Search Results (196)

Background and Objectives: Coronavirus Disease 2019 (COVID-19) may cause extensive multi-organ pathology, particularly in the lungs, heart, kidneys, and liver. While hypercoagulability—often signaled by elevated D-dimer—has been thoroughly investigated, the concurrent pathological findings across organs and their interrelation with distinct D-dimer levels remain incompletely characterized. This study aimed to evaluate the pathological changes observed in autopsied or deceased COVID-19 patients, focusing on the prevalence of organ-specific lesions, and to perform subgroup analyses based on three D-dimer categories. Methods: We conducted a retrospective review of 69 COVID-19 patients from a Romanian-language dataset, translating all clinical and pathological descriptions into English. Pathological findings (pulmonary microthrombi, bronchopneumonia, myocardial fibrosis, hepatic steatosis, and renal tubular necrosis) were cataloged. Patients were grouped into three categories by admission D-dimer: <500 ng/mL, 500–2000 ng/mL, and ≥2000 ng/mL. Laboratory parameters (C-reactive protein, fibrinogen, and erythrocyte sedimentation rate) and clinical outcomes (intensive care unit [ICU] admission, mechanical ventilation, and mortality) were also recorded. Intergroup comparisons were performed with chi-square tests for categorical data and one-way ANOVA or the Kruskal–Wallis test for continuous data. Results: Marked organ pathology was significantly more frequent in the highest D-dimer group (≥2000 ng/mL). Pulmonary microthrombi and bronchopneumonia increased stepwise across ascending D-dimer strata (p < 0.05). Myocardial and renal lesions similarly showed higher prevalence in patients with elevated D-dimer. Correlation analysis revealed that severe lung and heart pathologies were strongly associated with high inflammatory markers and a greater risk of ICU admission and mortality. Conclusions: Our findings underscore that COVID-19-related organ damage is magnified in patients with significantly elevated D-dimer. By integrating pathology reports with clinical and laboratory data, we highlight the prognostic role of hypercoagulability and systemic inflammation in the pathogenesis of multi-organ complications. Stratifying patients by D-dimer may inform more tailored management strategies, particularly in those at highest risk of severe pathology and adverse clinical outcomes. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

We inspected a sector of the Apennines (central–southern Italy) in geographic and structural continuity with the Quaternary-active extensional belt but where clear geomorphic and seismological signatures of normal faulting are unexpectedly missing. The evidence of active tectonics in this area, between Abruzzo and Molise, does not align with geodetic deformation data and the seismotectonic setting of the central Apennines. To investigate the apparent disconnection between active deformation and the absence of surface faulting in a sector where high lithologic erodibility and landslide susceptibility may hide its structural evidence, we combined multi-scale and multi-source data analyses encompassing morphometric analysis and remote sensing techniques. We utilised high-resolution topographic data to analyse the topographic pattern and investigate potential imbalances between tectonics and erosion. Additionally, we employed aerial-photo interpretation to examine the spatial distribution of morphological features and slope instabilities which are often linked to active faulting. To discern potential biases arising from non-tectonic (slope-related) signals, we analysed InSAR data in key sectors across the study area, including carbonate ridges and foredeep-derived Molise Units for comparison. The topographic analysis highlighted topographic disequilibrium conditions across the study area, and aerial-image interpretation revealed morphologic features offset by structural lineaments. The interferometric analysis confirmed a significant role of gravitational movements in denudating some fault planes while highlighting a clustered spatial pattern of hillslope instabilities. In this context, these instabilities can be considered a proxy for the control exerted by tectonic structures. All findings converge on the identification of an ~20 km long corridor, the Castel di Sangro–Rionero Sannitico alignment (CaS-RS), which exhibits varied evidence of deformation attributable to active normal faulting. The latter manifests through subtle and diffuse deformation controlled by a thick tectonic nappe made up of poorly cohesive lithologies. Overall, our findings suggest that the CaS-RS bridges the structural gap between the Mt Porrara–Mt Pizzalto–Mt Rotella and North Matese fault systems, potentially accounting for some of the deformation recorded in the sector. Our approach contributes to bridging the information gap in this complex sector of the Apennines, offering original insights for future investigations and seismic hazard assessment in the region. Full article

