model achieved a mean coefficient of determination (R²) of 0.927 and a root mean square error (RMSE) of 0.157 on the test set. Compared with the optimized standard radial basis function (RBF) kernel models and the single rough-set-induced-kernel model, RSIHK-SVR achieved the highest predictive accuracy on the test set (R² = 0.927, RMSE = 0.157), improving R² by 0.8–13.3% and reducing RMSE by 3.1–35.9% across the comparator models, thereby confirming the effectiveness of the hybrid-kernel strategy. The model-predicted morphological configurations were then integrated with the structural characteristics of bronze animal-mask, ice-crackle lattice, and fangsheng motifs to develop three front-face concepts targeting power, premium quality, and approachability, respectively. User evaluations further showed that all three concepts effectively communicated their intended Kansei semantics and exhibited favorable cultural-semantic compatibility. The proposed method thus provides quantitative decision support for conceptual HEV front-face designs with cultural identity and differentiated styling. Full article

To address critical energy access challenges, overcome the limitations of conventional baking systems, and bridge the research gap in solar-assisted injera baking, this paper presents an experimental investigation of a photovoltaic (PV)-powered injera baking system integrated with a solar salt mixture for thermal storage. In addition to introducing the PV system to the injera baking application, this study incorporates self-regulating positive temperature coefficient (PTC) heating elements and a storage design that has not been previously investigated for injera baking—an integrated unit that combines the base plate and thermal storage to facilitate heat transfer during charging and discharging. The system’s performance was assessed by conducting experiments based on energy consumption during charging and the utilized energy during the cyclic baking process. Conducted in May, the test demonstrated that the charging process achieved a complete melting of the PCM within 6.26 h. Under a cycle of 5 min of average baking time followed by 9 min of plate reheating, the system produces 6 injeras from the stored heat with a significant amount still in the storage. The storage system exhibited an efficiency of 74.6% during charging and 65% during discharging. The findings from this research have the potential to address energy challenges in off grid communities, as well as within institutions that still rely on biomass for injera baking by managing solar energy intermittency and providing stable thermal output. For future work, we recommend incorporating customized plate type self-regulating heating elements with higher temperature ratings and non-stick baking plates to enhance performance. Full article

Personal thermal management (PTM) textiles can reduce building energy consumption and improve personal comfort, yet their practical application is constrained by the inherent trade-off between flexibility, thermal insulation, and mechanical strength. Herein, porous single-component polyimide (PI) fibers were fabricated via coagulation bath-modulated wet spinning of 3,3′,4,4′-benzophenone tetracarboxylic dianhydride (BTDA)–4,4′-oxydianiline (ODA) poly(amic acid) (PAA). By tuning the EtOH/H₂O ratio (20/80–60/40) and winding speed (2.6–13.1 mm/s), the fiber cross-sectional morphology evolves from finger-like macropores to uniform spongy networks, with diameters controllable from 120 to 335 μm. The PI porous fibers exhibit a maximum tensile strength of 56.79 MPa, elongation at break of 13.89%, toughness of 4.98 MJ/m³, and thermal conductivity as low as 0.043 W·m⁻¹·K⁻¹. The highly imidized structure was confirmed by FTIR (imidization index = 0.848), and TGA revealed high thermal stability with 5% weight loss temperatures of 491 °C (N₂) and 488 °C (air). Compared with commercial insulators, a single-layer PI fabric (0.892 mm) shows thermal insulation comparable to that of the thicker aramid 1313 fabric (1.588 mm) under the same 100–200 °C hot-plate conditions, while also exhibiting self-extinguishing behavior equivalent to that of aramid 1313. The 5-layer PI stack (3.637 mm) is only half as thick as glass fiber cotton (7.342 mm) but retains 84–91% of its temperature difference, delivering 1.7–1.8 times higher thickness-normalized insulation efficiency. The ultrathin porous PI fabrics integrate robust mechanical performance, excellent thermal shielding, and flame retardancy, and are promising for extreme-environment thermal management including fire protection, spacecraft thermal control, and battery insulation. Full article

