Search Results (765)

Urban scene understanding for inclusive smart cities remains challenged by the scarcity of training data capturing people with mobility impairments. We propose SYNTHUA-DT, a novel methodological framework that integrates unmanned aerial vehicle (UAV) photogrammetry, 3D digital twin modeling, and high-fidelity simulation in Unreal Engine to generate annotated synthetic datasets for urban accessibility applications. This framework produces photo-realistic images with automatic pixel-perfect segmentation labels, dramatically reducing the need for manual annotation. Focusing on the detection of individuals using mobility aids (e.g., wheelchairs) in complex urban environments, SYNTHUA-DT is designed as a generalized, replicable pipeline adaptable to different cities and scenarios. The novelty lies in combining real-city digital twins with procedurally placed virtual agents, enabling diverse viewpoints and scenarios that are impractical to capture in real life. The computational efficiency and scale of this synthetic data generation offer significant advantages over conventional datasets (such as Cityscapes or KITTI), which are limited in accessibility-related content and costly to annotate. A case study using a digital twin of Curitiba, Brazil, validates the framework’s real-world applicability: 22,412 labeled images were synthesized to train and evaluate vision models for mobility aids user detection. The results demonstrate improved recognition performance and robustness, highlighting SYNTHUA-DT’s potential to advance urban accessibility by providing abundant, bias-mitigating training data. This work paves the way for inclusive computer vision systems in smart cities through a rigorously engineered synthetic data pipeline. Full article

Background: The increasing number of resistant bacterial strains is reducing the effectiveness of antimicrobial drugs in preventing infections. It has been shown that resistant strains invade living organisms and cause a wide range of illnesses, leading to a surprisingly high death rate. Objective: The present study aimed to identify novel dihydropteroate synthase (DHPS) inhibitors from Stenotrophomonas maltophilia using structure-based computational techniques. Methodology: This in silico study used various bioinformatics and cheminformatics approaches to find new DHPS inhibitors. It began by retrieving the crystal structure via PDB ID: 7L6P, followed by energy minimization. The DHPS enzyme was virtually screened against the CHEMBL library to target S. maltophilia through enzyme inhibition. Then, absorption, distribution, metabolism, and excretion (ADME) analysis was performed to select the top hits. This process identified the top-10 hits. Additionally, imidazole (control) was used for comparative assessment. Furthermore, a 100 ns molecular dynamics simulation and post-simulation analyses were conducted. The docking results were validated through binding free energy calculations and entropy energy estimation approaches. Results: The docking results prioritized 10 compounds based on their binding scores, with a maximum threshold of −7 kcal/mol for selection. The ADME assessment shortlisted 3 out of 10 compounds: CHEMBL2322256, CHEMBL2316475, and CHEMBL2334441. These compounds satisfied Lipinski’s rule of five and were considered drug-like. The identified inhibitors demonstrated greater stability and less deviation compared to the control (imidazole). The average RMSD stayed below 2 Å, indicating overall stability without major deviations in the DHPS–ligand complexes. Post-simulation analysis assessed the stability and interaction profiles of the complexes under physiological conditions. Hydrogen bonding analysis showed the control to be more stable than the three tested complexes. Increased salt bridge interactions suggested stronger electrostatic stabilization, while less alteration of the protein’s secondary structure indicated better structural compatibility. These findings support the potential of these novel ligands as potent DHPS inhibitors. Binding energy estimates showed that CHEMBL2322256 was the most stable, with scores of −126.49 and −124.49 kcal/mol. Entropy calculations corroborated these results, indicating that CHEMBL2322256 had an estimated entropy of 8.63 kcal/mol. Conclusions: The newly identified compounds showed more promising results compared to the control. While these compounds have potential as innovative drugs, further research is needed to confirm their effectiveness as anti-DHPS agents against antibiotic resistance and S. maltophilia infections. Full article

