Search Results (614)

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures. Full article

The postharvest preservation of banana (Musa paradisiaca) is essential to maintain fruit quality and minimize losses during storage and export. Packaging films play a critical role in protecting fruit from mechanical damage and environmental stress. This study compared the physicochemical and mechanical properties of two commercial polyethylene films—high-density polyethylene (HDPE) and low-density polyethylene (LDPE)—under controlled postharvest conditions (13 °C, 95% RH). Films were characterized using Differential Scanning Calorimetry (DSC), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), and Flame Atomic Absorption Spectroscopy (AAS), while tensile testing evaluated mechanical performance. HDPE exhibited greater melting stability (+8%), relative crystallinity (+12%), and tensile strength (+15%) compared with LDPE, which presented higher flexibility. HDPE contained trace zinc (0.82–0.94 mg/100 g), whereas LDPE was zinc-free. Both polymers retained their polyethylene fingerprint without oxidative degradation, confirming structural integrity under cold storage. The TGA data verified the absence of thermally unstable additives rather than operational degradation, supporting material homogeneity. Overall, HDPE demonstrated superior stability and durability for banana packaging applications, highlighting the relevance of integrated polymer diagnostics for safe and sustainable postharvest systems. Full article

In this study, we present a non-invasive and contactless method for estimating the internal temperature of Cr:LiCAF laser crystals using temperature-dependent shifts in fluorescence emission peaks. A high-resolution calibration dataset was created with 181 spectral points from 10 to 100 °C. Linear regression yielded a temperature estimation model with an R² of 0.73, which was validated under both lasing and non-lasing conditions. To further evaluate the reliability of this optical thermometry method, thermal imaging data from a FLIR E75 infrared camera were incorporated. Surface temperatures measured at various diode current levels closely matched the internal temperature predictions based on fluorescence shifts (MAE = 0.775 °C, R² = 0.993), confirming the robustness of the method. This dual-approach validation enhances confidence in using fluorescence-based diagnostics for real-time thermal monitoring in laser systems. The combined use of spectrometer-based and thermal camera measurements suggests potential for hybrid diagnostics in laser research and development, offering improved thermal feedback for optimizing high-power laser performance. Full article

Brushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies. Full article

This study presents a modular diagnostic framework for evaluating thermal degradation in aging building envelopes by integrating infrared thermography, panoramic reconstruction, and deep learning-based semantic segmentation into a unified workflow. The methodology combines image registration, panoramic synthesis, façade component segmentation, and quantitative surface temperature analysis to provide scalable and reproducible diagnostics. By excluding fenestration zones—where infrared measurements are physically unreliable—the framework focuses on opaque wall regions and window surroundings to ensure physically meaningful evaluation. Field validation was conducted on a multi-story office building constructed in 1996. The diagnostic indicators revealed a mean wall surface temperature of 14.3 °C with a standard deviation of 5.6 °C, and a temperature factor ranging from 0.67 to 0.78 under measured conditions. The vulnerable area ratio reached 9.1% for walls, while window areas showed greater vulnerability at 12.74%, with anomalies concentrated at frame–glass interfaces and perimeter seals. These quantitative results confirmed the framework’s ability to detect thermal irregularities and visualize localized anomalies. More importantly, the contribution of this study lies in establishing a systematic and extensible diagnostic pipeline that advances building envelope analysis, supporting large-scale energy audits, retrofit prioritization, and sustainable building management. Full article

Baltimore’s historic row-home neighborhoods face escalating risks to energy, heat, and durability under intensifying climate stress. This study develops a Housing-Performance Atlas that quantifies multi-hazard performance for eight representative archetypes using DesignBuilder/EnergyPlus Version 7.3.1.003, under Baltimore TMY3 boundary conditions. Performance is evaluated across the following four adaptation domains: energy use intensity, passive survivability during 72 h outage events, roof overheating exposure (>150 °F exceedance hours), and material service life derived from ISO 15686 and synthesized into Lean and Full Deficit Indices for comparative resilience ranking. Results show that EUI ranged from 46.7 to 67.6 kBtu ft⁻²·yr⁻¹, survivability from 0 to 23 h, and roof temperatures exceeded 150 °F for 150–210 h, shortening roof service life by up to 10 years. Composite Lean and Full Deficit Indices ranged 7.8–92.4, ranking Model 5 (end-unit, flat roof, two-story with basement) as the most resilient configuration and Model 8 (end-unit, pitched roof, three-story above-grade) as the least resilient due to compounded overheating and energy losses. Heat-related domains accounted for nearly 70% of overall resilience deficits, confirming thermal safety and roof reflectivity as retrofit priorities. The Housing-Performance Atlas establishes a reproducible diagnostic framework linking simulation, service life, and resilience metrics to guide cost-effective, climate-responsive retrofits in Baltimore’s aging urban housing stock. Full article

