Search Results (1,173)

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations. Full article

Unmanned aerial vehicles (UAVs) have been widely used in surveying and mapping, inspection, emergency rescue, and environmental monitoring. However, effective path planning remains a key challenge in complex three-dimensional terrain, where UAVs must simultaneously cope with terrain undulations, no-fly zones, safety-clearance requirements, and trajectory-smoothness constraints. In addition, conventional intelligent optimization algorithms often suffer from search instability and premature convergence. To address these challenges, this study proposes a reinforcement-learning-driven multi-strategy spherical-vector grey wolf optimizer, termed TLQ-SGWO, where TLQ denotes the combined use of Tent–Logistic hybrid initialization and Q-learning search-strategy scheduling. In the proposed method, candidate trajectories are encoded using spherical-vector increments; Tent–Logistic hybrid initialization is introduced to enhance population diversity; and Q-learning is incorporated to adaptively select search strategies, thereby dynamically balancing exploration and exploitation. A comprehensive cost function integrating path length, threat avoidance, terrain clearance, and trajectory smoothness is further constructed to improve the feasibility and safety of the planned trajectories. Experiments are conducted on the CEC2017 benchmark functions and artificially generated complex mountainous terrain scenarios. On the CEC2017 benchmark suite, TLQ-SGWO achieves the best average rankings in both mean error and standard deviation among the seven compared algorithms, indicating a stronger balance between optimization accuracy and robustness. In artificial mountainous scenarios, TLQ-SGWO obtains the lowest mean path cost in three of the four scenarios and remains statistically comparable to the strongest hybrid baseline in the remaining scenario, while maintaining stable feasible 3D trajectories under increasing no-fly-zone complexity. Full article

Background/Objectives: Drug repurposing has emerged as a promising strategy for identifying novel anticancer agents among clinically established drugs. Fexofenadine, a second-generation H1 antihistamine, has been proposed as a candidate for repurposing in oncology; however, the molecular mechanisms underlying its biological activity remain insufficiently characterized. This study investigated the effects of fexofenadine on oxidative stress, mitochondrial function, apoptosis, and pro-survival signaling pathways in cervical and lung cancer cells. Methods: HeLa and A549 cancer cells, as well as non-tumorigenic Beas-2B epithelial cells, were exposed to fexofenadine under in vitro conditions. Cell viability, apoptosis, reactive oxygen species generation, mitochondrial membrane potential, DNA damage, autophagy-associated responses, and PI3K/AKT and MAPK/ERK pathway activation were assessed using flow cytometry, fluorescence microscopy, electron microscopy, and biochemical assays. Three-dimensional spheroid cultures and N-acetyl-L-cysteine rescue experiments were additionally employed to evaluate biological relevance and the contribution of oxidative stress. Results: Fexofenadine induced concentration-dependent accumulation of reactive oxygen species, mitochondrial membrane depolarization, Bcl-2 inactivation, caspase-3/7 activation, DNA damage, and apoptotic cell death in HeLa and A549 cells. Antioxidant pretreatment with N-acetyl-L-cysteine significantly reduced oxidative stress, attenuated mitochondrial dysfunction, and partially suppressed apoptosis. Fexofenadine was associated with reduced PI3K/AKT and MAPK/ERK pathway activation and promoted autophagy-associated responses. In three-dimensional spheroid cultures, treatment disrupted spheroid integrity and increased apoptotic cell death. Non-tumorigenic Beas-2B cells exhibited lower sensitivity to treatment than malignant cells. Conclusions: Fexofenadine disrupts redox homeostasis and is associated with reduced activation of pro-survival signaling pathways, resulting in oxidative stress-associated mitochondrial dysfunction and apoptosis in cancer cells. These findings provide mechanistic support for further evaluation of fexofenadine as a candidate for anticancer drug repurposing, while additional pharmacokinetic and in vivo studies are required to determine its translational relevance. Full article

