Search Results (407)

In extreme scenarios such as nuclear explosions and high-energy radiation detection in space, UV-cured adhesives are usually used as coupling media to bind tapered optic fiber arrays with intensified charge-coupled devices or complementary metal–oxide semiconductors and a tapered optic fiber array for effective optical signal transmission. To address the issue of weak bonding strength caused by the small binding area between charge-coupled devices or complementary metal–oxide semiconductors and TOFA, a stage-wise curing process was investigated and proved to be efficient through comparison with the single curing process. The effect of interval time between the initial and final curing on coupling strength was characterized by tensile strength, shear strength and shock acceleration testing, and the samples were exposed to high and low temperatures for evaluation of their environmental adaptability. The curing mechanism was analyzed by surface morphology of the adhesive layer after decoupling and an energy-dispersive X-ray spectroscopy elemental analysis of interface layer. The results show that when the interval time is extended from 5 min to 60 min, the shock acceleration of the coupling device decreases by 26.1%, while the tensile and shear strengths also decrease by 49.4% and 60.7%, respectively. The decline in coupling strength is attributed to oxygen inhibition during interval time. The exposure of the adhesive surface to the air allows oxygen to diffuse into and react with active the free radicals that remain from the initial curing, which inhibits further polymerization and generates a thin, incompletely cured weak boundary layer. These findings provide insights for optimizing stage-wise curing processes and improving the reliability of coupled imaging devices. Full article

The global transition towards sustainable food systems has intensified the search for alternative protein sources that can meet human nutritional demands with reduced environmental impacts. Although microalgae are rich in protein, their applications in food remain limited due to thick cell walls and intense green color. The aim of this study is to modify Chlorella vulgaris by high-pressure homogenization (HPH) and decolorization to improve its processability for extrusion-based 3D printing. Microalgal biomass was pretreated by HPH at different pressures (10,000, 15,000, 20,000 psi) for one to three passes, followed by pigment removal using ethanol of different concentrations (70, 85, 100%). Microscopic imaging shows that HPH effectively disrupted microalgal cell walls and caused cell disintegration, resulting in increased foaming stability (22–28%) but lower solubility (up to 24%), with other functional properties largely preserved. Ethanol treatments markedly decolored microalgae and increased their water-holding capacity (10–45%) and solubility (6–11%). The formulation of HPH-treated decolorized microalgae with soy protein isolate and xanthan gum increased the viscosity (66–179%) and elasticity (78–235%) of printing inks. The resulting 3D prints show higher hardness (47–128%), springiness (up to 155%) and chewiness (47–408%). The information obtained from this study provides guidance for modifying the functional and rheological properties of microalgae and contributes to advancing the formulation and manufacturing of microalgae-based foods. Full article

Overwintering peat fires are increasingly reported in the boreal regions, where they persist underground through winter and reignite in spring, intensifying greenhouse gas emissions and landscape degradation. This study investigates the conditions that enable peat fires to survive freezing and snow cover, and presents practical methods for their winter detection and suppression. We combined satellite data, UAV-based thermal imaging, time-lapse photography, and ground measurements of temperature, groundwater depth, and peat moisture to identify active overwintering hotspots. Our results show that these fires persist primarily where groundwater levels remain below 60 cm, particularly under tree roots, compacted soil, or elevated terrain that limits moisture recharge. UAV thermal imaging proved the most reliable detection tool, identifying 98% of hotspots. We developed and successfully applied a winter extinguishing method that involves mechanical disruption and dispersion of smoldering peat over frozen ground, allowing rapid cooling without re-ignition. These findings clarify the mechanisms sustaining overwintering fires and provide an effective approach for their mitigation, contributing to reduced emissions and improved management of boreal peatlands vulnerable to climate change. Full article

