Search Results (1,602)

Background: Patellofemoral pain syndrome (PFPS) is a common musculoskeletal disorder that involves various biomechanical factors, including the altered positioning of the patella, weakness of the lower extremity muscles, delayed activation of the vastus medialis muscle, and excessive pronation of the foot. Although the short- and long-term effects of external support among the recommended conservative treatment methods for PFPS have been examined, there remains a lack of consensus regarding their impacts. This study was conducted to investigate the immediate effects of braces and rigid taping applied to control pain on proprioception, functional status, and balance in patients with PFPS, and to compare these outcomes with normative values obtained from healthy individuals. Methods: The study included 18 patients with PFPS and 18 healthy individuals who met the inclusion criteria. Through randomization of the intervention sequence, patients were evaluated under conditions of rigid taping, support, or without any support. Their pain levels before and after the application were assessed using the Visual Analog Scale; their functional status was evaluated with the Kujala Patellofemoral Scoring, the 10-Step Up Test, and the Squat; their balance performance was measured using the Y-Balance Test and the Single Leg Stance Test; and their proprioception was assessed with the Joint Position Sense Test. Results: It has been determined that rigid taping and bracing have similar effects in the immediate management of pain, proprioception, functional status, and balance issues in patients with PFPS. The interventions were observed to bring patients’ static balance and proprioception parameters closer to the values seen in healthy individuals. Conclusions: Rigid taping and bracing are both effective interventions in the management of PFPS, offering benefits such as pain relief, prevention of proprioceptive deficits, mitigation of balance impairments, and enhancement of functional outcomes. The selection of the most appropriate modality should be based on the individual patient’s characteristics and tolerance levels. Full article

As network attack methods continue to evolve, flooding attacks remain a major threat that causes network paralysis and service disruption. Statically configured systems are particularly vulnerable, as attackers can exploit reconnaissance information to launch large-scale attacks, while conventional defense mechanisms often fail under high-intensity traffic. To address this problem, this paper introduces Moving Target Defense (MTD) within a decentralized framework and proposes a blockchain-based decentralized End Hopping system. The system employs the Practical Byzantine Fault Tolerance (PBFT) consensus protocol for dynamic controller election and incorporates a disaster recovery mechanism, which eliminates single points of failure while ensuring reliable controller transitions and rapid service restoration. Experimental results demonstrate that the proposed system achieves satisfactory performance in terms of availability, effectiveness, and security, providing a practical approach to constructing robust proactive defense networks. Full article

This paper addresses the consensus problem in multi-agent systems via a self-learning control scheme that directly reuses prior control information to accelerate transient coordination while maintaining robustness. I study agents with linear dynamics and external disturbances, and design a lightweight self-learning consensus control law for the distributed consensus domain, formulated as $u_i\left(t\right)=k_1{u}_i(t-\tau )+k_2{s}_i\left(t\right)$ with learning intensity $k_1$ and learning interval $\tau$. I provide a Lyapunov-based stability proof showing uniform ultimate boundedness of the consensus error under bounded disturbances. Compared to non-learning consensus laws, the proposed strategy achieves faster agreement with reduced long-term effort and retains simplicity suitable for resource-constrained multi-agent platforms, while also achieving decent performance against external disturbances. Simulations validate the improved transient speed and steady accuracy. The full-version-source code is open-sourced. Full article