Background/Objectives: Contemporary patient-specific subperiosteal implants have re-emerged as graftless solutions for oral and maxillofacial rehabilitation, driven by advances in digital planning, CAD/CAM workflows, additive manufacturing, and biomaterial engineering. Their indications have progressively expanded from severely atrophic edentulous jaws to segmental defects, single-tooth replacement, congenital craniofacial anomalies, salvage situations, and oncologic reconstruction. This scoping review aimed to map the current evidence on modern patient-specific subperiosteal implants, focusing on indications, workflow, design principles, materials, outcomes, complications, and maintenance. Methods: A scoping review was conducted according to PRISMA-ScR principles to identify clinical studies, case series, case reports, systematic and scoping reviews, technical notes, finite element analyses, in vitro studies, and relevant translational investigations dealing with contemporary custom-made or CAD/CAM subperiosteal implants. The evidence was narratively synthesized according to clinical indication and thematic domains, including full-arch rehabilitation, sectional and single-tooth applications, congenital and post-oncologic defects, rescue indications, biomechanics, material selection, surface response, prosthetic protocols, and complication management. No quantitative meta-analysis was performed because of the scoping design and the substantial heterogeneity of study types, indications, implant systems, outcome definitions, and follow-up durations. Results: The final evidence map included 116 records, of which 56 were unique human clinical records with extractable denominators and 60 were biomechanical, in vitro, surface-biology, review, consensus, historical, or conceptual records. Of the 56 unique clinical records, 49 were mapped within the six indication-level clinical sections, while seven were retained as cross-cutting clinical evidence addressing patient-reported outcomes, design-related complications, bone apposition, anchorage strategy, comparative graftless rehabilitation, or reconstructive/prosthetic principles. The six indication-level sections included 52 clinical-record assignments: 15 for full-arch rehabilitation, 13 for segmental or sectional rehabilitation, one for single-tooth rehabilitation, four for congenital or craniofacial indications, 13 for post-oncologic or post-ablative reconstruction, and six for rescue or salvage indications. Because three records addressed more than one indication, these counts represent indication-level assignments rather than mutually exclusive clinical records. Reported survival in most short- to mid-term clinical series was generally high, commonly ranging from 90% to 100%, although lower values of 70–80% were reported in selected longer-term cohorts and survival clearly overestimated clinical success in some studies. Expanding applications include posterior mandibular and maxillary defects, lateral incisor agenesis, cleft-related or syndromic deformities, maxillectomy reconstruction, obturator support, and hybrid rehabilitation with endosseous implants; however, evidence for the indications at the extremes of this spectrum—single-tooth replacement and primary oncologic reconstruction—remains limited to small, largely single-group case series and reports. Soft-tissue events, including dehiscence, mucositis, recession, and framework exposure, were the dominant complications and showed wide variability, with reported recession/exposure rates ranging from approximately 10% in some sectional and full-arch series to as high as 65% in bilateral maxillary cohorts; their clinical significance varied from asymptomatic stable findings to progressive inflammatory complications requiring revision. Conclusions: Patient-specific subperiosteal implants represent a promising and increasingly versatile reconstructive option; however, the present findings should be interpreted as evidence mapping rather than as definitive comparative evidence. Their clinical use should remain highly selective, prosthetically driven, and supported by meticulous planning, rigid fixation, soft-tissue management, and structured maintenance. Standardized success criteria, longer follow-up, and comparative prospective studies are required. Full article

Safe-separation-and-collision-avoidance unmanned aerial vehicle (UAV) swarms are increasingly used for inspection, emergency response, environmental monitoring, and search-and-rescue support in cluttered airspace where communication links may be delayed, degraded, or intermittently unavailable. These applications require heterogeneous vehicles to maintain situational awareness, allocate tasks, and avoid hazards under partial observability and changing team topology. To address these challenges, this paper proposes a Hierarchical Graph-Attention Multi-Agent Reinforcement Learning architecture (HG-MARL) for safe-separation-and-collision-avoidance heterogeneous UAV swarm coordination. The proposed framework decomposes the task into high-level resource allocation and low-level local-control execution, uses graph attention for changing swarm topology, and applies Transformer memory, action masking, potential-field reward shaping, and domain-randomized simulation training. In the multi-scenario simulation summaries, HG-MARL achieves 92.9%, 89.8%, and 82.6% task success in Scenarios A–C, respectively, improving upon MAPPO by 15.1, 21.4, and 20.1 percentage points. Summary-statistic Welch tests show that all six HG-MARL comparisons against MAPPO and QMIX yield $p<0.01$ with large effect sizes. Fair-control, reward-sensitivity, communication-degradation, safety-ablation, training-stability, latency, and transfer-oriented stress tests further support the contributions of the integrated architecture. The validation scope is simulator-based, with platform-level flight/HIL evaluation discussed as future work. These results suggest that HG-MARL is a promising simulation-validated framework for civilian UAV swarm coordination in collision-and-separation-critical and communication-degraded environments. Full article