(1) Background: Surgical site infections (SSIs) pose a significant clinical challenge, with early detection hindered by the overlap between physiological postoperative inflammation and incipient infection. Continuous wound temperature monitoring offers a promising, non-invasive method to identify subtle thermal deviations that precede overt clinical signs. This review synthesizes current evidence on the utility of temperature monitoring as an early predictor of SSI and evaluates its clinical applications. (2) Methods: A narrative literature review was conducted using PubMed and Embase for English-language studies published between 2015 and 2025. Following PRISMA principles, eligible studies were selected that examined continuous or repeated local wound temperature measurements in adult postoperative patients and their association with a clinical diagnosis of SSI. (3) Results: Six studies met the inclusion criteria. Key findings indicate that infected wounds may paradoxically exhibit lower temperatures “cold spots” than non-infected wounds in the early postoperative period. Dynamic indicators, particularly the temperature difference (ΔT) between the wound and adjacent skin and the temperature trajectory over time, proved more predictive than single, isolated measurements. Confounding factors such as patient adiposity were noted to influence thermal signatures. (4) Conclusions: Wound temperature monitoring is a valuable strategy for the early risk stratification of SSI. The analysis of thermal trends and dynamic parameters holds greater diagnostic significance than single readings. Integration with other biomarkers may further enhance specificity, but the development of standardized measurement protocols is essential for reliable clinical implementation and improved postoperative outcomes. Full article

Background/Objectives: Diode lasers are used as adjuncts for endodontic disinfection, but their depth-resolved effects on root dentin are insufficiently described. This ex vivo study used optical coherence tomography (OCT) to qualitatively document laser-related morphological signatures on canal walls. Methods: Palatal roots from extracted maxillary first molars were standardized and hemisectioned to create specimens allocated to a conventional diode-laser protocol, a higher-power protocol, or control. A 940-nm diode laser with endodontic tips was applied per group. Swept-source OCT acquired serial B-scans along the root length. Two endodontists reviewed images for thermally induced morphological alterations (TIMAs). Reporting is descriptive. Results: OCT revealed laser-related hyper-reflective linear/radial signatures extending from the canal lumen toward the external root surface in laser-treated specimens. Qualitatively, signatures appeared more conspicuous and extended deeper with the higher-power protocol than with the conventional protocol. Findings were most evident in the coronal/middle thirds. Control specimens served to contextualize background appearances from preparation and sectioning. Representative B-scans illustrate typical patterns. The novelty of the present study results from the identification of areas of morphological alteration through the OCT examination of the walls of the root canals. Conclusions: Depth-resolved OCT can visualize dentinal alterations associated with diode-laser irradiation in an ex vivo model. These observations support careful parameter selection and motivate in situ studies with concurrent temperature monitoring and histologic correlation. Full article

Miniaturized long-wavelength infrared (LWIR) imaging systems are highly desirable for applications such as portable thermal sensing, unmanned surveillance, and medical diagnostics. Conventional refractive optics in the LWIR regime often require multiple lens configurations to extend depth of field (DoF), leading to increased size, weight, and cost. Although existing LWIR metalenses demonstrate competent capabilities, comprehensive approaches to DoF engineering have yet to be explored. Here, we demonstrate a miniature large-DoF camera using a metalens. The designed metalens features a 14 mm diameter aperture and weighs only 0.8 g while maintaining sharp focus over a working distance ranging from 1 m to 22 m. By leveraging subwavelength phase engineering, the metalens achieves high-resolution imaging with low aberration. The integrated camera exhibits an ultra-compact form factor, i.e., 2.3 cm × 2.3 cm × 1.2 cm (length × width × height) and weighs just 25 g. Experimental results confirm the superior DoF performance, enabling clear imaging across varying distances without mechanical refocusing. The advance provides a promising pathway toward ultra-compact, large-DoF LWIR imaging systems for applications ranging from autonomous vehicles to portable medical diagnostics and miniature surveillance devices. Full article