Background: High-voltage electrical injuries (HVEIs) represent a complex and life-threatening entity, frequently involving multi-organ damage. While traditionally linked to occupational hazards, train surfing—riding on moving trains—and train climbing—scaling stationary carriages—have emerged as increasingly common causes among adolescents. Popularized via social media, these behaviors expose individuals to the invisible danger of electric arcs from 15,000-volt railway lines, often resulting in extensive burns, cardiac complications, and severe trauma. This study presents a 30-year retrospective analysis comparing cardiac biomarkers and clinical outcomes in train-surfing injuries versus work-related HVEIs. Methods: All patients with confirmed high-voltage injury (≥1000 volts) admitted to a Level 1 burn center between 1994 and 2024 were retrospectively analyzed. Exclusion criteria comprised low-voltage trauma, suicide, incomplete records, and external treatment. Clinical and laboratory parameters—including total body surface area (TBSA), Abbreviated Burn Severity Index (ABSI), electrocardiogram (ECG) findings, intensive care unit (ICU) and hospital stay, mortality, and cardiac biomarkers (creatine kinase [CK], CK-MB, lactate dehydrogenase [LDH], aspartate transaminase [AST], troponin, and myoglobin)—were compared between the two cohorts. Results: Of 81 patients, 24 sustained train-surfing injuries and 57 were injured in occupational settings. Train surfers were significantly younger (mean 16.7 vs. 35.2 years, p = 0.008), presented with greater TBSA (49.9% vs. 17.9%, p = 0.008), higher ABSI scores (7.3 vs. 5.1, p = 0.008), longer ICU stays (53 vs. 17 days, p = 0.008), and higher mortality (20.8% vs. 3.5%). ECG abnormalities were observed in 51% of all cases, without significant group differences. However, all cardiac biomarkers were significantly elevated in train-surfing injuries at both 72 h and 10 days post-injury (p < 0.05), suggesting more pronounced cardiac and muscular damage. Conclusions: Train-surfing-related high-voltage injuries are associated with markedly more severe systemic and cardiac complications than occupational HVEIs. The significant biomarker elevation and critical care demands highlight the urgent need for targeted prevention, public awareness, and early cardiac monitoring in this high-risk adolescent population. Full article

Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in the western United States have experienced reduced fire frequency since Euro-American settlement, usually because of successful fire suppression policies and even without such human impacts at remote sites in the Great Basin and Mojave Deserts. In an effort to improve our understanding of long-term environmental dynamics in sky-island ecosystems, we developed tree-ring chronologies from ponderosa pines located in the Sheep Mountain Range of southern Nevada, inside the Desert National Wildlife Refuge (DNWR). After comparing those dendrochronological records with other ones available for the south-central Great Basin, we analyzed their climatic response using station-recorded monthly precipitation and air temperature data from 1950 to 2024. The main climatic signal was December through May total precipitation, which was then reconstructed at annual resolution over the past five centuries, from 1490 to 2011 CE. The mean episode duration was 2.6 years, and the maximum drought duration was 11 years (1924–1934; the “Dust Bowl” period), while the longest episode, 19 years (1905–1923), is known throughout North America as the “early 1900s pluvial”. By quantifying multi-annual dry and wet episodes, the period since DNWR establishment was placed in a long-term dendroclimatic framework, allowing us to estimate the potential drought resilience of its unique, tree-dominated environments. Full article

The growing interest in alternative fuels stems from the need to reduce greenhouse gas emissions and promote sustainable development. Despite the dominance of fossil fuels in aviation, pulsejet engines offer a promising platform for testing new fuels due to their simple design and fuel versatility. This study presents a multi-criteria analysis of alternative fuels for use in pulsejet engines, emphasizing environmental impacts. Both gaseous (biogas, ethyne, LPG, and natural gas) and liquid fuels (methanol, ethanol, biodiesel, Jet A-1, and SAF) were examined. Exhaust emissions (CO₂, H₂O, CO) were simulated in Ansys 2025 based on literature data and chemical calculations. Additional factors analyzed included calorific value, production cost, thermal expansion, density, life cycle emissions (LCA), CO₂ emissions per fuel mass, and renewable energy content. Using the zero-unitization method, results were normalized into a single aggregate variable for each fuel. The highest values were recorded for biogas and methanol, respectively, indicating their potential as alternative fuels. The findings support further development of sustainable fuels for pulsejet engines. Future research should address combustion optimization and noise reduction, enhancing viability in aviation and other transport sectors. Integration with the current fuel infrastructure is also recommended to facilitate broader implementation. Full article