Russian state-sponsored disinformation has been described in policy and the operational literature, but it is less often formalized in game-theoretic terms. Here, a two-layered formal model is developed showing how adversarial perturbation of a communication channel can collapse cross-group felt understanding—the third-order intentional state/belief structure, established empirically by Livingstone, in which one group believes its perspectives are recognized and accepted as valid by another. The Analytical Model is a static Bayesian signaling game with binary types and a noisy channel parameterized by perturbation rate π. The Analytical Model shows that when recognition benefits exceed signaling costs, there exists a perturbation threshold π* = 1 − c_R/(u_R · p) above which mutual misrecognition becomes the unique Perfect Bayesian Equilibrium outcome. The Computational Model embeds this logic in an agent-based simulation on a homophilic stochastic block model and scale-free networks with continuous recognition capacity. Four substantive findings emerge: the closed-form analytical threshold from the Analytical Model predicts the boundary of collapse in the dynamic networked simulation; high network homophily protects cooperative behavior below π* but provides no rescue above it; bridge seeding—the placement of recognition-capable agents at structurally central cross-group positions—is the most effective of three policy interventions tested, rescuing cooperation even above π*; and uniform adversarial volume is approximately as damaging as strategically targeted adversarial precision across both small dense and large scale-free topologies, qualifying the operational claim that targeted disinformation should strictly outperform volume-based approaches. The model is illustrated with the German Atomausstieg (nuclear phase-out) case, and implications for clinical psychology, public policy, and intergroup recognition under psychological warfare are discussed. Full article

Social media has emerged as a crucial environment for examining public sentiment during disasters, providing immediate insights into collective emotions and urgent expectations. This research examines the emotional reactions expressed on Turkish posts shared on the X platform (formerly Twitter) following the 6 February 2023 earthquake by employing an integrated method that combines topic modeling and topic-based sentiment analysis. Data were collected between 10 February 2023 and 28 February 2023. A large dataset consisting of 305,000 tweets was compiled, and 296,836 tweets remained for analysis after preprocessing and filtering procedures. Latent Dirichlet Allocation (LDA), enhanced with term frequency-inverse document frequency weighting and bigram extraction techniques, was applied to identify prominent themes, including rescue operations, appeals for assistance, communication about missing persons, and disaster management. The sentiment polarity within each topic was determined using a hybrid deep learning model incorporating Bidirectional Encoder Representations from Transformers (BERT) embeddings Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) layers, and FastText representations. This model reached a classification accuracy of 94%, with F1-scores of 0.91 and 0.95, recall values of 0.90 and 0.96, and precision values of 0.92 and 0.95, achieving higher performance than the evaluated baseline models. The findings indicate that supportive, solidarity-oriented, and resilience-related communication patterns were among the most frequently observed positive sentiment expressions, whereas negative sentiments appeared more frequently in discussions regarding delays in aid delivery and perceived shortcomings in institutional response. This study presents a scalable and flexible framework for analyzing sentiment in Turkish-language crisis communication, providing insights that may support disaster response monitoring and decision-making processes as well as the development of systems for tracking public reactions in real time. Full article

The increasing complexity of multi-hazard disasters poses significant challenges to sustainable disaster risk governance. However, prevention and control measures are often scattered across heterogeneous and unstructured sources, limiting their systematic reuse and application. To address this issue, this study proposes a structured framework and data-driven analysis approach for organizing prevention and control measures in multi-hazard scenarios. By integrating multi-source information, a four-dimensional framework consisting of human, technical, engineering, and managerial measures was developed, together with a two-dimensional representation model incorporating disaster scenarios. Large language models (LLMs) were employed to automatically extract prevention and control measures from disaster-related documents and construct a multi-hazard prevention dataset. A case study of typhoon–hazardous chemical leakage scenarios yielded 1089 measurement records. Results show that managerial measures had the highest coverage (87.1%), while technical measures mainly focused on critical risk nodes such as leakage monitoring and automatic interlock control. Prevention and preparedness measures accounted for 67.4% of all records, reflecting a proactive risk-governance orientation. Strong associations were observed among the four categories of measures (Jaccard coefficient: 0.624–0.879). The proposed framework supports the structured representation, knowledge organization, and data-driven analysis of prevention and control measures, providing a foundation for sustainable disaster risk governance and resilient emergency management. Full article