Background: Posterior cruciate ligament reconstruction (PCLR) remains one of the most technically demanding procedures in knee ligament surgery, with complication rates considerably higher than those observed for other arthroscopic procedures. Residual laxity, arthrofibrosis, neurovascular injury, tunnel-related complications, and heterotopic ossification (HO) represent the most frequent adverse events. With increasing surgical volumes and complexity—particularly in multiligament knee injuries (MLKIs)—structured, evidence-based strategies for complication avoidance are essential. The objective of this review is to provide a comprehensive, evidence-based overview of the main complications associated with PCLR and to propose a structured, reproducible protocol for complication prevention integrating current literature and high-volume institutional experience. Methods: A narrative review of the literature was conducted using PubMed and Google Scholar to identify clinical, biomechanical, and systematic studies on PCLR complications published between 2010 and 2025. Overall, 58 studies were screened and 33 were included for qualitative synthesis. Among the included studies, the level of evidence was Level I in five systematic reviews/meta-analyses, Level III–IV in seven observational clinical studies and registries, and Level V in biomechanical studies, narrative reviews, and expert consensus reports. In parallel, the recommendations were informed by the cumulative experience of a high-volume tertiary referral center with 187 PCLR procedures performed between 2010 and 2025 (136 MLKI, 51 isolated). Results: Evidence identifies several key predictors of postoperative complications: low posterior tibial slope (<6.54°), small graft diameter (<7.0 mm), untreated posterolateral corner insufficiency, excessive tibial tunnel angle, and surgical trauma at the “killer turn.” Neurovascular complications primarily arise during tibial tunnel instrumentation, with knee hyperflexion (>90°) significantly improving safety. Suture tape augmentation (STA) reduces graft elongation by 45–58% and is associated with improved biomechanical stability without increasing complication rates. Early controlled motion is critical to prevent arthrofibrosis, whereas HO—affecting up to 45% of MLKI patients—requires delayed surgical excision after maturation. Conclusions: Optimal outcomes after PCLR derive from a structured, complication-focused approach encompassing anatomical risk assessment, meticulous tunnel planning, neurovascular protection, biological augmentation, and disciplined postoperative rehabilitation. Adoption of standardized protocols—particularly in MLKIs—can substantially reduce the incidence of adverse events and improve long-term knee stability. Full article

This study introduces a novel robust finite-time adaptive sliding mode control (FTSMC) strategy, emphasizing its contributions to the synchronized deployment of hydraulically actuated multi-arm systems in confined environments, such as coal bunker cleaning. Key innovations include the integration of adaptive sliding mode control with guaranteed finite-time convergence, a distributed leader–follower framework, and a graph-theoretical communication topology for localized interactions. Specifically, we developed a dynamic model for a multi-agent system comprising one leader and multiple followers, incorporating nonlinear dynamics and unknown external disturbances. The proposed controller ensures rapid finite-time convergence of tracking errors while maintaining robustness against parameter uncertainties, frictional forces, and external perturbations. The theoretical analysis, based on Lyapunov stability, rigorously proves the boundedness and convergence of all system states. Simulation results on a three-arm robotic platform validate the method’s superiority, demonstrating higher tracking accuracy, faster convergence, and stronger disturbance rejection compared with baseline controllers, including SMC, ETASMC, PID, Fixed-Time Consensus Control (FTCC), Disturbance Observer-Based Control (DOBC), and Adaptive Sliding Mode Control (ASMC). This research provides a practical and scalable solution for multi-arm coordination in unstructured environments, significantly advancing the autonomy and reliability of industrial robotic systems. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article

This paper investigates the event-triggered secure consensus problem for stochastic multi-agent systems (MASs) subject to bilateral false data injection attacks (FDIAs). To achieve reliable secure consensus while reducing resource consumption, an event-triggered defense scheme incorporated with a configurable waiting period is proposed. By introducing an adjustable time interval between consecutive trigger events, the developed scheme not only rigorously eliminates Zeno behavior but also alleviates the computational and sensing burdens. Notably, the analysis of event-triggered secure consensus for stochastic MASs is more challenging compared to conventional deterministic scenarios, due to the coupling effects of stochastic disturbances, event-triggered mechanisms, and bilateral FDIAs. To address this critical challenge, a stochastic convergence theorem is adopted in this study. Distinct from the traditional Lyapunov theorem for stochastic stability analysis, this theorem exhibits inherent similarities to the deterministic Barbalat lemma, which offers a more flexible analytical framework. A key advantage of the proposed approach is that it relaxes the positive definiteness constraint on the candidate Lyapunov function, thereby significantly enhancing the flexibility in constructing Lyapunov functions for stochastic MASs under bilateral FDIAs. Finally, two numerical simulation examples are presented to verify the correctness and effectiveness of the proposed control protocol and key theoretical results. Full article

