Search Results (128,359)

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

Lead (Pb) exposure poses a significant public health concern due to its neurotoxic effects. While mitochondrial dysfunction is implicated in lead neurotoxicity, the precise molecular mechanisms, particularly the role of non-coding RNA-mediated competing endogenous RNA networks, remain underexplored. SH-SY5Y neuroblastoma cells were treated with 10 μM lead acetate. Cell viability was assessed by Cell Counting Kit-8 (CCK-8). Mitochondrial ultrastructure and quantity were analyzed via transmission electron microscopy (TEM). Key mitochondrial dynamics proteins were examined by Western blot. Comprehensive transcriptome sequencing, including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs) and mRNAs, was performed followed by functional enrichment and ceRNA network construction. Selected RNAs and hub genes were validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). Lead exposure significantly reduced SH-SY5Y cell viability and induced mitochondrial damage (decreased quantity, swelling, fragmentation). Western blot confirmed an imbalance in mitochondrial dynamics, as indicated by decreased mitofusin 2 (MFN2), increased total and phosphorylated dynamin-related protein 1 (DRP1). Transcriptomic analysis revealed widespread differential expression of lncRNAs, circRNAs, miRNAs, and mRNAs. Enrichment analysis highlighted mitochondrial function and oxidative stress pathways. A ceRNA network identified five key hub genes: SLC7A11, FOS, HMOX1, HGF, and NR4A1. All validated RNA and hub gene expression patterns were consistent with sequencing results. Our study demonstrates that lead exposure significantly impairs mitochondrial quantity and morphology in SH-SY5Y cells, likely via disrupted mitochondrial dynamics. We reveal the potential regulatory mechanisms of lead-induced neurotoxicity involving ceRNA networks, identifying hub genes crucial for cellular stress response. This research provides a foundational framework for developing therapeutic strategies against lead-induced neurotoxicity. Full article

Background: Medullary thyroid cancer (MTC), a neuroendocrine tumor originating from thyroid parafollicular C-cells, presents therapeutic challenges, particularly in advanced stages. While RET proto-oncogene mutations are known drivers, a comprehensive understanding of the broader somatic mutation landscape is needed to identify novel therapeutic targets and improve prognostication. This study leveraged the extensive AACR Project GENIE dataset to characterize MTC genomics. Methods: A retrospective analysis of MTC samples from GENIE examined recurrent somatic mutations, demographic/survival correlations, and copy number variations using targeted sequencing data (significance: p < 0.05). Results: Among 341 samples, RET mutations predominated (75.7%, mostly M918T), followed by HRAS (10.0%) and KRAS (5.6%), with mutual exclusivity between RET and RAS alterations. Recurrent mutations included KMT2D (5.3%), CDH11 (5.3%), ATM (5.0%), and TP53 (4.1%). NOTCH1 mutations were enriched in metastatic cases (p = 0.023). Preliminary associations included sex-linked mutations (BRAF/BRCA1/KIT in females, p = 0.028), and survival (ATM associated with longer survival, p = 0.016; BARD1/BLM/UBR5/MYH11 with shorter survival, p < 0.05), though limited subgroup sizes warrant caution. Conclusions: This large-scale genomic analysis confirms the centrality of RET and RAS pathway alterations in MTC and their mutual exclusivity. The association of NOTCH1 mutations with metastasis suggests a potential role in disease progression. While findings regarding demographic and survival correlations are preliminary, they generate hypotheses for future validation. This study enhances the genomic foundation for understanding MTC and underscores the need for integrated clinico-genomic datasets to refine therapeutic approaches. Full article

The COVID-19 pandemic has significantly altered consumer habits, particularly in relation to food choices. In this context, plant-based diets have gained prominence, driven by health, environmental, and ethical considerations. This study investigates the primary motivational and inhibitory factors influencing the consumption of plant-based foods among residents of the Galicia–Northern Portugal Euroregion. Utilizing the Theory of Reasoned Action, an extended model was proposed and tested through a quantitative survey. A total of 214 valid responses were collected via an online questionnaire distributed in Portuguese and Spanish. Linear regression analysis revealed that health awareness, animal welfare, and environmental concern significantly shape positive attitudes, which subsequently affect the intention to consume plant-based foods. Additionally, perceived barriers—such as lack of taste and insufficient information—were found to negatively influence intention. These findings contribute to the consumer behavior literature and provide strategic insights for stakeholders aiming to promote more sustainable dietary patterns in culturally connected cross-border regions. Full article

