Search Results (3,133)

Accurate aircraft detection in remote sensing imagery is critical for aerospace surveillance, military reconnaissance, and aviation security but remains fundamentally challenged by extreme scale variations, arbitrary orientations, and dense spatial clustering in high-resolution scenes. This paper presents an adaptive attention-guided super-resolution network that integrates multi-scale feature learning with saliency-aware processing to address these challenges. Our architecture introduces three key innovations: (1) A hierarchical coarse-to-fine detection pipeline that first identifies potential regions in downsampled imagery before applying precision refinement, (2) A saliency-aware tile selection module employing learnable attention tokens to dynamically localize aircraft-dense regions without manual thresholds, and (3) A local tile refinement network combining transformer-based super-resolution for target regions with efficient upsampling for background areas. Extensive experiments on DIOR and FAIR1M benchmarks demonstrate state-of-the-art performance, achieving 93.1% AP₅₀ (DIOR) and 83.2% AP₅₀ (FAIR1M), significantly outperforming existing super-resolution-enhanced detectors. The proposed framework offers an adaptive sensing solution for satellite-based aircraft detection, effectively mitigating scale variations and background clutter in real-world operational environments. Full article

Organophosphate pesticides are among the most persistent and toxic contaminants in aquatic environments, requiring effective strategies for detection and remediation. In this work, density functional theory (DFT) calculations were employed to investigate the adsorption of nine representative organophosphates (glyphosate, malathion, diazinon, azinphos-methyl, fenitrothion, parathion-methyl, disulfoton, tokuthion, and ethoprophos) on mercaptobenzothiazole disulfide (MBTS) and MBTS-functionalized graphene (G–MBTS). All simulations were performed in aqueous solution using the SMD solvation model with dispersion corrections and counterpoise correction for basis set superposition error. MBTS alone displayed a range of affinities, suggesting potential selectivity across the organophosphates, with adsorption energies ranging from 0.27 to 1.05 eV, malathion being the strongest binder and glyphosate the weakest. Anchoring of MBTS to graphene was found to be highly favorable (1.26 eV), but the key advantage is producing stable adsorption platforms that promote planar orientations and $\pi$–$\pi$/dispersive interactions. But the key advantage is not stronger binding but the tuning of interfacial electronic properties: all G–MBTS–OP complexes show uniform, narrow HOMO-LUMO gaps (∼0.79 eV) and systematically larger charge redistribution. These features are expected to enhance electrochemical readout even when adsorption strength was comparable or slightly lower (0.47–0.88 eV) relative to MBTS alone. A Quantum Theory of Atoms in Molecules (QTAIM) analysis of the G–MBTS–malathion complex revealed a dual stabilization mechanism: multiple weak C–H$\dots \pi$ interactions with graphene combined with stronger S…O and hydrogen-bonding interactions with MBTS. These results advance the molecular-level understanding of pesticide–surface interactions and highlight MBTS-functionalized graphene as a promising platform for the selective detection of organophosphates in water. Full article

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care. Full article

To alleviate strong strata-pressure bursts during ultra-thick coal extraction, we selected the 26 m number five seam of the Chenjiagou Coal Mine as a full-scale prototype. Three objectives were pursued: (1) elucidate the initiation mechanism of high-energy roof failures under top-coal caving (TCC); (2) quantitatively link the failure sequence of key strata to burst intensity; and (3) deliver field-oriented prevention criteria. A 1:300 physical similarity model and UDEC plane-strain simulations were combined to monitor roof deformation, stress evolution and dynamic response during extraction. Results demonstrate that pressure bursts are driven by abrupt kinematics of the overburden, triggered by sequential breakage of key horizons: the secondary key stratum collapsed at 130 m face advance, followed by the main-key stratum at 360 m. Their combined rupture generated a violent energy release, with roof displacement accelerating markedly after the main horizon failed. We therefore propose two dimensionless indices—the dynamic load factor (DLF) and stress concentration factor (SCF)—to characterize burst severity; peak values reached 1.62 and 2.43, respectively, while pronounced stress accumulation was localized 6–15 m ahead of the face. These metrics furnish a theoretical basis for early warning systems and control strategies aimed at intense rock burst. Full article

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance ($R^2$ = 0.85 for milk yield and $R^2$ = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH₃) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact. Full article

