Search Results (2,030)

Wireless sensor networks using battery-powered, low-cost sensors, due to their non-rechargeability and strictly limited energy resources, are more sensitive to energy efficiency than other networks of this type. Clustered wireless sensor networks address this problem. In these networks, the most energy-intensive communication, i.e., a long-range one, is carried out via designated nodes, called cluster head nodes, while other cluster nodes communicate with their cluster heads. Cluster head node selection is handled by appropriate routing protocols, and newly designed protocols are first tested in simulations. Among the simulators of cluster head selection protocols, those implemented in a MATLAB environment play an important role, and among these, those implementing a first-order radio model to estimate the energy cost of transmission, both at the transmitter and at the receiver, play a particularly important role. This paper presents and discusses the energy aspects of MATLAB-based open-source wireless sensor network environments that employ the first-order radio model for the simulation of cluster head selection protocols. Current MATLAB-based open-source simulators of cluster head selection protocols were inventoried and analyzed. The review results showed that the first-order radio model had been used in its classic form for years, with the same default parameters. Although the simulators were written using different programming paradigms, precluding simple copy-and-paste, the first-order radio model was generally similar. However, there were exceptions to this rule. A hard exception is the simulator for a body-area wireless sensor network, which only implements a version of the first-order radio model specific to that environment. Soft exceptions are two simulators of the popular cluster head selection protocol, which implemented only half the functionality of the classic first-order radio model. On the one hand, this demonstrates both the widespread use of a conservative approach to the model, which ensures relatively easy repeatability of simulation results, and, on the other hand, the flexibility of the model, which allows its extension to other environments. Finally, the limitations of the model are presented and directions for future research are indicated. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Background: Neonatal and infant aortic arch reconstruction remains a high-risk cardiovascular procedure requiring effective cerebral and myocardial protection. Variability in perfusion strategies may influence early hemodynamic stability and postoperative recovery. This study aimed to evaluate the early and short-term cardiovascular outcomes of a standardized beating-heart aortic arch reconstruction strategy incorporating simultaneous antegrade selective cerebral and continuous coronary perfusion. Methods: In this retrospective single-center cohort study, 31 consecutive neonates and infants undergoing aortic arch reconstruction between November 2022 and December 2025 were analyzed. A standardized surgical protocol was applied, consisting of extensive ductal tissue resection, interdigitating posterior end-to-end anastomosis, anterior autologous pericardial patch augmentation, and moderate hypothermic antegrade selective cerebral perfusion combined with continuous coronary perfusion via innominate artery cannulation. Early postoperative outcomes and short-term echocardiographic follow-up results were assessed. Results: The cohort included 31 patients, 22.6% of whom had complex associated cardiac anomalies requiring concomitant procedures. Median cardiopulmonary bypass and aortic cross-clamp times were 119 and 64 min, respectively. There was no in-hospital mortality. Major complications were infrequent, and median intensive care unit stay was 5 days. During a median follow-up of 6.8 months, one patient (3.2%) developed recoarctation requiring reintervention. No late mortality was observed. Conclusions: A fully standardized beating-heart aortic arch reconstruction strategy incorporating simultaneous cerebral and coronary perfusion demonstrated favorable early cardiovascular and short-term outcomes, even in anatomically complex cases. Preservation of continuous coronary perfusion may be associated with improved myocardial stability and early postoperative recovery; however, these findings should be interpreted as observational and hypothesis-generating given the absence of a control group. Larger multicenter studies with longer follow-up are warranted to confirm these findings. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Castrate-resistant prostate cancer (PCa) is a critical situation in which many patients will relapse. Hormonal androgen deprivation therapy (HADT) is the gold standard of care when a patient relapses, following primary surgical or radiation therapy. Usually, the benefits from HADT are poor and recurrent disease after HADT treatment is termed castrate-resistant prostate cancer (CRPC), which is in most cases fatal. The therapeutic regimens for CRPC include chemotherapy with docetaxel, immunotherapy agent sipuleucel-T, the taxane cabazitaxel, the CYP17 inhibitor abiraterone acetate and the androgen receptor (AR) antagonist enzalutamide. Thus, it is imperative to study the inherent property of prostate cancer cells, to resist therapy and reconsider the therapeutic protocols (continuous v’s intermittent). We make use of a hybrid mathematical model which consists of an extension of a very potent ordinary differential equation (ODE) Baez–Kuang model, combined with two Game Theory components: the Minority Game for adaptive behavior and the Axelrod model for heterogeneity behavior. Our study suggests that increasing tumor adaptability, through Minority Game dynamics, improves short-term prostatic-specific antigen (PSA) control and stabilizes therapy cycles. However, this comes at the cost of driving the tumor to a homogeneous, androgen-independent (AI) state, which is therapy-resistant. Conversely, maintaining heterogeneity, via Axelrod dynamics, sustains a mixed population, with androgen-dependent (AD) cells persisting longer and potentially delaying resistance emergence. Full article