The rapid increase in companion animal ownership has intensified the demand for innovative tools that support animal health and overall welfare. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach in veterinary medicine. However, its application beyond clinical diagnostics, especially in behaviour and personality assessment, remains fragmented and insufficiently integrated into routine practice. This systematic review aims to synthesise current knowledge on AI-based applications in companion animal care, with a focus on behavioural monitoring, personality prediction, and welfare-related challenges. Following PRISMA guidelines, a structured literature search was conducted in the Scopus and PubMed databases from 2020 to 2025. In addition, grey literature sources were searched to capture relevant non-peer-reviewed data. A total of 115 studies met the inclusion criteria and were included in the analysis. Eligibility criteria included studies applying AI methods (machine learning or deep learning) to companion animals (dogs, cats, and exotic pets), while studies on humans, farm animals, or without AI methods were excluded. Due to the heterogeneity of included studies, no formal risk of bias assessment was performed, and results were synthesised narratively. The findings indicate that AI applications are most advanced in diagnostic imaging and clinical decision support, where data availability and methodological maturity are highest. In contrast, AI-based approaches for behaviour and personality prediction remain limited, particularly in cats and exotic companion animals, largely due to small, heterogeneous datasets, potential bias, and a lack of external validation. Emerging technologies such as wearable sensors, computer vision, and multimodal data integration demonstrate substantial potential for continuous behavioural monitoring and early detection of welfare-related issues in real household environments. Nevertheless, significant challenges persist, including data heterogeneity, limited model explainability, ethical considerations, and the absence of regulatory frameworks specifically addressing AI-based veterinary applications. Overall, this review highlights a substantial gap between the technical potential of AI and its current readiness for widespread application in companion animal behaviour and welfare assessment. Future research should prioritise large-scale and standardised data collection, cross-species validation, and interdisciplinary collaboration to ensure that AI-driven tools effectively support veterinary decision-making, animal welfare, and the well-being of owners. Full article

Transformer-based detectors have demonstrated exceptional accuracy in visible-object detection tasks. However, adversarial patches, specific types of adversarial examples, can disrupt these detectors by introducing unrestricted perturbations into specific image regions. Traditional methodologies focus on placing patches directly on objects and increasing attention scores between the patch and all areas of the image to impair detector performance. Nevertheless, these approaches are suboptimal due to significant discrepancies between background and object features, which contradict optimization objectives. Moreover, they overlook the impact of cross-attention mechanisms on detection results. To address these limitations, we introduce a novel approach named Localized Query Attack (LQA), designed to interfere with both self-attention within the encoder and cross-attention in the decoder. Unlike conventional global interference methods, LQA targets object features specifically, enhancing self-attention interactions between the adversarial patch and foreground regions to redirect model focus toward the patch. In the context of decoder cross-attention, we compute the joint attention matrix connecting encoder outputs with object queries. By diminishing the influence of encoder outputs and residual components in this matrix, we amplify the relative importance of the adversarial patch, thereby intensifying the attack’s effectiveness. Our experiments show that LQA achieves an approximately 20% improvement in transfer attack performance compared to the second-best method across various transformer-based detectors. The practical efficacy of LQA is further substantiated through real-world scenario validations, underscoring its applicability. Full article

Early risk stratification of COVID-19 severity in oncology patients is critical for improving clinical outcomes and optimizing hospital resource allocation. This study proposes a rule-based clinical decision support system (CDSS) designed for integration into digital triage workflows. In practical terms, the score is intended to be applied at hospital admission or triage, where demographic and comorbidity information is routinely available. The computed score can automatically flag high-risk oncology patients for intensified monitoring or early ICU evaluation, supporting rapid resource allocation while preserving clinician decision-making. Using retrospective clinical data from hospitalized oncological patients with confirmed SARS-CoV-2 infection, we developed a scoring algorithm based on four common comorbidities: age ≥ 70, obesity, diabetes mellitus, and hypertension. Each factor was assigned a weighted contribution to a cumulative score ranging from 0 to 7. Patients were classified into three risk levels (low, moderate, high), correlating with observed rates of ICU admission and mortality. The system is built for low-complexity implementation in electronic health records (EHRs) or web-based triage dashboards and includes a software logic model with pseudocode. Results indicate that the score effectively distinguishes patient risk levels with statistical significance (p < 0.01), and can function as an early triage mechanism. The proposed model does not require laboratory data or imaging, making it particularly suitable for rapid deployment in both hospital and remote settings. This work demonstrates a pragmatic, interpretable, and scalable approach to clinical decision support in pandemic contexts involving vulnerable populations such as cancer patients. Full article