This work presents the design and prototyping of two reconfigurable BLE-based devices developed to overcome the limitations of commercial platforms in terms of configurability, data transparency, and energy efficiency. The first is a wearable smart wristband integrating inertial and biometric sensors, while the second is a configurable beacon (ASIA Beacon) able to dynamically adjust key transmission parameters such as channel selection and power level. Both devices were engineered with energy-aware components, OTA update support, and flexible 3D-printed enclosures optimized for residential environments. The firmware, developed under Zephyr RTOS, exposes data through standardized interfaces (GATT, MQTT), facilitating their integration into IoT architectures and research-oriented testbeds. Initial experiments carried out in an anechoic chamber demonstrated improved RSSI stability, extended autonomy (up to 4 months for beacons and 3 weeks for the wristband), and reliable real-time data exchange. These results highlight the feasibility and potential of the proposed devices for future deployment in ambient assisted living environments, while the focus of this work remains on the hardware and software development process and its validation. Full article

Wide-bandgap (WBG) perovskite solar cells (PSCs) are critical for high-efficiency tandem photovoltaic devices, but their practical application is severely limited by phase separation and poor film quality. To address these challenges, this study proposes a dual-additive passivation strategy using potassium thiocyanate (KSCN) and potassium chloride (KCl) to synergistically optimize the crystallinity and defect state of WBG perovskite films. The selection of KSCN/KCl is based on their complementary functionalities: K⁺ ions occupy lattice vacancies to suppress ion migration, Cl⁻ ions promote oriented crystal growth, and SCN⁻ ions passivate surface defects via Lewis acid-base interactions. A series of KSCN/KCl concentrations (relative to Pb) were tested, and the effects of dual additives on film properties and device performance were systematically characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), space-charge-limited current (SCLC), current-voltage (J-V), and external quantum efficiency (EQE) measurements. Results show that the dual additives significantly enhance film crystallinity (average grain size increased by 27.0% vs. control), reduce surface roughness (from 86.50 nm to 24.06 nm), and passivate defects-suppressing non-radiative recombination and increasing electrical conductivity. For WBG PSCs, the champion device with KSCN (0.5 mol%) + KCl (1 mol%) exhibits a power conversion efficiency (PCE) of 16.85%, representing a 19.4% improvement over the control (14.11%), along with enhanced open-circuit voltage (Voc: +2.8%), short-circuit current density (Jsc: +6.7%), and fill factor (FF: +8.9%). Maximum power point (MPP) tracking confirms superior operational stability under illumination. This dual-inorganic-additive strategy provides a generalizable approach for the rational design of stable, high-efficiency WBG perovskite films. Full article