Hydrocarbon biodegradation in cold environments is constrained not only by microbial catabolic capacity but also by interfacial access to poorly soluble substrates and by the way remediation materials interact with microbial envelope-related structures. This study presents an uncertainty-aware low-temperature computational screening workflow for prioritizing functionalized oxide surface-patch models that may favor hydrocarbon association while avoiding excessive perturbation of simplified microbial-interface proxies. Twelve finite oxide–ligand candidates derived from TiO₂, ZnO, and CeO₂ patches functionalized with bare, catechol, glycerol, or citric acid states were evaluated against three hydrocarbon probes, hexane, toluene, and naphthalene, and two microbial-interface proxies. The workflow combined GFN2-xTB geometry optimization and relative interaction-energy screening, clean GFN2-xTB/ALPB rescoring with rescue tracking, short xTB-MD perturbation analysis, ORCA refinement of selected candidates, sensitivity analysis of ranking parameters, and integrated evidence classification. The analysis supports interfacial selectivity, rather than maximum adsorption strength, as the central design principle. TiO₂–catechol and TiO₂–glycerol remain experimentally testable primary candidates because their original screening profile combines chemically interpretable hydrocarbon association with comparatively mild microbial-proxy interaction descriptors. ZnO–catechol and ZnO–glycerol emerged as sensitivity-competitive secondary candidates under several scoring assumptions. Completed short xTB-MD trajectories further showed that TiO₂–glycerol produced moderate perturbation against the peptide proxy, whereas TiO₂–glycerol against NAG and ZnO–catechol against the peptide proxy showed very high proxy displacement. Overall, the workflow provides a transparent prioritization framework for experimental validation. Full article

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies. Full article

Progression to fibrosis is a major complication of chronic kidney disease (CKD) in obesity, type 2 diabetes, hypertension, and metabolic syndrome, yet effective antifibrotic therapies remain limited. Here, we review how disordered renal energy metabolism—ectopic lipid accumulation, impaired fatty acid oxidation (FAO), and a compensatory shift toward glycolysis—drives tubulointerstitial fibrosis in fatty kidney disease. Lipid overload in tubular, glomerular, and vascular cells arises from increased uptake via scavenger and lipoprotein receptors, enhanced lipogenesis, and reduced lipid catabolism and clearance. Spatial lipidomic studies further reveal nephron-segment-specific lipid signatures and obesity-associated oxidized phospholipids linked to glomerular inflammation. Lipotoxicity, mitochondrial damage, and associated innate-immune signaling, ferroptosis, cellular senescence, and adipose-derived mediators (including leptin, adiponectin, and a locally active renin–angiotensin system) converge on myofibroblast activation from pericytes, fibroblasts, and other resident cells. We discuss established and emerging therapies targeting this metabolic axis—peroxisome proliferator-activated receptor-α (PPARα) modulators, sodium–glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and the mineralocorticoid receptor antagonist finerenone—and propose that restoring metabolic flexibility, by rescuing FAO while limiting maladaptive glycolysis, offers a promising disease-modifying strategy for fatty kidney disease. Full article

Mycobacterium abscessus subsp. massiliense (Mabs) is an emerging nontuberculous mycobacterium associated with difficult-to-treat infections due to intrinsic antimicrobial resistance, intracellular persistence, biofilm formation, and limited responsiveness to currently available therapeutic regimens. In this context, adjuvant strategies targeting iron-dependent metabolic pathways and metal homeostasis may enhance the efficacy of conventional antimicrobials. This study investigated deferoxamine (DFO), a clinically approved iron chelator, as a potential adjuvant against Mabs using integrated in vitro and in silico approaches. Cytocompatibility was assessed using an MTT assay in RAW 264.7 macrophages and a hemolysis assay in human erythrocytes. Antimicrobial activity was evaluated through minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays, while interactions with clarithromycin (CLA) and amikacin (AMK) were assessed using the checkerboard method. Effects on virulence-associated phenotypes were examined through biofilm formation assays and protein quantification in extracellular vesicle-enriched fractions. Intracellular activity and modulation of inflammatory mediator gene expression were investigated in Mabs-infected RAW 264.7 macrophages through colony-forming unit (CFU) recovery and reverse transcription quantitative polymerase chain reaction (qPCR). DFO exhibited low cytotoxicity and negligible hemolytic activity under the tested conditions. Direct antimicrobial testing revealed a predominantly bacteriostatic profile (MIC = 9.75 µg/mL; MBC > 10 mg/mL), whereas checkerboard analysis suggested a synergistic interaction with CLA (FICI = 0.047), which requires further confirmation by time-kill or CFU-based combination assays. Furthermore, DFO reduced biofilm biomass, decreased protein levels in vesicle-enriched fractions, lowered intracellular bacterial burden, and modulated cytokine gene expression in infected macrophages. Molecular docking, ADME/Tox, and PASS analyses generated exploratory hypotheses regarding potential molecular interactions and pharmacological properties. Overall, these findings support DFO as a promising experimental adjuvant candidate for further investigation against Mabs, particularly in combination with clarithromycin. However, confirmation of a putative iron-restriction-associated mechanism and its translational relevance will require validation in additional clinical isolates, iron-rescue experiments, mature biofilm models, and in vivo studies. Full article