A series of multi-parameter, multi-layer observations was conducted to study possible electromagnetic precursors associated with the M 6.7 earthquake that struck Iburi, Hokkaido, Japan, at 18:07:59 UT on 5 September 2018. The most significant observation is seismogenic lower-ionospheric perturbations in the propagation anomalies of sub-ionospheric VLF/LF signals recorded in Japan and Russia. Other substantial observations include the GIM-TEC irregularities, the intensification of stratospheric atmospheric gravity waves (AGWs), and the satellite and ground monitoring of air temperature (T), relative humidity (RH), atmospheric chemical potential (ACP), and surface latent heat flux (SLHF). We have found that there were very remarkable VLF/LF anomalies indicative of lower-ionospheric perturbations observed on 4 and 5 September just before the EQ date and even after it from the observations in Japan and Russia. In particular, the anomaly was detected for a particular propagation path from the JJY transmitter (Fukushima) to a VLF station at Wakkanai one day before the EQ, i.e., on 4 September, and is objectively confirmed by machine/deep learning analysis. An anomaly in TEC occurred only on 5 September, but it is unclear whether it is related to a pre-EQ effect or a minor geomagnetic storm. We attempted to determine whether any seismo-related atmospheric gravity wave (AGW) activity occurred in the stratosphere. Although numerous anomalies were detected, they are most likely associated with convective weather phenomena, including a typhoon. Finally, the Earth’s surface parameters based on satellite monitoring seem to indicate some anomalies from 29 August to 3, 4, and 5 September, a few days prior to EQ data, but the ground-based observation close to the EQ epicenter has indicated a clear T/RH and ACP on 2 September with fair weather, but no significant data on subsequent days because of severe meteorological activities. By integrating multi-layer observations, the LAIC (lithosphere–atmosphere–ionosphere coupling) process for the Hokkaido earthquake appears to follow a slow diffusion-type channel, where ionospheric perturbations arise a few days after ground thermal anomalies. This study also provides integrated evidence linking concurrent lower-ionospheric, atmospheric, and surface thermal anomalies, emphasizing the diagnostic value of such multi-parameter observations in understanding EQ-associated precursor signatures. Full article

Introduction: Infrared thermography (IRT) is a non-invasive, non-ionizing imaging modality capable of rapidly capturing surface temperature variation. In dentistry, particularly orthodontics and TMD evaluation, IRT may serve as a valuable complementary tool to be added in conventional diagnostic protocols. Objective: Correlate possible relationships between thermographic findings of orofacial structures and cephalometric landmarks. Methods: An infrared imaging camera, FLIR^® i7, was used to record the regions of interest, correspondent to the temporal, masseter and orbicular oris muscles, in adolescents (n = 22). Bilateral temperature differences were considered as thermal asymmetries with a conventional threshold of 0.3 °C to distinguish an eventual hyperactivity or hyperfunctions of detrimental structures. The Trevisi cephalometric parameters that were taken into consideration for the study were SNA, SNB, ANB, OccltoSn, Wits relation to base and Molar/canine classes. Results: Most of the participants showed a normal temperature difference ΔΤ for the upper and lower orbicular oris muscle, right vs. left, 96% and 92%, respectively. The other ROIs presented a mixed pattern of thermal asymmetries; however, no statistically significant differences were found when crossed with the cephalometric landmarks. Conclusions: Asymmetrical patterns of infrared thermography can aid on the diagnosis and treatment plan of an orthodontic appointment, since the actual stability of pos-orthodontic treatment is highly dependent on the muscular activity of the tongue and lips, in particular when the patient has atypical swallowing. Our findings suggest that this technique can be used to quantify anatomical landmarks relevant to craniofacial morphology in specific populations, particularly at ages where muscular functional activity is strongly correlated with dentoskeletal development. Full article

Background/Objectives: This review systematically assesses machine learning (ML) and deep learning (DL) applications using images to diagnose diabetic foot ulcers (DFUs), focusing on detection, segmentation, and classification. The study explores trends, challenges, and quality measurements of the reviewed research. Methods: A comprehensive search was conducted in October 2025 across 14 databases, covering studies published between 2010 and 2025. Studies employing ML/DL for DFU diagnosis with accurate measurements were included, while those without image-based methods, AI techniques, or relevant outcomes were excluded. Out of 4653 articles initially identified, 1016 underwent detailed review, and 102 met the inclusion criteria. Results: The analysis revealed that ML/DL models are effective tools for DFU diagnosis, achieving accuracy between 0.88 and 0.97, specificity between 0.85 and 0.95, and sensitivity between 0.89 and 0.95. Common methods included Support Vector Machines (SVMs) for ML and U-Net or fully convolutional neural networks (FCNNs) for DL. Recent studies also explored thermal infrared imaging as a promising diagnostic technique. However, only 45% of segmentation datasets and 67.3% of classification datasets were publicly accessible, limiting reproducibility and further development. Conclusions: This review provides valuable insights into trends and key findings in ML/DL applications for DFU diagnosis. It highlights the need for improved data availability and sharing to enhance reproducibility, accuracy, and reliability, ultimately improving patient care. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical–thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH₄/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16%, 25%, and 60% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH₄/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste. Full article