This study aims to systematically review how artificial intelligence (AI) is being adopted by small and medium-sized enterprises (SMEs) in Latin America to improve resilience and support sustainable practices in uncertain business environments. Based on the PRISMA protocol, fourteen peer-reviewed studies published between 2020 and 2025 were analyzed. The results identify significant barriers such as lack of adoption, including insufficient technological infrastructure, limited specialized talent, and budgetary constraints. Despite these obstacles, AI is increasingly used to automate processes and enhance predictive decision-making. While most applications remain concentrated in logistics, manufacturing, and telecommunications, their potential to improve efficiency and competitiveness is evident. The review also identifies the absence of robust regulatory frameworks as a critical limitation, particularly regarding ethical issues such as data privacy and algorithmic transparency. Although these aspects are not extensively covered in the reviewed literature, they represent important gaps for future research. In conclusion, the responsible adoption of AI in SMEs can contribute to business performance and sustainability goals, provided that appropriate public policies and sectoral strategies are implemented. Full article

This systematic review aimed to assess whether the initial apical position of impacted maxillary canines, evaluated using cone-beam computed tomography [CBCT], influences three-dimensional root displacement during orthodontic traction. An extensive literature search was conducted in PubMed/MEDLINE, Web of Science, Embase, Scopus, and the Cochrane Library up to November 2025. Prospective and retrospective clinical studies including pre-treatment CBCT assessment and reporting either direct apical displacement or CBCT-derived three-dimensional position parameters were considered eligible. Study selection, data extraction, and quality appraisal were carried out independently by two reviewers. Seven studies met the inclusion criteria. Substantial heterogeneity was observed in imaging protocols, reference systems, traction mechanics, and outcome measures, precluding quantitative synthesis. Only two studies directly quantified three-dimensional apical displacement using CBCT–CBCT or CBCT–STL superimposition methods, predominantly suggesting bodily movement patterns; although, this is based on limited direct evidence, with velocities ranging from 0.29 to 0.84 mm/month. The remaining studies provided indirect evidence based on angular changes, positional parameters, or traction duration. Taken together, the available evidence suggests that unfavorable initial apical positions, including palatal or bicortical impactions and increased root angulation, may be associated with greater biomechanical complexity and longer traction duration. Although CBCT-based three-dimensional evaluation provides clinically relevant diagnostic information, standardized measurement protocols are required to improve comparability and reproducibility across studies. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

The Internet of Drones (IoD) has emerged as a critical extension of the Internet of Things, enabling unmanned aerial vehicles to support diverse applications, including precision agriculture, logistics, disaster monitoring, and security surveillance. Despite its rapid growth, securing IoD communications remains a significant challenge due to the open wireless environment, high drone mobility, and strict computational and energy constraints. Existing authentication mechanisms either rely on computationally expensive cryptographic operations or remain validated only at the protocol or simulation level, leaving a critical gap in practical, hardware-validated solutions suitable for resource-constrained drone platforms. This gap motivates the need for a lightweight, privacy-preserving authentication scheme that is both theoretically sound and experimentally deployable on real hardware. To address this, we propose a Physically Unclonable Functions (PUF)-assisted lightweight authentication scheme for IoD environments that binds cryptographic keys to each drone’s intrinsic hardware characteristics via PUFs. The scheme employs dynamically generated pseudo-identities to conceal permanent drone identities and prevent tracking, while authentication and key agreement are achieved using efficient symmetric cryptographic primitives, including SHA-256 for key derivation and updates, AES-256 for secure communication, and lightweight XOR operations to minimize overhead. Forward secrecy is ensured through rolling key updates, and periodic renewal of PUF challenges enhances resistance to replay and modeling attacks. To validate practicality, both software-based and hardware-based implementations were developed and evaluated. The software evaluation demonstrates a low communication overhead of 708.5 bytes and an average computation time of 18.87 ms. The hardware implementation on a Nexys A7-100T FPGA operates at 100 MHz with only 12.49% LUT utilization and low dynamic power consumption of approximately 182.5 mW. These results confirm that the proposed framework achieves an effective balance between security, privacy, and efficiency. The significance of this work lies in providing a fully hardware-validated, PUF-based authentication framework specifically tailored to the real-world constraints of IoD environments, offering a practical foundation for securing next-generation drone networks. Full article