This paper investigates the fully distributed observer-based dynamic double-event-triggered bipartite consensus tracking problem of fractional-order multi-agent systems (FOMASs) with input saturation under a directed graph. First, to address this complex challenge, a pull-based dynamic double-event-triggered mechanism (DDETM) with different event-triggered conditions and capable of operating independently is designed, which can effectively reduce communication costs and controller updates concurrently. Then, the low-gain feedback technique is used to solve the input saturation problem faced by FOMASs under a directed graph. Based on the estimated state information, a fully distributed control protocol with pull-based DDETM is proposed to ensure the achievement of bipartite consensus tracking for FOMASs. A noteworthy feature of this control protocol is its ability to achieve system stability without the need for global information. Correspondingly, the sufficient conditions for achieving bipartite consensus is obtained with the help of low gain feedback technology and Lyapunov stability theory. Moreover, the Zeno behavior is precluded. Finally, a simulation example is presented to illustrate the theoretical results. Full article

Background/Objectives: Cervical cancer remains a leading cause of cancer morbidity and mortality worldwide. Although chemoradiation followed by brachytherapy is the curative-intent standard for locally advanced disease, outcomes remain heterogeneous and recurrence and distant metastasis persist. In parallel, immune checkpoint inhibitors (ICIs) and antibody–drug conjugates (ADCs) have expanded systemic options in recurrent or metastatic settings and created new opportunities for multimodality. This review aims to integrate treatment-relevant cervical cancer biology and biomarkers to clarify how chemoradiation, immunotherapy, and ADCs can be optimally selected, sequenced, and combined across disease states. Methods: We conducted a structured narrative, evidence-based literature synthesis focusing on cervical cancer management. The review encompassed: (i) the molecular and immune mechanisms underlying human papillomavirus (HPV)-driven carcinogenesis; (ii) contemporary diagnostic and staging approaches, including advanced imaging modalities and histopathological evaluation; and (iii) clinical and translational evidence supporting the optimization of chemoradiation, immune checkpoint inhibition, and antibody–drug conjugates, with emphasis on clinically validated or emerging biomarkers that are relevant to patient stratification and mechanistically rational combination or sequencing strategies. A systematic search of PubMed/MEDLINE, Embase, and major oncology conference proceedings was performed. Priority was given to peer-reviewed original research articles, high-impact clinical trials (Phase II–III), meta-analyses, and consensus guidelines published within the past 10 years to ensure contemporary relevance. Articles published prior to this period were generally excluded to maintain clinical currency; however, seminal studies that established foundational therapeutic standards, mechanistic paradigms, or landmark treatment milestones were intentionally retained due to their enduring influence on current practice. Exclusion criteria included non-peer-reviewed sources, case reports with limited generalizability, non-English publications, and studies lacking methodological rigor or clinical relevance to cervical cancer management. Preclinical studies were included selectively when directly informing therapeutic mechanisms, biomarker development, or translational rationale. This approach was designed to balance historical context with up-to-date clinical applicability, ensuring both scientific rigor and contemporary relevance. Results: Chemoradiation and brachytherapy remain essential for local control, while ICIs can restore antitumor T-cell activity in biomarker-enriched contexts. ADCs enable target-directed delivery of potent cytotoxins and may promote immunogenic cell death, supporting immunotherapy and radiation. However, key challenges include resistance mechanisms, toxicity management, and patient identification for the most beneficial combined multimodality. Conclusions: A biology- and biomarker-informed framework can guide more rational integration of multimodality therapy in cervical cancer. Future progress will depend on validated predictive biomarkers, optimized sequencing/combination strategies, and trials that balance efficacy with short- and long-term toxicity. Full article