Asian elephants (Elephas maximus) face substantial thermoregulatory constraints because of their large body size, low relative surface area, sparse hair, and lack of functional sweat glands. Reliable body temperature measurement is essential for assessing thermal status and evaluating welfare in both wild and managed populations, but conventional rectal thermometry requires close physical contact, animal training, and repeated manual handling, making high-frequency, continuous, large-scale monitoring impractical. This study developed a non-invasive framework for automatically detecting the outer ear and extracting auricular surface temperature from infrared thermograms. Rectal temperature, regional surface temperatures, ambient temperature, and relative humidity were measured synchronously in eight semi-captive Asian elephants, yielding 425 matched observations. The associations between rectal temperature and the surface temperatures of three anatomical regions (head, outer ear, torso and limbs) were analyzed using repeated-measures correlation accounting for the non-independence of repeated measurements. Mean outer-ear temperature showed the strongest within-individual association with rectal temperature (r_rm = 0.395, p < 0.001), identifying the outer ear as the optimal thermal window for subsequent automated monitoring. Eight lightweight YOLO models—YOLOv5n, YOLOv5s, YOLOv8n, YOLOv8s, YOLO11n, YOLO11s, YOLO26n, and YOLO26s—were trained on 2178 annotated infrared images and evaluated on an independent 194-image test set from extra elephants. Model performance was assessed using detection metrics, inference speed, Bland–Altman agreement, Taylor diagram statistics, and a weighted multi-criteria score with Monte Carlo sensitivity analysis. YOLO11n achieved the best overall performance, with an mAP50 of 0.933 and an inference speed of 164 frames per second. The proposed framework provides an efficient method for automated auricular temperature monitoring and has potential applications in elephant welfare management and remote physiological surveillance. Full article

The increasing generation of plastic waste has intensified the search for sustainable recycling technologies capable of recovering valuable resources while minimizing environmental impacts. Among emerging solutions, thermochemical recycling processes such as pyrolysis, gasification, and hydrothermal liquefaction have gained significant attention. However, the presence of chlorine-containing polymers, particularly poly(vinyl chloride) (PVC), represents a major challenge due to the release of hydrogen chloride (HCl), catalyst deactivation, equipment corrosion, and contamination of valuable products. Consequently, effective dechlorination pretreatments are essential for improving process performance and product quality. This review provides a comprehensive overview of current pretreatment technologies for chlorine management in plastic solid waste. Mechanical sorting, density-based separation, chemical extraction, alkaline treatments, hydrothermal processing, thermal dehydrochlorination, catalytic methods, and emerging photocatalytic approaches are critically analyzed and compared. The mechanisms, operating conditions, chlorine removal efficiencies, technological readiness levels, and industrial applicability of each method are discussed. Moreover, particular attention will be devoted to integrating different pretreatment technologies to achieve an overall enhanced chlorine removal efficiency before thermochemical technologies. Finally, reported techno-economic, environmental, and safety indicators are compared where quantitative data are available, while the principal data gaps and technological barriers to large-scale implementation are identified. Full article