Low back pain is common in road cyclists and has been associated with prolonged lumbar flexion during cycling. The flexion–relaxation (FR) phenomenon reflects neuromuscular control of the lumbar spine, but its response to prolonged cycling under physiologically individualized conditions remains unclear. Thirty-one pain-free road cyclists completed a laboratory protocol in which exercise intensity was prescribed at 50% of the range between the first and second ventilatory thresholds (VT1 and VT2). Surface electromyography (sEMG) was recorded during trunk flexion extension tasks performed before and after a 60 min cycling trial. FR responses were characterized at both the individual and group levels using the flexion–relaxation ratio (FRR), descriptive classification of altered patterns, and exploratory estimates of mean change, effect size, and 95% confidence intervals. Four cyclists (12.9%; 95% CI: 3.6–29.8%) exhibited altered FR responses: three showed persistent alterations already present before cycling, and one showed an exercise-associated alteration. Group-level changes were minimal (effect sizes: −0.20 to 0.04). These findings suggest that prolonged cycling under controlled physiological load primarily reveals heterogeneous subject-specific neuromuscular patterns rather than a uniform average response. FR assessment using sEMG may therefore be useful as a complementary tool for identifying individual neuromuscular behavior in pain-free cyclists. Full article

The synergistic integration of Hyperspectral Imaging (HSI) and Light Detection and Ranging (LiDAR) data has become a pivotal strategy in remote sensing for precise land-cover classification. However, existing multimodal deep learning frameworks frequently suffer from intrinsic limitations, including rigid feature extraction protocols, underutilization of LiDAR-derived textural information, and asymmetric fusion mechanisms that fail to balance the contribution of spectral and elevation features effectively. To address these challenges, this paper proposes a novel framework named DGC-BCAF, which integrates Dynamic Group Convolution and Bidirectional Guided Cross-Attention Fusion to achieve adaptive feature representation and robust cross-modal interaction. First, a Dynamic Group Convolution (DGConv) module embedded within a ResNet18 backbone is designed to function as the central spatial context extractor. Unlike traditional group convolution, this module learns a dynamic relationship matrix to automatically group input channels, thereby facilitating flexible and context-aware feature representation that adapts to complex spatial distributions. Second, to overcome the insufficient exploitation of elevation data, we introduce a dedicated LiDAR texture encoding branch. This branch innovatively fuses Gray-Level Co-occurrence Matrix (GLCM) statistical features with multi-scale convolutional representations, capturing both geometric height information and fine-grained surface textural details that are critical for distinguishing objects with similar elevations. Finally, central to our architecture is the Bidirectional Cross-Attention Fusion (BCAF) module. Unlike standard unidirectional fusion approaches, BCAF employs a LiDAR geometry to guide the selection of salient spectral bands, while simultaneously utilizing spectral signatures to emphasize informative LiDAR channels. This mutual guidance ensures a balanced contribution from both modalities. Extensive experiments conducted on three benchmark datasets—Houston 2013, Trento, and MUUFL—demonstrate that DGC-BCAF consistently outperforms state-of-the-art methods in terms of overall accuracy, average accuracy, and Kappa coefficient. The results confirm that the proposed adaptive grouping and bidirectional guidance strategies significantly improve classification performance, particularly in distinguishing spectrally similar materials and delineating complex urban structures. Full article

Automated recognition of equine–human interaction behaviors from video represents a significant challenge in computational ethology, with critical applications spanning animal welfare assessment, equine-assisted services evaluation, and safety monitoring in equestrian environments. Existing approaches to animal behavior recognition typically focus on single species in isolation, rely solely on facial expression analysis while ignoring full-body posture, or employ flat classification architectures that fail under the severe class imbalances characteristic of naturalistic behavioral datasets. Furthermore, no prior framework integrates simultaneous analysis of both human and equine body language for cross-species interaction classification. This paper presents a novel hierarchical classification framework integrating multi-modal computer vision features to distinguish behavioral states during horse–human encounters. Our methodology employs three complementary feature extraction pipelines: YOLOv8 for spatial relationship modeling, MediaPipe for human postural analysis, and AP-10K for equine body language interpretation. From 28 annotated interaction videos comprising 50,270 temporal samples across five horse breeds, we extract 35 discriminative features capturing proximity dynamics, body orientation, and species-specific behavioral indicators. To address severe class imbalance (18.3:1 ratio between affiliative and avoidant categories), we implement cost-sensitive gradient boosting with automatic class weight optimization within a two-stage hierarchical architecture. The first stage classifies interactions into three parent categories (affiliative, neutral, avoidant) achieving 73.2% balanced accuracy, while stage two discriminates six fine-grained sub-behaviors achieving 88.5% balanced accuracy (under oracle parent-category routing; cascaded end-to-end performance is 62.9% balanced accuracy due to Stage 1 error propagation, identifying parent classification as the primary bottleneck). Notably, our system achieves 85.0% recall on safety-critical avoidant behaviors despite their representation of only 3.8% of the dataset. Extensive ablation studies demonstrate that equine pose features contribute most critically to classification performance, while comprehensive cross-validation analysis confirms model robustness across diverse interaction contexts. The proposed framework establishes the first systematic multimodal cross-species behavioral assessment pipeline in human–animal interaction research, with direct implications for improving equine welfare monitoring and rider safety protocols. Full article