Post-traumatic stress disorder (PTSD) is associated with long-term disturbances in stress regulation, neuroinflammation, and oxidative stress and reduced psychological coping capacity. The aim of the study was to assess the relationship between selected neurobiological biomarkers (Insulin-like Growth Factor 1—IGF-1; Caspase-9—CASP-9; Neuronal Nitric Oxide Synthase—nNOS; and Interleukin-10—IL-10) and coping styles evaluated using the Brief Coping Orientation to Problems Experienced (Brief-COPE) questionnaire in men with trauma experience. Particular emphasis was placed on analyzing the effect of PTSD chronicity (≤5 years vs. >5 years) on these relationships. The study included 92 adult men with a history of life-threatening situations. Participants were divided into three groups: PTSD within the past ≤5 years (n = 33), PTSD within the past >5 years (n = 31), and a No PTSD group (n = 28). Biomarkers were measured in blood serum. Coping strategies were assessed using the Brief-COPE questionnaire, which includes four subscales: task-oriented, emotion-oriented, avoidant, and general coping. Due to the lack of normal distribution, the Kruskal–Wallis test and Dunn’s post hoc test were used. Correlations between biomarkers and Brief-COPE subscales were calculated using Spearman’s Rank Correlation Coefficient (Rho). Significant differences between groups were found in all four biomarkers (p < 0.001). IGF-1 and IL-10 reached the highest values in the No PTSD group and the lowest in the PTSD ≤ 5 years group, indicating neuroprotective and anti-inflammatory deficits in PTSD. Conversely, CASP-9 and nNOS levels (markers of apoptosis and oxidative stress) were highest in PTSD ≤ 5 years, with partial normalization in the PTSD > 5 years group. In terms of coping strategies, the No PTSD group displayed a highly adaptive profile (task-oriented: 30/32; emotion-oriented: 43/48; and avoidant: 12/32). Individuals with PTSD ≤ 5 years presented a maladaptive pattern (task-oriented: 13/32; avoidant: 26/32; and emotion-oriented: 27/48), while in PTSD > 5 years, a further decline in emotion-oriented (21/48) and general coping (59/112) was observed, suggesting progressive depletion of psychological resources. The strongest correlations between biomarkers and coping strategies occurred in PTSD groups. Low IGF-1 levels in PTSD ≤ 5 years correlated negatively with emotion-oriented coping (Rho = −0.39) and general coping (Rho = −0.35). High CASP-9 levels were associated with reduced task-oriented coping in PTSD > 5 years (Rho = −0.29). Similar trends were observed for nNOS and IL-10, indicating a disturbance in neurobiological balance that favors persistence of PTSD symptoms. PTSD, both in its acute and chronic phases, is associated with an abnormal profile of neuroprotective, apoptotic, and inflammatory biomarkers, which correlates with impaired adaptive coping capacity. Although partial normalization of biological parameters is observed in chronic PTSD, deficits in emotion-oriented and task-oriented coping persist. The Brief-COPE questionnaire, combined with biomarker analysis, may serve as a useful clinical tool for assessing psychophysiological balance and designing early interventions. These results highlight the potential of IGF-1, CASP-9, nNOS, and IL-10 as biomarkers of stress adaptation and therapeutic targets in PTSD. Full article

This work presents a microstructural analysis and X-ray diffraction (XRD) investigation of the plastic deformation in commercially pure, single-phase hexagonal close-packed (hcp) Zn subjected to rolling and tensile tests up to failure. Samples were examined by optical microscope and XRD; hardness was assessed by Vickers microhardness. High-resolution diffraction profiles with Kα1/Kα2 deconvolution were used to identify deformation-induced texture and to estimate the dislocation density. Results show that rolling (40% thickness reduction) and tensile test change texture and cause peak shifts and broadening, with corresponding microstructural changes. Microhardness changes from 28–45 HV (annealed) to 30–50 HV after deformation. After rolling, the texture (002) is the most intense reflection and (004) increases without significant angular shifts. Tensile tests induce low-angle shifts of (101) and (004), as well as selective texture changes (appearance of (103) and (110)). The (101) full width at half maximum increases from β(2θ) = 0.115° (annealed) to 0.160° (rolled) and 0.140° (after tensile test), yielding dislocation densities from 2.73 × 10⁶ cm⁻² (annealed) to 3.03 × 10¹¹ cm⁻² (rolled) and 3.38 × 10¹⁰ cm⁻² (after tensile test). Finally, this study quantifies the XRD parameters (full width at half maximum, angular shifts and dislocation density). Plastic deformation of pure Zn leads to significant microstructural changes, including grain refinement, the generation of dislocations, and the formation of new crystallographic orientations, which are then observable in XRD patterns as peak broadening, shifts, and texture development. The severity of these effects depends on the level of deformation. Full article

The growing implementation of close-to-nature forestry practices in the management of northern forests, characterized by dispersed harvesting operations, has heightened the importance of minimizing damage to residual stands as a key aspect of sustainable forest management. The objective of this study is to examine and compare the resistance of various tree species and diameter classes to wounds incurred during logging operations of differing sizes, intensities, and locations. In addition, the research aims to assess temporal changes in wound characteristics, including healing and closure processes, across species. This long-term, 18-year investigation was conducted in the Kheyrud Forest, located within the Hyrcanian broadleaf forest region of northern Iran, to evaluate the dynamics of wound healing in residual trees following ground-based skidding operations. Through a comprehensive assessment of 272 wounded trees across six species, we demonstrate that species significantly influences healing ratio (Kruskal–Wallis, p < 0.01), with Oriental beech (Fagus orientalis Lipsky) (50.6%) showing superior recovery compared to the Chestnut-leaved oak (Quercus castaneifolia) (37.5%). Healing ratio decreased with larger diameter at breast height (DBH) (R² = 0.114, p < 0.01), while absolute healed area increased. Larger areas (>1000 cm²) reduced healing by 42.3% versus small wounds (<500 cm²) (R² = 0.417, p < 0.01). Severe wounds (deep gouges) showed 19% less healing than superficial injuries (p = 0.003). Circular wounds healed significantly better than rectangular forms (χ² = 24.92, p < 0.001). Healing ratio accelerated after the first decade, reaching 69% by year 17 (R² = 0.469, p < 0.01). Wound height (p = 0.117) and traffic intensity (p = 0.65) showed no statistical impact. Contrary to expectations, stem position had no significant effect on wound recovery, whereas wound geometry proved to be a critical determinant. The findings highlight that appropriate species selection, minimizing wound size (to less than 500 cm²), and adopting extended cutting cycles (exceeding 15 years) are essential for enhancing residual stand recovery in close-to-nature forestry systems. Full article