The 2021 eruption of Cumbre Vieja on La Palma, Canary Islands, produced extensive lava flows and major landscape transformation, providing an opportunity to evaluate remote sensing approaches for mapping the extent of an emplaced lava flow. This study assessed direct spectral, classic Rao’s quadratic entropy (RaoQ), and multidimensional RaoQ approaches using satellite observations acquired before and after the eruption. Optical, radar, thermal infrared, and night-time radiance datasets were evaluated within a common change-detection framework implemented in Google Earth Engine. Difference maps were converted into binary change maps using a histogram-based thresholding procedure calibrated on the reference delineation and evaluated against the Copernicus Emergency Management Service (CEMS) lava-flow reference and no-change validation areas derived from ESA WorldCover using multiple accuracy metrics. Because the change reference is the final CEMS lava-flow delineation and the no-change samples lie outside a 100 m buffer around it, the accuracy figures reported here quantify the mapping of lava-flow extent and not of other eruption-related effects such as ash deposition or vegetation damage beyond the flow margins. Among the direct spectral approaches, the NHI_SWIR index achieved the highest overall classification performance. Among the individual Sentinel-2 bands, B12 achieved the highest overall accuracy, whereas B8A achieved the highest true skill statistic; both exceeded the multidimensional RaoQ configurations in mean prevalence-independent discrimination. Within the classic RaoQ approach, MIRBI produced the strongest single-variable heterogeneity-based results. The best multidimensional configurations combined Sentinel-2 B8A and B12 with Sentinel-1 VV, demonstrating that radar backscatter provided complementary information to optical observations. Although multidimensional RaoQ did not surpass the best direct spectral variables, it produced competitive and spatially coherent representations of lava-flow disturbance. The evaluated thermal infrared and night-time radiance products did not provide competitive discrimination under the selected spatial and temporal conditions for different reasons: a thresholding limitation in the case of the Landsat thermal product, and an unfavourable ratio of pixel size to flow width in the case of the night-time radiance products, while the MODIS product returned no valid validation points and could not be evaluated. These product-specific explanations rest on a small number of comparisons and are provisional. These results show that carefully selected Sentinel-2 SWIR variables remain the strongest benchmark for detailed mapping of fresh lava-flow disturbance, while multidimensional RaoQ provides a framework for optical–radar integration that requires no training data or prior classification. Because the evaluation covers a single eruption in a single landscape, transfer of the framework to other events and settings remains to be demonstrated. Full article

Sustainability policies introduced under the European Green Deal have strengthened climate-related disclosure requirements for agri-food companies. In particular, the Corporate Sustainability Reporting Directive requires in-scope companies to transparently disclose information on their environmental performance. However, the academic literature on decarbonisation in the olive oil sector remains fragmented. This systematic literature review synthesises findings by considering cultivation, milling and retail, and waste management as interconnected stages of the olive oil supply chain and by developing a matrix linking decarbonisation strategies to the relevant European Sustainability Reporting Standards (ESRS) environmental, social and governance (ESG) topics. Following the PRISMA protocol, 42 peer-reviewed studies from Scopus and Web of Science were included in the final synthesis, covering cultivation (RQ1), milling and retail (RQ2), and waste management (RQ3). The cultivation stage represents an important part of the emission profile of the chain while also offering potential for carbon sequestration through sustainable management practices, such as reduced tillage, cover crops, organic amendments and biochar application. In the downstream stages, the mill and its retail interface rely on a different set of measures, including two-phase extraction, rooftop photovoltaic systems, thermal recovery from pits, and lighter bottles transported in bulk. Waste management also offers opportunities to recover value from pomace, mill wastewater and pruning waste through biogas, biochar, compost or phenolic extracts. The potential for a net-negative carbon balance is context-dependent and varies with system boundaries, the balancing period, functional units, and the methods used to account for carbon sequestration. The matrix offers a clear classification of decarbonisation strategies and ESRS topics, opening valuable avenues for upcoming studies to extend its practical utility. Full article

Double-skin façades (DSFs) offer a promising building-envelope strategy for improving thermal management by promoting heat’s removal from the façade cavity before it is transferred toward the indoor environment. This study numerically investigates the influence of cavity width on the thermo-fluid performance of a mechanically ventilated DSF under summer operating conditions. A two-dimensional computational fluid dynamics (CFD) model was developed using the RNG k-ε turbulence model together with the discrete ordinates radiation model. Mechanical ventilation was imposed through a velocity inlet of 0.765 m s⁻¹, with an inlet air temperature of 17 °C and a solar radiation intensity of 365.4 W·m⁻². The numerical model was validated against published experimental temperature measurements, yielding an average absolute relative error of approximately 5.65%. The validated model was subsequently applied to cavity widths ranging from 0.10 to 0.70 m. Increasing the cavity width substantially modified the airflow development and thermal field. The monitored temperature decreased from 31.66 °C at 0.10 m to 17.64 °C at 0.50 m, while further enlargement produced only minor reductions to 17.43 and 17.28 °C at 0.60 and 0.70 m, respectively. The total heat-transfer rate increased from approximately 1000 W at 0.10 m to a maximum of 1388 W at 0.50 m before slightly decreasing to 1379 and 1376 W at 0.60 and 0.70 m, respectively. This temperature reduction enhances heat removal from the façade cavity, helping to limit heat transfer toward the indoor environment and improve indoor thermal comfort under summer conditions. These results demonstrate a non-monotonic relationship between cavity width and heat-removal performance, with 0.50 m providing the highest heat-transfer rate among the investigated configurations. This result is specific to the geometry, boundary conditions, ventilation rate, and operating conditions considered in the present study. It should not be interpreted as a universally optimal cavity width for mechanically ventilated DSFs. The findings highlight the importance of cavity-width selection in the thermal management and design of mechanically ventilated DSFs for energy-efficient building envelopes. Full article