Perioperative management of antithrombotic therapy in oral surgery represents an evolving paradigm. This critical review evaluates the contemporary scientific evidence that challenges the conventional practice of routinely discontinuing anticoagulants and/or antiplatelet agents to prevent postoperative bleeding. The traditional strategy carries an unacceptable risk of iatrogenic, sometimes severe, thromboembolic events. The aim of this systematic narrative review is to synthesize the current evidence (2015–2025) and to outline a new, patient-centered clinical framework that dynamically balances thromboembolic and hemorrhagic risks. Materials and methods: A systematic search of major databases (PubMed/MEDLINE, Scopus, Web of Science) identified relevant studies, structured according to the PICO framework. The search strategy prioritized high-level evidence, including clinical guidelines, systematic reviews, meta-analyses, randomized controlled trials, and prospective cohort studies published between January 2015 and November 2025. Results: The results reinforce an emerging consensus: the basis of safe management is the rigorous application of advanced local hemostasis techniques (e.g., prioritizing resorbable materials, sutures, topical hemostatic agents, and antifibrinolytics) and the use of perioperative decision-making algorithms. These measures allow, in most routine dental surgical procedures, the safe continuation of antithrombotic therapy, thus minimizing the thromboembolic risk without significantly increasing the risk of clinically significant bleeding. In the future, research should focus on optimizing materials science (novel biomaterials and controlled-release systems) and on standardizing and validating protocols in specific populations (e.g., patients on combination therapy or at extreme cardiovascular risk). This review argues that the adoption of this evidence-based model, with local hemostasis as a critical pillar, is essential for modern, ethical, and safe dental practice. Full article

Centralized agricultural data platforms raise concerns about ownership, provenance, and vendor lock-in, motivating decentralized alternatives. This study evaluates BigchainDB as a blockchain-database hybrid for owner-controlled precision agriculture data sharing. We address three research questions: (1) functional feasibility for data integrity, access control, and heterogeneous sensor integration; (2) integration patterns bridging IoT ingestion with blockchain consensus; and (3) operational trade-offs versus centralized alternatives. A proof-of-concept implementation comprising a sensor simulator, FastAPI middleware, and three-node BigchainDB cluster demonstrates end-to-end data flow with cryptographic provenance. Key contributions include the following: identification of three integration patterns (message queue buffering for high-throughput ingestion, hierarchical asset modeling, and dual-key access control); comparative analysis against five blockchain-database alternatives; and characterization of deployment complexity. Results show BigchainDB satisfies the functional requirements for data integrity and access control, while requiring increased operational overhead compared to single-node databases. The architecture is viable when multi-party governance outweighs operational simplicity, though production deployments require further scalability validation, including detailed performance benchmarking. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

A growing body of literature argues in favor of the influence of organizational culture (OC) on firm performance (FP). Yet this consensus often emanates from studies that over-emphasize the direct culture–performance relationship, with methodologies that are deficient in revealing causal mechanisms and prone to giving ambiguous results. To address these gaps, this study proposes and tests an integrated theoretical framework, synthesizing the Schema Theory, Resource-Based View/Capability theory, and Contingency Theory of Firm Performance. This framework establishes a foundational influence mechanism of OC on performance, moving from cognitive schemas to actualized capabilities and environmental fit. Using data from 249 construction firms in Ghana, we employed a three-stage analytical process; using cluster analysis, we identified five cultural clusters, dominated by Clan and Adhocracy culture types (Organic cultures). Cross-tabulation revealed that large and resource-rich firms (D1K1 and D2K2) were more likely to exhibit balanced cultural profiles. Initial analysis using Kruskal–Wallis H Test showed no significant performance difference between balanced and organic clusters. However, when multiple regression was employed to control for firm classification and adverse industry conditions, the Balanced Culture profile emerged as a statistically significant predictor of superior performance. Consequently, we argue that while an Appropriate Culture, one dominated by organic traits and values, provides survival in a challenged environment, the Balanced Culture profile serves as a critical enabler of superior firm performance, once resource constraints and industry stressors are neutralized. Our findings hold particular importance for international–local joint ventures, where cultural alignment is a critical success factor. Additionally, the proposed framework establishes a robust theoretical foundation for future studies, especially those conceptualizing organizational culture as a foundational, independent variable. Full article