Background: Emotion regulation refers to the processes through which individuals influence their emotional experiences, including how emotions are generated, experienced, and expressed. Difficulties in emotion regulation have been identified as a relevant factor in the development and maintenance of Eating Disorders (EDs). In the sports context, high physical and performance demands may intensify emotional challenges, potentially increasing vulnerability to eating disorder symptomatology among athletes. Objectives: This systematic review aimed to examine the relationship between emotion regulation and EDs in athletic populations, with a particular focus on emotion regulation strategies and related emotional processes. Methods: The PICO model was used, and PRISMA guidelines were followed. The Redalyc, Dialnet, SpringerLink, and PubMed databases were searched from inception to April 2025, with an update in November 2025. After the selection process, nine studies involving athletes from different disciplines and competitive levels were included. Methodological quality and risk of bias were assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklists. Results: The findings indicate that adaptive emotion regulation strategies, such as Cognitive Reappraisal and emotional identification, are associated with lower levels of eating disorder symptomatology, body dissatisfaction, and greater resilience to sport-related pressures. In contrast, dysfunctional strategies, including expressive suppression, emotional unawareness, and difficulties in emotion management, were consistently associated with restrictive eating behaviors, bulimic symptomatology, excessive weight control, and increased ED risk. Additional emotional factors, including anxiety, perfectionism, low self-esteem, and body image dissatisfaction, were also related to higher vulnerability to EDs, particularly in sports with high aesthetic or weight-related demands. Conclusions: Emotional regulation is closely associated with ED risk in athletes. Adaptive emotion regulation strategies may serve as protective factors, whereas dysfunctional strategies are associated with increased risk. Full article

Rapid agricultural expansion threatens water security in one of the world’s largest hydroelectric systems, the Itaipu dam, located on the Brazil–Paraguay border. Yet regional hydrological responses to land cover change and climate variability remain insufficiently characterized at management-relevant scales. The Upper Paraná River Basin (UPRB), which sustains agriculture, hydropower, and municipal water supply across both countries, exemplifies this challenge as accelerating cropland conversion raises concerns about long-term water availability. This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. We integrate GRACE/GRACE-FO (Gravity Recovery and Climate Experiment Follow-On), Terrestrial Water Storage Anomalies (TWSAs), MODIS (Moderate Resolution Imaging Spectroradiometer) land cover, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, and LandScan population density using Pettitt’s breakpoint test and Mann–Kendall trend analysis to detect temporal breakpoints and quantify co-variability between hydrology and land surface dynamics. Together, these methods identify a significant basin-wide shift in TWSAs in mid-2009, with storage increases of 151.6 cm at Itaipu and 103.1 cm at Yguazú Reservoir. Over the study period, cropland expanded from 13.5% to 37.9% of total land cover, while savanna declined from 28.1% to 24.2%. After 2009, correlations between land cover and TWSAs strengthened substantially, particularly for wetlands (r = 0.88), croplands (r = 0.73), and savannas (r = −0.81; all p < 0.001), indicating strong coupling between landscape transformation and basin-scale storage variability. Principal Component Analysis shows land use change explains 39–41% of TWSA variance, exceeding hydroclimatic contributions. Granger causality analysis reveals bidirectional coupling between wetlands and water storage at Itaipu, while cropland and savanna dynamics exert predictive influence on downstream hydrology in the Yguazú basin. Water balance decomposition further indicates a post-2009 regime shift, with residual storage transitioning from −10.6 to +4.7 and 78% greater runoff generation per unit precipitation, consistent with reduced infiltration capacity. Together, these findings underscore intensifying land–water feedback and the need for adaptive watershed management under expanding agriculture and climate variability. Full article

The widespread implementation of population-based mammographic screening has markedly increased the detection of ductal carcinoma in situ (DCIS), without a proportional reduction in breast cancer-specific mortality. This divergence has intensified concerns regarding overdiagnosis and overtreatment and has prompted increasing interest in treatment de-escalation and active surveillance strategies. Breast imaging remains indispensable for DCIS detection, extent assessment, and longitudinal monitoring. However, although imaging features correlate with histopathologic risk factors at the population level, their ability to predict individual biological progression is inherently probabilistic and limited. Overinterpretation of imaging phenotypes as surrogates of invasive destiny risks inappropriate reassurance or unjustified therapeutic escalation, particularly in the context of high-sensitivity modalities that may overestimate disease extent or trigger additional interventions without proven outcome benefits. This review examines the modality-specific roles of mammography, ultrasound, breast magnetic resonance imaging (MRI), contrast-enhanced mammography (CEM), and emerging artificial intelligence (AI) approaches within contemporary DCIS management, with particular attention to their implementation in active surveillance trials such as LORIS, COMET, LORD, and LORETTA. Across modalities, imaging primarily reflects lesion morphology, spatial distribution, and vascular behaviour, and functions most reliably as a risk-filtering and safety-gating instrument aimed at excluding radiologically unsafe scenarios, including occult invasion, underestimated disease extent, or imaging evolution incompatible with continued observation. By delineating both the capabilities and the epistemological limits of imaging, this review proposes a structured clinical decision framework in which imaging supports—but does not independently determine—risk-adapted management. Disciplined integration of imaging into multidisciplinary decision-making is essential to enable safe de-escalation, prevent false reassurance, and align DCIS care with patient-centred and value-based principles. Full article