As power grids continue to expand in scale, the occurrence of cascading failures within them can lead to significant economic losses. Therefore, assessing the criticality of grid nodes is crucial for ensuring the secure and stable operation of power systems and for mitigating losses when cascading failures occur. The classical Local Link Similarity (LLS) algorithm in complex networks evaluates the importance of network nodes from a neighborhood topology perspective, but it suffers from issues such as the excessive weighting of node degrees and the neglect of electrical parameters. Based on the classical algorithm, this paper first develops the Improved Local Link Similarity (ILLS) algorithm by substituting alternative similarity metrics and comparatively evaluating their performance. Building upon the ILLS, we then propose the Electrical LLS (ELLS) algorithm by integrating node power flow and electrical coupling connectivity as multiplicative factors, with optimal combinations determined via simulation experiments. Compared to classical approaches, ELLS demonstrates superior adaptability to power grid contexts and delivers enhanced accuracy in power system node importance assessments. These algorithms are applied to rank the node importance in the IEEE 300-bus system. Their performance is evaluated using the loss-of-load-size metric, comparing ELLS, ILLS, and the classical algorithm. The results demonstrate that under the loss-of-load-size metric, the ELLS algorithm achieves approximately 25% higher accuracy compared to both the ILLS and the classical algorithm, validating its effectiveness. Full article

Background/Objectives: The integration of machine learning (ML) and artificial intelligence (AI) has revolutionized the pharmaceutical industry by improving drug discovery, development and manufacturing processes. Based on literature data, an ML model was developed by our group to predict the formation of binary co-amorphous systems (COAMSs) for inhalation therapy. The model’s ability to develop a dry powder formulation with the necessary properties for a predicted co-amorphous combination was evaluated. Methods: An extended experimental validation of the ML model by co-milling and X-ray diffraction analysis for 18 API-API (active pharmaceutical ingredient) combinations is presented. Additionally, one COAMS of rifampicin (RIF) and ethambutol (ETH),two first-line tuberculosis (TB) drugs are developed further for inhalation therapy. Results: The ML model has shown an accuracy of 79% in predicting suitable combinations for 35 APIs used in inhalation therapy; experimental accuracy was demonstrated to be 72%. The study confirmed the successful development of stable COAMSs of RIF-ETH either via spray-drying or co-milling. In particular, the milled COAMSs showed better aerosolization properties (higher ED and FPF with lower standard deviation). Further, RIF-ETH COAMSs show much more reproducible results in terms of drug quantity dissolved over time. Conclusions: ML has been shown to be a suitable tool to predict COAMSs that can be developed for TB treatment by inhalation to save time and cost during the experimental screening phase. Full article