Background: Gait analysis offers a comprehensive assessment of locomotion and postural control, which are often altered in individuals with spinal deformities. After validating a stereophotogrammetric protocol for whole-body kinematics, including spinal motion in healthy subjects, its application to clinical populations is needed to assess its clinical relevance. Patients treated with spinal arthrodesis for scoliosis may show reduced trunk mobility and compensatory gait strategies. Methods: The validated spinal protocol was applied to 10 patients with scoliosis who underwent arthrodesis and 5 healthy controls. For each participant, the range of motion (ROM) of the upper thoracic, lower thoracic, and lumbar districts was computed. Group differences were assessed with the Mann–Whitney U test, and time-normalized angular curves were compared using Statistical Parametric Mapping (SPM1d). Results: In the pathological group, the protocol showed moderate-to-excellent intra- and inter-operator reliability (ICC > 0.594). Compared with controls, patients exhibited a significant reduction in ROM in fused or adjacent districts. SPM analysis identified altered upper thoracic flexion–extension patterns, particularly relative to the lower thoracic segment, throughout the gait cycle. Conclusions: The protocol demonstrated preliminary feasibility and sensitivity in identifying segmental and phase-dependent changes in spinal motion after arthrodesis, indicating that it may serve as a useful tool for exploratory postoperative gait evaluation. Full article

Background/Objectives: MRI-guided neurovascular interventions could benefit from lower-field systems due to reduced magnetic and radiofrequency hazards. However, safety and practical visibility of commonly used neurointerventional devices at 0.55 T remain insufficiently characterized. We evaluated magnetic field interactions, RF-induced heating, and qualitative device visibility in 11 commercially available and commonly used neurovascular devices on a 0.55 T MRI system. Methods: Eleven devices, including stent retrievers, guidewires, catheters, and one embolization implant, were tested at 0.55 T. Magnetostatic interactions were quantified using the American Society for Testing and Materials (ASTM)-guided deflection methods for translational force (ASTM-F2052) and a two-string suspension apparatus for torque (adapted from Stoianovici et al.). RF-induced heating was measured in an in vitro perfused cerebral vessel phantom using a 15 min high-specific absorption rate spin echo sequence under static and flow conditions. Qualitative device visibility was assessed using a turbo spin echo (TSE) and balanced steady-state free precession (bSSFP) imaging on each device individually. Results: Eight of eleven devices passed the translational force test, while three devices (D, E, and G), containing significant ferromagnetic components, failed with deflection angles > 45°. Eight devices passed torque testing, remaining below the critical threshold in all rotation positions; three devices (D, G, and J) failed by exceeding the 54° criterion, including one guidewire and two devices with braided/coiled metallic structures. Under static conditions, RF-induced heating ranged from negligible to 10.4 °C (maximum in device D) and generally decreased under flow; in the flow configuration, temperature rise remained below 2 °C for 6/11 devices. Qualitative imaging performance differed by sequence, with bSSFP enabling improved delineation of device structure (best for devices A, C, and H), whereas devices D, E, F, and J produced extensive signal voids that precluded reliable visualization in both sequences. Overall, three devices satisfied all safety criteria while remaining clearly visible under MRI. Conclusions: Devices that pass safety thresholds at 0.55 T can serve as candidates for further sequence optimization and preclinical workflow development, enabling the design of low-SAR, device-compatible imaging protocols tailored for neurointerventional workflows. These results provide key safety data supporting the feasibility of MR-guided neurovascular procedures at 0.55 T. Full article

Unsupervised domain adaptation (UDA) has been extensively studied for bearing fault diagnosis under multiple operating conditions by mitigating distribution discrepancies across domains. However, in cross-domain imbalanced scenarios, bearing vibration signals are affected by both feature shift and class imbalance. Although a robust decision boundary learned from the source domain is critical for reliable transfer, classifier discriminability and robustness can be degraded by hard samples located near the boundary. As a result, the decision boundary may become ambiguous during adaptation, leading to degraded diagnostic performance in the target domain. To address these issues, a Maximum Margin Local Domain Adaptation (MMLDA) framework is proposed in which a multi-scale convolutional neural network is adopted as the backbone. Three core components are integrated into our framework: first, category-level reweighting to alleviate source-domain class imbalance; second, cross-domain local category alignment to reduce fine-grained feature discrepancies and feature shift; and finally, maximum-margin loss regularization to impose adaptive margin constraints on hard samples for improved decision boundary robustness. To evaluate the proposed method, cross-domain imbalanced transfer tasks under multiple operating conditions were constructed on two public bearing fault datasets, and comparative experiments were conducted. The results under different imbalance protocols demonstrate improved robustness and generalization of MMLDA. Full article