Adiabatic shear banding (ASB) is a critical failure mechanism in titanium alloys subjected to high-strain-rate deformation, and its initiation is strongly influenced by the initial crystallographic texture. The dynamic response and ASB sensitivity of extruded and annealed Ti-6Al-4V (TC4) alloy rods were investigated under dynamic compression of cubic specimens along the extrusion direction (ED) and the transverse direction (TD) at a strain rate of 2500 s⁻¹. Split Hopkinson pressure bar (SHPB) tests combined with digital image correlation (DIC) were employed to obtain the stress–strain response and the evolution of strain localization. A dislocation density-based crystal plasticity finite element model (CPFEM), incorporating the measured texture, was established to elucidate the correlation between texture and ASB behavior. The experimental results show that TD specimens exhibit a yield strength approximately 100 MPa higher than that of ED specimens, while both orientations display comparable post-yield hardening behavior. ASB initiation occurs earlier in TD (compressive strain ~0.13) than in ED (~0.23), indicating greater ASB sensitivity in the TD orientation. The CPFEM successfully reproduces the directional stress–strain responses and the observed localization morphology, enabling mechanistic interpretation in terms of slip activity and thermomechanical coupling. The simulations indicate that ED loading is dominated by prismatic ⟨a⟩ slip, resulting in lower flow stress and more dispersed strain localization. In contrast, TD loading is governed primarily by pyramidal ⟨c + a⟩ slip, leading to elevated flow stress and intensified localization. The higher ASB sensitivity in the TD orientation is therefore attributed to texture-controlled slip-mode partitioning, enhanced thermomechanical coupling, and a more concentrated crystallographic orientation distribution that facilitates intergranular slip transfer. These findings provide guidance for tailoring microtexture to mitigate dynamic failure in titanium alloys subjected to high-strain-rate loading. Full article

Accurate non-invasive prediction of pathological complete response (pCR) following neoadjuvant chemotherapy (NACT) in breast cancer (BC) remains challenging despite its established prognostic significance. Objective: We aimed to evaluate the prognostic utility of baseline and post-NACT magnetic resonance imaging (MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for predicting pCR and survival outcomes, focusing on molecular subtype-specific performance and post-NACT imaging discordance. Methods: In this multicenter study, we retrospectively analyzed 335 patients with BC who received NACT between 2015 and 2025. Baseline (pre-NACT) and post-NACT imaging assessments were performed using MRI and ¹⁸F-FDG PET/CT. Pathological response was graded using the Miller–Payne classification system. Multivariable logistic regression was applied to identify independent predictors of pCR, whereas survival outcomes were examined using Kaplan–Meier analysis and Cox regression. Results: The overall pCR rate was 41.2%. Post-NACT imaging demonstrated complete response in 58.7% of patients by ¹⁸F-FDG PET/CT and 43.6% by MRI, both significantly correlating with pCR (p < 0.001). Pre-NACT MRI tumor size showed predictive value exclusively in Luminal A/B HER2-negative disease (area under curve = 0.681; p = 0.013). Importantly, post-NACT discordance between MRI and ¹⁸F-FDG PET/CT-based tumor size assessments was an independent predictor of both mortality (hazard ratio, 1.03) and disease progression (hazard ratio, 1.01). Conclusions: Post-NACT MRI and ¹⁸F-FDG PET/CT findings correlate strongly with pCR achievement, whereas pre-NACT MRI tumor size predicts pCR only in hormone receptor-positive HER2-negative subtypes. Importantly, post-NACT imaging discordance independently predicted mortality and disease progression, suggesting that dual-modality imaging assessment may identify high-risk patients requiring intensified surveillance. Full article

The increasing concern over heavy metal contamination in export crops has intensified research on the application of computer vision systems (CVS) and advanced sensing technologies within multi-level agricultural monitoring frameworks spanning soil contamination assessment, crop spectral diagnostics, and in situ elemental sensing. This study conducts a systematic literature review following Kitchenham’s methodology, from which 68 studies were finally included after screening and eligibility assessment. The review focuses on the use of hyperspectral imaging (HSI) and XRF-IoT sensors (X-ray fluorescence units enhanced with IoT connectivity) for detecting heavy metals in export crops, considering publications from the last seven years indexed in Web of Science Core Collection, Scopus, IEEE Xplore, EBSCOhost, and Springer Nature Link. The findings indicate that research is concentrated in highly digitalized countries, which limits its global applicability; moreover, a substantial proportion of studies is published in Q1 journals, although the methodologies are not always fully objective. Likewise, the most developed research lines are oriented toward image-based diagnostics and crop analysis. These results reveal a gap between technological advances in computer vision and their integration into agricultural decision-making aimed at improving the quality of export crops. It is recommended to foster research with greater geographical diversity, grounded in solid theoretical frameworks and an ethical perspective. Full article