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods. Full article

Oral squamous cell carcinoma (OSCC) remains a significant global health challenge, representing 90% of oral malignancies. Despite therapeutic advances, patient outcomes remain poor, highlighting the need for novel prognostic biomarkers and treatment targets. We investigated the expression patterns of NTRK genes and their corresponding proteins (TrkA, TrkB, and TrkC) in OSCC, analyzing their relationships with clinical outcomes and potential as therapeutic targets. We examined 93 OSCC tissue samples using immunohistochemistry and quantitative real-time PCR. Protein expression was quantified using the H-score method. We analyzed correlations between Trk expression, clinicopathological parameters, and 2-year survival rates using chi-square tests, Mann–Whitney U tests, and Kaplan–Meier survival analysis. TrkA showed near-universal expression (97.8%—91 patients) in OSCC samples, with high expression levels significantly correlating with lower tumor grade (p = 0.014) and improved 2-year survival (p = 0.011). While TrkB and TrkC were expressed in 65.5% and 84.9% of cases, respectively, neither showed significant associations with clinical parameters. NTRK2 and NTRK3 mRNA levels demonstrated a strong positive correlation (R = 0.64, p = 0.002), suggesting coordinated regulation. Our findings establish TrkA as a promising positive prognostic marker in OSCC, warranting investigation as a therapeutic target. The strong correlation between NTRK2 and NTRK3 expression suggests shared regulatory mechanisms in OSCC pathogenesis. Further studies with larger cohorts and longer follow-up periods are needed to validate these findings and explore their therapeutic implications. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Porcine rotavirus (PoRV), a primary etiological agent of viral diarrhea in piglets, frequently co-infects with other enteric pathogens, exacerbating disease severity and causing substantial economic losses. Its genetic recombination capability enables cross-species transmission potential, posing public health risks. Globally, twelve G genotypes and thirteen P genotypes have been identified, with G9, G5, G3, and G4 emerging as predominant circulating strains. The limited cross-protective immunity between genotypes compromises vaccine efficacy, necessitating genotype surveillance to guide vaccine development. While conventional molecular assays demonstrate sensitivity, they lack rapid genotyping capacity and face technical limitations. To address this, we developed a novel diagnostic platform integrating reverse transcription recombinase-aided amplification (RT-RAA) with CRISPR–Cas12a. This system employs universal primers for the simultaneous amplification of G4/G5/G9 genotypes in a single reaction, coupled with sequence-specific CRISPR recognition, achieving genotyping within 50 min at 37 °C with 10⁰ copies/μL sensitivity. Clinical validation showed a high concordance with reverse transcription quantitative polymerase chain reaction (RT-qPCR). This advancement provides an efficient tool for rapid viral genotyping, vaccine compatibility evaluation, and optimized epidemic control strategies. Full article

This study systematically reviews the non-traditional pharmacological effects of diclofenac, a well-known nonsteroidal anti-inflammatory drug, to explore its potential for drug repositioning beyond its established analgesic and anti-inflammatory applications. A comprehensive literature search was conducted using the PubMed, Scopus and Web of Science databases, covering studies from 1981 to 2025. It was revealed that over 94% of records in Scopus and Web of Science are duplicated in PubMed, so the latter was used for the search in our study. After duplicate removal and independent screening, 89 from 1123 retrieved studies were selected for the search. The analysis revealed a broad spectrum of diclofenac’s non-traditional pharmacological activities, including neuroprotective, antiamyloid, anticancer, antiviral, immunomodulatory, antibacterial, antifungal, anticonvulsant, radioprotective, and antioxidant properties, primarily identified through preclinical In vitro and In vivo studies. These effects are mediated through diverse molecular pathways beyond cyclooxygenase inhibition, such as modulation of neurotransmitter release, apoptosis, and cellular proliferation. Diclofenac showed potential for repositioning in oncology, neurodegenerative disorders, infectious diseases, and immune-mediated conditions. Its hepatotoxicity and cardiovascular risks necessitate strategies like advanced drug formulations, dose optimization, and personalized medicine to enhance safety. Large-scale randomized clinical trials are essential to validate these findings and ensure safe therapeutic expansion. Full article

A rather novel approach for delivery of inertia-like grid services through energy storage devices is described and validated by physical experiments and on-site measurements. In this approach, denoted “amplified inertia response”, an actual inertial response from a grid-connected synchronous machine is amplified. This inertia emulation approach is contrasted by what is called synthetic inertia, which uses a frequency-locked loop in order to extract the grid frequency. The synthetic inertia faces the usual input signal filtering challenges if the signal-to-noise ratio is low. The amplified inertia controller avoids the input filtering since it only amplifies the natural inertial response from a synchronous machine. However, rotor angle oscillations lead to filtering requirements of the amplified version as well, but on the output signal of the controller. Experimental comparisons are conducted both on the measurement output from the physical experiments in a microgrid and on analysis based on input from on-site measurements from a 55 MVA hydropower generator connected to the Nordic grid. In the specific cases compared, we observe that the amplified inertia version is the better method for smaller power systems, with large frequency fluctuations. On the other hand, the synthetic inertia method is the better in larger power systems as compared to the amplification of the inertial response from a real production unit. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article