LiDAR point clouds, as direct carriers of 3D spatial information, comprehensively record the geometric features and spatial topological relationships of object surfaces, providing intelligent systems with rich 3D scene representation capability. However, current point cloud semantic segmentation methods primarily extract features through operations such as convolution and pooling, yet fail to adequately consider sparse features that significantly influence the final results of point cloud-based scene perception, resulting in insufficient feature representation capability. To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. In the context of this paper, sparse features refer to feature representations in which only a small number of activation units or channels exhibit significant responses during the forward pass of the model. First, a sparse feature regularization method was used to motivate the network model to learn the sparsified feature weight matrix. Next, a split edge convolution module, abbreviated as SEConv, was designed to extract the local features of the point cloud from multiple neighborhoods by dividing the input feature channels, and to effectively learn sparse features to avoid feature redundancy. Finally, a multi-neighborhood feature fusion strategy was developed that combines the attention mechanism to fuse the local features of different neighborhoods and obtain global features with fine-grained information. Taking S3DIS and ScanNet v2 datasets, we evaluated the feasibility and effectiveness of SFDGNet by comparing it with six typical semantic segmentation models. Compared with the benchmark model DGCNN, SFDGNet improved overall accuracy $\left(OA\right)$, mean accuracy $\left(mAcc\right)$, mean intersection over union $\left(mIoU\right)$, and $sparsity$ by $1.8\%$, $3.7\%, 3.5\%$, and $85.5\%$ on the S3DIS dataset, respectively. The $mIoU$ on the ScanNet v2 validation set, $mIoU$ on the test set, and $sparsity$ were improved by $3.2\%, 7.0\%$, and $54.5\%$, respectively. Full article

Sound source localization represents one of the major challenges for soundscapes due to the dynamicity of a large variety of signals. Many applications are found related to ecosystems to study the migration process of birds and animals other than other terrestrial environments to survey wildlife. Other applications on sound recording are supported by sensors to detect animal movement. This paper deals with the immersive 3D soundscape by using a multi-channel spherical microphone probe, in combination with a 360° camera. The soundscape has been carried out in three Italian squares across the city of Parma. The acoustic maps obtained from the data processing detect the directivity of dynamic sound sources as typical of an urban environment. The analysis of the objective environmental parameters (like loudness, roughness, sharpness, and prominence) was conducted alongside the investigations on the historical importance of Italian squares as places for social inclusivity. A dedicated listening playback is provided by the AGORA project with a portable listening room characterized by modular unit of soundbars. Full article

Badminton is an intermittent sport with a diverse exercise profile that stresses both aerobic and anaerobic energy systems. The aim of this study was to compare the internal and external load profiles of multi-feeding (MF) drills and simulated match play (SMP) in elite junior badminton players, and to explore potential sex-based differences. Forty-two players (24 males (age 17.4 ± 2.6 years, training experience 9.9 ± 1.8 years) and 18 females (age 16.9 ± 2.9 years, training experience 9.4 ± 2.1 years)) completed MF and SM sessions while external load (e.g., relative distance, explosive distance, relative jumps) and internal load (heart rate [HR], session rating of perceived exertion [sRPE]) variables were recorded using inertial measurement units and HR monitors. Two-way ANOVA revealed that MF induced significantly greater external (p < 0.05) and internal (p < 0.001) loads compared to SM, with large effect sizes. Male players showed markedly higher jump frequency (1.60 n/min vs. 0.80 n/min) and maximum speed (19.80 km/h vs. 15.80 km/h), although HR and sRPE values were similar between sexes (p > 0.05), suggesting that female athletes may experience greater relative physiological load. These findings highlight the importance of using MF drills to target specific conditioning goals and reinforce the need for individualized training strategies considering sex differences. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