Ferroptosis has emerged as a critical mechanism linking iron dysregulation, oxidative stress, and neurodegeneration in amyloid-associated pathologies. Building on our previous work, which identified compound 20 as a promising antioxidant and neuroprotective agent, the present study investigates the molecular mechanisms underlying its protective activity against amyloid-induced ferroptosis in human neuroblastoma SH-SY5Y cells exposed to Aβ(25–35). Compound 20 (3-(((4-hydroxybenzyl)(methyl)amino)methyl)-1-methyl-N-(2-(piperazin-1-yl)ethyl)-1H-indole-5-carboxamide) markedly counteracted Aβ(25–35)-induced ferroptotic damage by restoring intracellular glutathione levels, depleting the labile iron pool, and suppressing lipid peroxidation. In parallel, the compound significantly rescued mitochondrial membrane potential and attenuated endoplasmic reticulum (ER) expansion associated with ER stress, thereby preserving cellular homeostasis under oxidative challenge. These protective effects were further corroborated by real-time PCR analysis, which revealed the modulation of key genes involved in the oxidative stress response, endoplasmic reticulum stress, and inflammatory pathways. To gain a systems-level insight into these mechanisms, untargeted ¹H-NMR metabolomic profiling was performed. This analysis confirmed the activation of antioxidant pathways and disclosed a significant modulation of energy metabolism and GABA-related pathways, both of which are closely linked to redox balance and neuronal resilience. Overall, these findings demonstrate that compound 20 drives metabolic reprogramming that orchestrates its multifactorial protective effect against Aβ(25–35)-induced ferroptosis by coordinating antioxidant defense, iron homeostasis, and ER stress mitigation. Full article

Background/Objectives: Ultrasound-guided fascial plane blocks have emerged as opioid-sparing analgesic strategies for cardiac surgery; however, evidence regarding combined block techniques remains limited. This randomized controlled trial evaluated the analgesic efficacy of combined pectoserratus plane block (PSPB) and pecto-intercostal fascial plane block (PIFB) in patients undergoing cardiac surgery via median sternotomy. Methods: Sixty-two adult patients undergoing cardiac surgery via median sternotomy were randomized to either a block group receiving bilateral PSPB and PIFB after anesthetic induction or a control group receiving conventional analgesia alone. The primary outcome was postoperative visual analog scale (VAS) pain score at 6, 12, 24, and 48 h after surgery. Secondary outcomes included Korean version of Quality of Recovery-15 (QoR-15K) scores, total opioid consumption, rescue analgesic dose, time to first rescue analgesia, extubation time, intensive care unit (ICU) stay, hospital stay, and the incidence of postoperative nausea and vomiting. Results: Fifty-four patients were included in the final analysis. Postoperative VAS scores did not differ significantly between groups after Bonferroni correction for repeated measurements. No significant overall between-group effect was observed in repeated-measures ANOVA. ICU stay was statistically shorter in the block group, although the absolute difference was small and of uncertain clinical relevance. No significant differences were observed in the remaining secondary outcomes. Conclusions: Combined PSPB and PIFB did not reduce postoperative pain or improve recovery outcomes after cardiac surgery via median sternotomy. Early postoperative pain scores were numerically higher in the block group, although these differences were not statistically significant after correction for multiple comparisons. The incremental analgesic benefit of combined fascial plane blocks may therefore be limited in this clinical setting. Full article