This systematic review examines how artificial intelligence (AI) technologies relevant to human-factor risk reduction can be integrated into international and Spanish maritime safety frameworks. The formal PRISMA corpus comprises 23 core sources (13 peer-reviewed studies and 10 regulatory, institutional or technical documents), while a separate supplementary search provides recent independent technical and regulatory evidence up to 31 August 2026. The analysis covers computer vision, thermal and near-infrared sensing, multimodal fusion, behavioral and fatigue analysis, and onboard edge processing, with particular attention to precision, recall, false alarms, latency, computational requirements and operational robustness. The evidence shows that high detection performance can be achieved in specific maritime datasets, but the reported values depend strongly on the task, sensor, dataset and hardware and cannot be treated as a universal accuracy threshold. A system architecture is therefore proposed in which heterogeneous sensors feed synchronized edge processing, event verification, alarm management, VDR-compatible event logging, and human confirmation with defined fail-safe behavior. On the regulatory side, the study proposes staged adaptations of SOLAS, the ISM Code, STCW, MLC and Spanish inspection frameworks. The 2026 IMO MASS Code, considered as supplementary regulatory evidence, provides a relevant precedent for goal-based approval, risk assessment and progressive operational experience. Fixed tonnage and implementation-date thresholds are consequently treated as illustrative parameters rather than validated requirements; any mandatory carriage provision should be supported by formal safety assessment, type approval and operational evidence. The resulting framework links electronics implementation with a short-, medium- and long-term regulatory roadmap for safer merchant-ship operations. Full article

Advances in artificial intelligence, high-performance computing, and generative AI technologies have driven a rapid increase in the memory bandwidth and data throughput required of semiconductor systems, establishing High Bandwidth Memory (HBM)—which vertically stacks multiple DRAM dies—as a key enabling memory technology. However, increasing the stack count and shrinking the interconnect pitch in HBM not only intensify vertical heat accumulation and hotspot formation but also give rise to complex reliability issues, including thermo-mechanical stress arising from coefficient-of-thermal-expansion (CTE) mismatch, package warpage, interfacial delamination, Cu protrusion, void formation, and joint degradation. This review analyzes the heat-generation and heat-transfer mechanisms of HBM packages and examines package-level thermal management strategies based on thermal interface materials, underfill, non-conductive film, epoxy molding compound, heat spreaders, and high-thermal-conductivity composites. It further summarizes the current crowding, electromigration, Cu–dielectric interfacial defects, and thermo-mechanical failure mechanisms that arise at fine-pitch interconnects and hybrid-bonding interfaces, together with the material and process design strategies developed to mitigate them. In addition, structure-based thermal management technologies—thermal TSVs, embedded cooling, and hybrid bonding—are compared. This review emphasizes that the thermal bottlenecks and reliability degradation of HBM are interconnected through interfacial thermal resistance, interfacial adhesion, residual stress, and interfacial defects, and proposes that next-generation, highly stacked HBM requires a multi-scale thermal-reliability co-design that integrally controls the heat-, stress-, and current-transfer pathways across the entire package and interconnect domain, rather than relying on the improvement of individual material properties alone. Full article