Background/Objectives: Annona muricata (AM), commonly known as soursop or guanabana, has long been used in traditional medicine for its purported anticancer properties. However, scientific studies evaluating its potential enhancing or additive effects with conventional antineoplastic drugs (ADs) remain limited. This study aimed to assess the cytotoxic effects of an aqueous AM infusion alone and in combination with standard ADs in cancer cell lines, while also evaluating its safety in healthy cells. Additionally, we explored the potential molecular interactions of AM metabolites with therapeutic targets using silico modeling. Methods: An AM infusion (125 and 250 µg/mL) was tested on two cancer cell lines—MDA-MB-231 (human triple-negative breast cancer) and TC-1 (murine HPV16-positive cancer)—as well as healthy human leukocytes and a non-tumorigenic mouse lung cell line. Cell viability was assessed using the Alamar Blue™ assay. The combined effects of AM with multiple first-line ADs were evaluated. In silico molecular docking was performed with Molegro Virtual Docker to assess the interaction of AM metabolites (quercetin and hyperoside) with the A2B adenosine receptor. Additionally, the physicochemical properties of 13 AD were analyzed to explore correlations with cytotoxic outcomes. Results: AM infusion alone exhibited low cytotoxicity in both cancer and healthy cell types. However, when combined with ADs, it enhanced cytotoxic effects in cancer cells while sparing healthy cells at the evaluated concentrations. Docking studies revealed strong interactions between quercetin and hyperoside (major metabolites in the AM infusion) and the A2B receptor, supporting a possible mechanistic explanation for the observed effects. Conclusions: AM infusion may act as a chemical modulator, potentiating the effects of conventional ADs in cancer cells while preserving normal cell viability. These findings encourage further preclinical exploration of AM as a complementary agent in integrative oncology. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

Continual reinforcement learning (CRL) agents face significant challenges when encountering distributional shifts. This paper formalizes these shifts into two key scenarios, namely virtual drift (domain switches), where object semantics change (e.g., walls becoming lava), and concept drift (task switches), where the environment’s structure is reconfigured (e.g., moving from object navigation to a door key puzzle). This paper demonstrates that while conventional convolutional neural networks (CNNs) struggle to preserve relational knowledge during these transitions, graph convolutional networks (GCNs) can inherently mitigate catastrophic forgetting by encoding object interactions through explicit topological reasoning. A unified framework is proposed that integrates GCN-based state representation learning with a proximal policy optimization (PPO) agent. The GCN’s message-passing mechanism preserves invariant relational structures, which diminishes performance degradation during abrupt domain switches. Experiments conducted in procedurally generated MiniGrid environments show that the method significantly reduces catastrophic forgetting in domain switch scenarios. While showing comparable mean performance in task switch scenarios, our method demonstrates substantially lower performance variance (Levene’s test, $p<1.0\times {10}^{-10}$), indicating superior learning stability compared to CNN-based methods. By bridging graph representation learning with robust policy optimization in CRL, this research advances the stability of decision-making in dynamic environments and establishes GCNs as a principled alternative to CNNs for applications requiring stable, continual learning. Full article

The Working Set concept, originally introduced by P. Denning for memory management, defines a dynamic subset of system elements actively in use. Designed to reduce page faults and prevent thrashing, it has proven effective in optimizing memory performance. This study explores the interdisciplinary potential of the Working Set by applying it to two distinct domains: virtual memory systems and epidemiological modeling. We demonstrate that focusing on the active subset of a system enables optimization in both contexts—minimizing page faults and containing epidemics via dynamic isolation. The effectiveness of this approach is validated through memory access simulations and agent-based epidemic modeling. The advantages of using the Working Set as a general framework for describing the behavior of dynamic systems are discussed, along with its applicability across a wide range of scientific and engineering problems. Full article

This study aims to understand the meaning nursing professionals attribute to their lived experiences of providing humanized care within a public hospital setting. Grounded in Jean Watson’s theory of human caring, the research adopts a qualitative, descriptive phenomenological design to capture the perceptions and emotions of nurses regarding humanized care. Data were collected through semi-structured interviews with nine experienced nurses, selected through purposive sampling. The interviews, conducted virtually between July and December 2024, were analyzed using Colaizzi’s method and supported by Atlas.ti software. Four main thematic categories emerged: institutional health policies, professional image and identity, strengths and challenges in care, and essential competencies for humanized care. The findings highlight the critical role of empathy, cultural sensitivity, ethical commitment, and emotional presence in delivering compassionate care. Participants emphasized that, beyond clinical procedures, humanized care requires relational and contextual sensitivity, often hindered by institutional limitations and excessive administrative burdens. The study concludes that nursing professionals are key agents in promoting ethical, empathetic, and culturally respectful practices that humanize health services. These insights offer valuable contributions for designing policies and training strategies aimed at strengthening humanized care as a cornerstone of quality healthcare systems. Full article

Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments. Full article

Background and Objective: This study presents a combined osteometric and biomechanical analysis of a Korean female cadaver exhibiting bilateral lower limb bone asymmetry with abnormal curvature and callus formation on the left femoral midshaft. Methods: To investigate bilateral bone length differences, osteometric measurements were conducted at standardized landmarks. Additionally, we developed three gait models using Meta Motivo, an open-source reinforcement learning platform, to analyze how skeletal asymmetry influences stride dynamics and directional control. Results: Detailed measurements revealed that the left lower limb bones were consistently shorter and narrower than their right counterparts. The calculated lower limb lengths showed a bilateral discrepancy ranging from 39 mm to 42 mm—specifically a 6 mm difference in the femur, 33 mm in the tibia, and 36 mm in the fibula. In the gait pattern analysis, the normal model exhibited a straight-line gait without lateral deviation. In contrast, the unbalanced, non-learned model demonstrated compensatory overuse and increased stride length of the left lower limb and a tendency to veer leftward. The unbalanced, learned model showed partial gait normalization, characterized by reduced limb dominance and improved right stride, although directional control remained compromised. Conclusions: This integrative approach highlights the biomechanical consequences of lower limb bone discrepancy and demonstrates the utility of virtual agent-based modeling in elucidating compensatory gait adaptations. Full article

The current proceedings summarize the presentations delivered during the third International Electronic Conference on Microbiology (ECM 2025), which was held online from 1 to 3 April 2025, via the SciForum platform. This virtual event brought together researchers from around the world to share recent advances in microbiological sciences. The ECM 2025 highlighted recent developments across a broad spectrum of microbiological research, including antimicrobial resistance, gut microbiota, infectious diseases, and environmental microbiomes. Participants shared their work through online presentations and abstracts, with selected submissions invited for full publication. The event fostered global collaboration, promoted open-access science, and showcased innovative tools for studying and managing microbial systems in health, agriculture, and industry. The multidisciplinary program was organized into several thematic sessions: S1. Gut Microbiota and Health Disease. S2. Foodborne Pathogens and Food Safety. S3. Antimicrobial Agents and Resistance. S4. Emerging Infectious Diseases. S5. Microbiome and Soil Science. S6. Microbial Characterization and Bioprocess. S7. Microbe–Plant Interactions. This conference report presents summaries of the contributions made by participating authors over the three-day event. Full article

This paper studies the distributed robust predefined-time sliding mode control (DRPSC) problem for high-order heterogeneous multi-agent systems under input saturation while considering external disturbances and model uncertainties. Firstly, a distributed predefined-time state observer (PTSO) is designed for each agent to achieve individual estimation of the state information of the virtual leader within a predefined time, and the observer does not need to count on the global information of the system. Secondly, a predefined-time auxiliary dynamic system (PTADS) is developed to solve the actuator’s input saturation problem. Thirdly, a distributed predefined-time sliding mode controller (PTSMC) is proposed, which ensures that the error converges to a small region near zero within a predefined time and combines H_∞ control to deal with the lumped uncertainty disturbances in the system to improve the robustness of the system. In addition, a memory event-triggered mechanism (METM) is designed to reduce the communication frequency of the underactuated AUV-USV multi-agent system and reduce the consumption of communication resources. Finally, Lyapunov theory is employed to prove that the closed-loop system is predefined-time stable, and the simulation results demonstrate that the proposed method is effective. Full article

Background: Perioperative care is an integral part of the procedure of a surgical operation, with strictly defined rules. The need to upgrade and improve some individual long-term processes aims at optimal patient care and the provision of high-level health services. Therefore, preoperative care is drawn up with new data resulting from the evolution of technology to upgrade the procedures that need improvement. According to the international literature, a factor considered to be of major importance is high preoperative anxiety and its effects on the patient’s postoperative course. High preoperative anxiety is postoperatively responsible for prolonged hospital stays, increased postoperative pain, decreased effect of anesthetic agents, increased amounts of analgesics, delayed healing of surgical wounds, and increased risk of infections. The use of Virtual Reality technology appears as a new method of managing preoperative anxiety. Objective: This study investigates the effect and effectiveness of Virtual Reality (VR) technology in managing preoperative anxiety in adult patients. Methods: A literature review was performed on 193 articles, published between 2017 and 2024, sourced from the scientific databases PubMed and Cochrane, as well as the trial registry ClinicalTrials, with a screening and exclusion process to meet the criterion of investigating VR technology’s effectiveness in managing preoperative anxiety in adult patients. This systematic review was conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Results: Out of the 193 articles, 29 were selected. All articles examined the efficacy of VR in adult patients (≥18) undergoing various types of surgery. The studies represent a total of 2.354 participants from 15 countries. There are two types of VR applications: distraction therapy and patient education. From the studies, 14 (48%) used the distraction VR intervention, 14 (48%) used the training VR intervention, and 1 (4%) used both VR interventions, using a range of validated anxiety scales such as the STAI, VAS-A, APAIS, and HADS. Among the 29 studies reviewed, 25 (86%) demonstrated statistically significant reductions in preoperative anxiety levels following the implementation of VR interventions. VR technology appears to manage preoperative anxiety effectively. It is a non-invasive and non-pharmacological intervention with minimal side effects. Conclusions: Based on the review, the management of preoperative anxiety with VR technology shows good levels of effectiveness. Further investigation of the efficacy by more studies and randomized controlled trials, with a larger patient population, is recommended to establish and universally apply VR technology in the preoperative care process as an effective method of managing preoperative anxiety. Full article