The deployment of autonomous robots is critical for advancing sustainable agriculture, but their effectiveness hinges on visual perception systems that can reliably operate in natural, real-world environments. Selecting an appropriate vision model for these robots requires a practical evaluation that extends beyond standard accuracy metrics to include critical deployment factors such as computational efficiency, energy consumption, and robustness to environmental disturbances. To address this need, this study presents a deployment-oriented benchmark of state-of-the-art You Look Only Once (YOLO)-based models for orange detection and segmentation. Following a systematic process, the selected models were evaluated on a unified public dataset, annotated to rigorously assess real-world challenges. Performance was compared across five key dimensions: (i) identification accurac, (ii) robustness, (iii) model complexity, (iv) execution time, and (v) energy consump-tion. The results show that the YOLOv5 variants achieved the most accurate detection and segmentation. Notably, YOLO11-based models demonstrated strong and consistent results under all disturbance levels, highlighting their robustness. Lightweight architectures proved well-suited for resource-constrained operations. Interestingly, custom models did not consistently outperform their baselines, while nanoscale models showed demonstra-ble potential for meeting real-time and energy-efficient requirements. These findings offer valuable, evidence-based guidelines for the vision systems of precision agriculture robots. Full article

Background: Diabetic ketoacidosis (DKA) in patients with kidney failure receiving dialysis presents a formidable clinical challenge. Standard DKA protocols, designed for patients with preserved renal function, often fail in this cohort and can be unsafe when applied without modification. Patients are at risk of iatrogenic fluid overload, dyskalaemia, and hypoglycaemia due to altered insulin kinetics, impaired gluconeogenesis, and the absence of osmotic diuresis. Purpose: This narrative review aims to synthesise current understanding of DKA pathophysiology in dialysis patients, delineate distinct clinical phenotypes, and propose individualised management strategies grounded in physiology-based reasoning, comparative guideline insights, and consensus-supported literature. Methods: We searched PubMed/MEDLINE, Embase, and Google Scholar (January 2004–June 2024) for adult dialysis populations, using terms spanning DKA, kidney failure, insulin kinetics, fluid balance, and cerebral oedema. Reviews, observational cohorts, guidelines, consensus statements, and physiology papers were prioritised; case reports were used selectively for illustration. Evidence was weighted by physiological plausibility and practice relevance. Nephrology-led authors aimed for a pragmatic, safety-first synthesis, seeking and integrating contradictory recommendations. Conclusions: Our findings highlight the critical need for a nuanced approach to fluid management, a tailored insulin strategy that accounts for glucose-insulin decoupling and prolonged insulin half-life, and careful consideration of potassium and acidosis correction. We emphasise the importance of recognising specific volume phenotypes (hypovolaemic, euvolaemic, hypervolaemic) to guide fluid therapy, and advocating the judicious use of variable-rate insulin infusions (‘dry insulin’) to mitigate fluid overload. We also show that service-level factors are critical. Dialysis-specific pathways, interdisciplinary training, and quality improvement metrics can reduce iatrogenic harm. By linking physiology with workflow adaptations, this review provides a physiologically sound, bedside-oriented map for navigating this complex emergency safely and effectively. In doing so, it advances an individualised model of DKA care for dialysis-dependent patients. Full article