The short-term scheduling of pumped-storage hydropower plants is characterised by high dimensionality and nonlinearity and is subject to multiple operational constraints. This study proposes an intelligent scheduling framework that integrates an Atomic Orbital Search (AOS)-optimised Long Short-Term Memory (LSTM) network with the Deep Deterministic Policy Gradient (DDPG) algorithm to minimise water consumption during the generation period while satisfying constraints such as system load and safety states. Firstly, the AOS-LSTM model simultaneously optimises the number of hidden neurons, batch size, and training epochs to achieve high-precision fitting of unit flow–efficiency characteristic curves, reducing the fitting error by more than 65.35% compared with traditional methods. Subsequently, the high-precision fitted curves are embedded into a Markov decision process to guide DDPG in performing constraint-aware load scheduling. Under a typical daily load scenario, the proposed scheduling framework achieves fast inference decisions within 1 s, reducing water consumption by 0.85%, 1.78%, and 2.36% compared to standard DDPG, Particle Swarm Optimisation, and Dynamic Programming methods, respectively. In addition, only two vibration-zone operations and two vibration-zone crossings are recorded, representing a reduction of more than 90% compared with the above two traditional optimisation methods, significantly improving scheduling safety and operational stability. The results validate the proposed method’s economic efficiency and reliability in high-dimensional, multi-constraint pumped-storage scheduling problems and provide strong technical support for intelligent scheduling systems. Full article

Inertial measurement units (IMUs) have gained popularity as portable and cost-effective alternatives to optoelectronic motion capture systems for assessing joint kinematics. This study aimed to validate a commercially available multi-sensor IMU-based system against a laboratory-grade motion capture system across lower limb, trunk, and upper limb movements. Fifteen healthy participants performed a battery of single- and multi-joint tasks while motion data were simultaneously recorded by both systems. Range of motion (ROM) values were extracted from the two systems and compared. The IMU-based system demonstrated high concurrent validity, with non-significant differences in most tasks, root mean square error values generally below 7°, percentage of similarity greater than 97%, and strong correlations (r ≥ 0.77) with the reference system. Systematic biases were trivial (≤3.9°), and limits of agreement remained within clinically acceptable thresholds. The findings indicate that the tested IMU-based system provides ROM estimates statistically and clinically comparable to those obtained with optical reference systems. Given its portability, ease of use, and affordability, the IMU-based system presents a promising solution for motion analysis in both clinical and remote rehabilitation contexts, although future research should extend validation to pathological populations and longer monitoring periods. Full article

The European Water Framework Directive (WFD) 2000/60/EC emphasizes the use of fish communities as key indicators for assessing the ecological quality of freshwater ecosystems. Despite over two decades of WFD implementation, many Balkan countries lack standardized ecological assessment indices, particularly for fish fauna. This situation complicates efforts to monitor and manage aquatic ecosystems, especially transboundary waters facing significant environmental pressures. In this context, our study assesses fish communities and ecological quality in Lake Dojran, a transboundary lake shared by Greece and North Macedonia. Fish sampling was independently conducted by each country (North Macedonia in 2021 and Greece in 2023), using benthic multi-mesh gillnets following standardized European methodologies (CEN 2005). A total of 12 out of 16 historically recorded fish species were confirmed. Higher catch per unit effort (CPUE) values were observed in 2021 (282.50 specimens/gillnet, biomass 6321.81 g/gillnet) compared to 2023 (207.83 specimens/gillnet, 2378.67 g/gillnet). Dominant species included Alburnus macedonicus and Perca fluviatilis. No significant differences were found in CPUE values based on either number of specimens (NPUE) or biomass (BPUE) across the different depth zones. Using the Greek Lake Fish Index (GLFI), ecological quality based on fish fauna was classified as “good” in 2021 and “high” in 2023, reflecting the low relative contribution of both introduced numerical abundance and omnivorous species biomass in total catches. This study contributes valuable baseline data for transboundary ecological management and conservation strategies, supporting efforts aligned with WFD objectives. Full article

Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) in the tribe Xyleborini are economically important pests of woody ornamentals, tree nuts, and fruit orchards, including pecans in the United States. Among them, the granulate ambrosia beetle, Xylosandrus crassiusculus (Motschulsky), is the most common species in pecan orchards in Georgia. Various traps, including ethanol-mediated Lindgren multi-funnel traps, panel traps, bottle traps, sticky cards, and ethanol-infused wooden bolts, are used in ambrosia beetle monitoring programs. Trap color and placement height are important factors that increase trap effectiveness. To improve trap effectiveness for ambrosia beetles, we conducted a color and height preference experiment under field conditions using six different colored sticky cards, including black, blue, green, red, transparent, and yellow, placing them at three different heights (15, 60, and 120 cm from ground level). The results show that red and transparent sticky cards consistently captured a higher number of ambrosia beetles, whereas yellow-colored sticky cards consistently captured a lower number of ambrosia beetles compared to all other tested colors of sticky cards. A similar trend was observed with X. crassiusculus in field and laboratory settings. Among the evaluated trap heights, more ambrosia beetles, including X. crassiusculus, were consistently captured in the sticky cards placed at a height of 60 cm from the ground surface. Additionally, we monitored natural infestations of ambrosia beetles in commercial pecan orchards in Georgia and found more damage to pecan trees near the ground surface (45 cm) compared to the upper parts. We also recorded three ambrosia beetle species, X. crassiusculus, the black stem borer, X. germanus (Blandford), and the Southeast Asian ambrosia beetle, Xylosandrus amputatus (Blandford). Among them, X. crassiusculus (90.50%) was the most abundant species in the pecan orchards. Therefore, red and transparent sticky cards placed at a height of 45 to 60 cm could improve the trap efficacy and can be used for monitoring ambrosia beetles in pecan orchards. Full article

Objective: Current good manufacturing practice (cGMP) guidance for positron emission tomography (PET) drugs has been established in Europe and the United States. In Japan, the Pharmaceuticals and Medical Devices Agency (PMDA) approved the use of radiosynthesizers as medical devices for the in-house manufacturing of PET drugs in hospitals and clinics, regardless of the cGMP environment. Without adequate facilities, equipment, and personnel required by cGMP regulations, the quality assurance (QA) and clinical effectiveness of PET drugs largely depend on the radiosynthesizers themselves. To bridge the gap between radiochemistry standardization and site qualification, the Japanese Society of Nuclear Medicine (JSNM) has issued guidance for the in-house manufacturing of small-scale PET drugs under academic GMP (a-GMP) environments. The goals of cGMP and a-GMP are different: cGMP focuses on process optimization, certification, and commercialization, while a-GMP facilitates the small-scale, in-house production of PET drugs for clinical trials and patient-specific standard of care. Among PET isotopes, N-13 has a short half-life (10 min) and must be synthesized on site. [¹³N]Ammonia ([¹³N]NH₃) is used for myocardial perfusion imaging under the Japan Health Insurance System (JHIS) and was thus selected as a working example for the manufacturing of PET drugs in an a-GMP environment. Methods: A [¹³N]NH₃-radiosynthesizer was installed in a hot cell within an a-GMP-compliant radiopharmacy unit. To comply with a-GMP regulations, the air flow was adjusted through HEPA filters. All cabinets and cells were disinfected to ensure sterility once a month. Standard operating procedures (SOPs) were applied, including analytical methods. Batch records, QA data, and radiation exposure to staff in the synthesis of [¹³N]NH₃ were measured and documented. Results: 2.52 GBq of [¹³N]NH₃ end-of-synthesis (EOS) was obtained in an average of 13.5 min in 15 production runs. The radiochemical purity was more than 99%. Exposure doses were 11 µSv for one production run and 22 µSv for two production runs. The pre-irradiation background dose rate was 0.12 µSv/h. After irradiation, the exposed dosage in the front of the hot cell was 0.15 µSv/h. The leakage dosage measured at the bench was 0.16 µSv/h. The exposure and leakage dosages in the manufacturing of [¹³N]NH₃ were similar to the background level as measured by radiation monitoring systems in an a-GMP environments. All QAs, environmental data, bacteria assays, and particulates met a-GMP compliance standards. Conclusions: In-house a-GMP environments require dedicated radiosynthesizers, documentation for batch records, validation schedules, radiation protection monitoring, air and particulate systems, and accountable personnel. In this study, the in-house manufacturing of [¹³N]NH₃ under a-GMP conditions was successfully demonstrated. These findings support the international harmonization of small-scale PET drug manufacturing in hospitals and clinics for future multi-center clinical trials and the development of a standard of care. Full article