Vision–language–action (VLA) models often suffer from limited robustness in long-horizon manipulation tasks—where robots must execute extended sequences of actions over multiple time steps to achieve complex goals—due to their inability to explicitly exploit structural symmetries and to react adaptively when such symmetries are violated by environmental uncertainty. To address this limitation, this paper proposes PI-VLA, a symmetry-aware predictive and interactive VLA framework for robust robotic manipulation. PI-VLA is built upon three key symmetry-driven principles. First, a Cognitive–Motor Synergy (CMS) module jointly generates discrete and continuous action chunks together with predictive world-model features in a single forward pass, enforcing cross-modal action consistency as an implicit symmetry constraint across heterogeneous action representations. Second, a unified training objective integrates imitation learning, reinforcement learning, and state prediction, encouraging invariance to task-relevant transformations while enabling adaptive symmetry breaking when long-horizon deviations emerge. Third, an Active Uncertainty-Resolving Decider (AURD) explicitly monitors action consensus discrepancies and state prediction errors as symmetry-breaking signals, dynamically adjusting the execution horizon through closed-loop replanning. Extensive experiments on long-horizon benchmarks demonstrate that PI-VLA achieves state-of-the-art performance, attaining a 73.2% average success rate on the LIBERO benchmark (with particularly strong gains on the Long-Horizon suite) and an 88.3% success rate in real-world manipulation tasks under visual distractions and unseen conditions. Ablation studies confirm that symmetry-aware action consensus and uncertainty-triggered replanning are critical to robust execution. These results establish PI-VLA as a principled framework that leverages symmetry preservation and controlled symmetry breaking to enable reliable and interactive robotic manipulation. Full article

Background: Temporomandibular disorders (TMDs) are common musculoskeletal conditions associated with pain, functional limitation, and reduced quality of life (QoL). Despite the widespread use of conservative and minimally invasive treatments, the available evidence remains fragmented across heterogeneous interventions, diagnostic criteria, and outcome measures, limiting comparative interpretation and clinical applicability. Objectives: The primary objective of this systematic review is to evaluate the effectiveness of conservative and minimally invasive interventions for pain reduction in adult patients with temporomandibular disorders. Secondary objectives include assessing effects on mandibular function and QoL and exploring differences across intervention categories, TMD subtypes, diagnostic criteria, and follow-up durations. Methods: This protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251250251) and adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A systematic search will be conducted in PubMed/MEDLINE, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) for randomized controlled trials (RCTs) published from 1 January 2015, up to the date of study initiation, using controlled vocabulary terms and free-text keywords combined with Boolean operators. Eligible studies will include adult patients (≥18 years) diagnosed with temporomandibular disorders using validated diagnostic criteria and treated with conservative or minimally invasive interventions, compared with placebo/sham, no treatment or usual care, or active comparators, in accordance with the PICOS framework. Two reviewers will independently screen studies and extract data, with disagreements resolved by consensus or consultation with a third reviewer; the study selection process will be documented using a PRISMA 2020 flow diagram. Interventions will be synthesized within predefined clusters (e.g., physical and manual therapies, occlusal splint therapy, physical agent modalities, and minimally invasive joint procedures). Risk of bias will be assessed using the revised Cochrane Risk of Bias tool (RoB 2). The primary outcome will be pain intensity, while secondary outcomes will include mandibular function and QoL. Where appropriate, meta-analysis using a random-effects model will be performed; otherwise, a structured narrative synthesis will be provided. Expected Impact: The systematic review is expected to deliver an updated and methodologically rigorous synthesis of evidence on conservative and minimally invasive interventions for TMDs. By addressing existing research gaps such as the fragmentation of evidence across intervention types, heterogeneity in diagnostic criteria, and variability in outcome measures, this review will support evidence-based clinical decision-making and identify priorities for future research. Full article