Industrial safety in high-risk sectors such as mining, construction, oil and gas, petrochemicals, and offshore fishing remains a strategic global challenge due to the high incidence of occupational accidents and their human, financial, and legal consequences. Despite international standards and advancements in safety strategies, significant barriers persist in the effective implementation of a Zero Accident culture. This scoping review, conducted under PRISMA-ScR guidelines, analyzed 11 studies selected from 232 records, focusing on documented practices in both multinational corporations from developed economies and local companies in emerging markets. The methodological synthesis validated theoretical models, practical interventions, and regulatory frameworks across diverse industrial settings. The findings led to the construction of a five-pillar model that provides the structural foundation for a comprehensive safety strategy: (1) strategic safety planning, defining long-term vision, mission, and objectives with systematic risk analysis; (2) executive leadership and commitment, expressed through decision-making, resource allocation, and on-site engagement; (3) people and competencies, emphasizing continuous training, communities of practice, and the development of safe behaviors; (4) process risk management, using validated protocols, structured methodologies, and early warning systems; and (5) performance measurement and auditing, combining reactive and proactive indicators within continuous improvement cycles. The results demonstrate that only a holistic approach, one that aligns strategy, culture, and performance, can sustain a robust safety culture. While notable reductions in incident rates were observed when these pillars were applied, the current literature is dominated by theoretical contributions and model replication from developed countries, with limited empirical evaluation in emerging contexts. This study provides a comparative, practice-oriented framework to guide the implementation and refinement of safety systems in high-risk organizations. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/XFDPR. Full article

The zygomaticus major and minor (ZMa/ZMi) are key determinants of smile dynamics and midface contour, yet they exhibit substantial morphological variability—including bifid or multibellied bellies, accessory slips, and atypical insertions. Such variants can alter force vectors, fat-compartment boundaries, and SMAS planes, increasing the risk of asymmetry, contour irregularities, or “joker smile” following facelifts, fillers, thread lifts, and smile reconstruction. To our knowledge, this is the first review to integrate the Landfald classification of ZMa/ZMi variants with a standardized dynamic imaging-based workflow for aesthetic and reconstructive midface procedures. We conducted a narrative literature synthesis of anatomical and imaging studies. Bifid or multibellied variants have been reported in up to 35% of cadaveric specimens. We synthesize anatomical, biomechanical, and imaging evidence (MRI, dynamic US, 3D analysis) to propose a practical protocol: (1) focused history and dynamic examination, (2) US/EMG mapping of contraction vectors, (3) optional high-resolution MRI for complex cases, and (4) individualized adjustment of surgical vectors, injection planes, and dosing. Procedure-specific adaptations are outlined for deep-plane releases, thread-lift trajectories, filler depth selection, and muscle-transfer orientation. We emphasize that standardizing preoperative dynamic mapping and adopting a “patient-specific mimetic profile” can enhance safety, predictability, and preservation of authentic expression, ultimately improving patient satisfaction across diverse midface interventions. Full article

Directed self-assembly (DSA) of polystyrene-block-poly (methyl methacrylate) (PS-b-PMMA) has garnered substantial interest for semiconductor manufacturing, particularly for fabricating contact holes and vias. However, its application is limited by the low etch selectivity between the PS and PMMA domains. Here, we report an acid-responsive block copolymer, PS-N=CH-PMMA, incorporating a Schiff base (-N=CH-) linkage between the two blocks to impart acid sensitivity. The copolymer is synthesized via aldehyde-terminated PMMA (PMMA-CHO) precursors and is fully compatible with conventional thermal annealing workflows used for PS-b-PMMA. Uniform thin films with vertically oriented cylindrical domains were obtained, which could be directly converted into high-fidelity PS masks through acetic acid immersion without UV exposure. Graphoepitaxial DSA in 193i pre-patterned templates produced shrink-hole patterns with reduced critical dimension (CD) and improved local CD uniformity (LCDU). The shrink-hole CD was tunable by varying PMMA-CHO molecular weights. XPS confirmed selective cleavage of Schiff base linkages at the PS/PMMA interface under acidic conditions, while Ohta–Kawasaki simulations indicated interfacial wetting asymmetry governs etch fidelity and residual layer formation. Pattern transfer into TEOS layers was achieved with minimal CD loss. Overall, the acid-cleavable BCP enables scalable, high-fidelity nanopatterning with improved etch contrast, tunable process windows, and seamless integration into existing PS-b-PMMA lithography platforms. Full article