To investigate the dynamic instability mechanism of surrounding rock in deep, rockburst-prone coal seams, a Split Hopkinson Pressure Bar (SHPB) system was utilized to carry out dynamic impact compression tests on Rock–Coal–Rock (RCR) composites featuring four different seam dip angles, namely 0°, 15°, 30°, and 45°. We systematically analyze incorporating high-speed imaging, the mechanical properties, energy evolution, and progressive failure characteristics of the composites under various strain rates. The results indicate that the dynamic compressive strength and elastic modulus of the composites exhibit a significant strain-rate hardening effect. With the increase in the dip angle of the coal seam, the compressive strength of the specimen decreases accordingly. Specifically, the range of 15–30° is identified as a critical transition zone where the failure mode shifts from matrix-dominated bearing to interfacial slip instability. At an impact pressure of 0.12 MPa, the compressive strength drops by 36.9% within this interval. Furthermore, the energy distribution is profoundly modulated by the geometric characteristics of the interface. As the dip angle increases, the degree of wave impedance mismatch at the coal–rock interface intensifies, leading to a sharp rise in the reflected energy ratio (up to 80.7%) and a pronounced attenuation of transmitted energy. Notably, the dissipation energy per unit volume increases with the dip angle, revealing that interfacial sliding and frictional work become the primary energy dissipation pathways under large-inclination conditions. High-speed camera monitoring confirms that the instability mechanism shifts from axial splitting/tension to an interfacial shear-slip mode as the dip angle increases. These findings provide a scientific reference for the stability evaluation of roadway surrounding rock and the prevention of dynamic disasters. Full article

Integrating diamond with GaN provides an effective pathway to mitigate self-heating. However, the thermal boundary resistance (TBR) remains a persistent bottleneck for further heat dissipation. While carbon (C) diffusion into the SiNx interlayer is known to reduce TBR, the associated stress evolution and its impact on device performance remain underexplored. In this work, the synergistic regulation of heat transport and electrical performance induced by C diffusion was systematically investigated. Transmission electron microscopy (TEM) was employed to characterize the interfacial microstructure and the influence of C diffusion on the interface. To further assess the resulting impact on heat dissipation, transient thermoreflectance was utilized to precisely quantify the thermal transport within the heterostructures. Classical molecular dynamics (MD) simulations were then performed to analyze the underlying physical mechanisms, revealing that intensifying C diffusion increases the phonon density of states overlap and effectively reduces the TBR. Furthermore, the intrinsic stress was quantified through geometric phase analysis (GPA) based on TEM images, demonstrating that the stress induced during the diffusion process propagates to the AlGaN/GaN heterostructure. Crucially, this stress modulation enhances the piezoelectric polarization by approximately 32%, resulting in a 5% increase in the two-dimensional electron gas (2DEG) sheet density. These findings provide a comprehensive strategy for optimizing the thermal management and mechanical reliability of high-power GaN devices. Full article

The combined effects of global warming and urbanisation have intensified the urban heat island (UHI) phenomenon and thermal stress, especially in the summer season. This study develops an integrated multi-scale framework to quantify the sustainability in terms of the thermal performance of high albedo interlocking concrete block pavement (ICBP) in the city of Turin, Italy. The framework combines: (1) experimental campaigns to establish baseline albedo values, using an albedometer (in accordance with the standard ASTM E1918-21 and E1980-24); (2) in situ measurements to assess the performance of ICBP in three parking areas; (3) satellite analysis using Landsat 8-9 and Sentinel-2 images to derive the land surface temperature (LST) and quantify changes in the surface urban heat island intensity (SUHII). In situ measurements showed an average albedo of 0.20 for ICBP, lower values for aged surfaces and about 0.08 for asphalt. Satellite analysis confirmed the effectiveness of the substitution of asphalt surface pavements with ICBP, revealing an increase of over 30% in both the average albedo and the solar reflectance index (SRI). These results are also combined with the 15% decrease in SUHII. Combining on-site measurements and satellite analysis provides a comprehensive framework for quantifying surface urban heat island effects and thermal performances of more sustainable road pavements. These findings support high albedo ICBP as an effective strategy for UHI mitigation. Full article