The search for selective anticholinergic agents stems from varying cholinesterase levels as Alzheimer’s Disease progresses from the mid-to-late stage and from butyrylcholinesterase’s (BChE) role in β-amyloid plaque formation. While structure-based and pharmacophore-based virtual screening could search from large libraries in a short time, these methods do not consider dynamic features that result from a ligand’s inhibition of the enzyme and consequently may under- or overexaggerate enzyme selectivity of a given ligand. In this computational study, we probed the selectivity of representative secondary metabolite compounds against acetylcholinesterase and BChE through molecular dynamics simulations. The results were evaluated by analysis of the root mean squared deviation of ligand heavy atoms, the radius of gyration of each inhibited and uninhibited enzyme, root mean squared fluctuation of residues, intermolecular interaction energy, and linear interaction energy approximation of the Gibbs free energy of binding. These considerations further reveal the induced-fit characteristics contributing to ChE selectivity that are predominantly due to the greater flexibility of BChE’s active site gorge. Full article

The global impact of the COVID-19 crisis has underscored the need for novel therapeutic candidates capable of efficiently targeting essential viral proteins. Existing therapeutic strategies continue to encounter limitations such as reduced efficacy against emerging variants, safety concerns, and suboptimal pharmacodynamics, which emphasize the potential of natural-origin compounds as supportive agents with immunomodulatory, anti-inflammatory, and antioxidant benefits. The present study significantly advances prior molecular docking research through comprehensive virtual screening of structurally related analogs derived from antiviral phytochemicals. These compounds were evaluated specifically against the SARS-CoV-2 main protease (3CLpro) and papain-like protease (PLpro). Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. Several analogs showed enhanced binding scores relative to their parent scaffolds, with CHEMBL1720210 (a shogaol-derived analog) demonstrating strong interaction with PLpro (−9.34 kcal/mol), and CHEMBL1495225 (a 6-gingerol derivative) showing high affinity for 3CLpro (−8.04 kcal/mol). Molecular interaction analysis revealed that CHEMBL1720210 forms hydrogen bonds with key PLpro residues including GLY163, LEU162, GLN269, TYR265, and TYR273, complemented by hydrophobic interactions with TYR268 and PRO248. CHEMBL1495225 establishes multiple hydrogen bonds with the 3CLpro residues ASP197, ARG131, TYR239, LEU272, and GLY195, along with hydrophobic contacts with LEU287. Gene expression predictions via DIGEP-Pred indicated that the top-ranked compounds could influence biological pathways linked to inflammation and oxidative stress, processes implicated in COVID-19’s pathology. Notably, CHEMBL4069090 emerged as a lead compound with favorable drug-likeness and predicted binding to PLpro. Overall, the applied in silico framework facilitated the rational prioritization of bioactive analogs with promising pharmacological profiles, supporting their advancement toward experimental validation and therapeutic exploration against SARS-CoV-2. Full article

In order to explore the influence of the temperature gradient on rock failure degree during freezing and thawing, freeze–thaw-cycle tests were carried out on saturated red sandstone under the conditions of all-directional freeze–thaw and unidirectional freeze–thaw. The results show that the deformation behavior of saturated red sandstone during freeze–thaw cycles is significantly affected by freeze–thaw direction, and the redistribution of water during freeze–thaw cycles leads to significant strain variations. Macro-cracks caused by all-directional freeze–thaw are located in the center of the sample and crack from the inside out, while macro-cracks caused by unidirectional freeze–thaw are perpendicular to the temperature gradient direction and located in the lower part of the sample. Unidirectional freeze–thaw cycles cause the vertical inhomogeneity of the sample to be more obvious, and the uniaxial compressive strength of the sample decreases more significantly in the early stage. After 30 freeze–thaw cycles, the uniaxial strength of all-directional freeze–thaw and unidirectional freeze–thaw samples tends to be stable and virtually identical. The freeze–thaw cycles have seriously damaged the micro-structure of the sample, but the extent of damage to the cementing agents between particles is weaker than that caused by the all-directional freeze–thaw, owing to the seepage path formed in the pore water under unidirectional freeze–thaw conditions. Full article