Search Results (93)

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

Background: Children with autism spectrum disorder (ASD) commonly experience persistent difficulties in social communication, emotional regulation, and social engagement. In recent years, artificial intelligence (AI)-based technologies, particularly socially assistive robots and intelligent sensing systems, have been explored as complementary tools to support psychosocial interventions in this population. Objective: This systematic review aimed to critically evaluate recent evidence on the effectiveness of AI-based interventions in improving social, emotional, and cognitive functioning in children with ASD. Methods: A systematic literature search was conducted in PubMed following PRISMA guidelines, targeting English-language studies published between 2020 and 2025. Eligible studies involved children with ASD and implemented AI-driven tools within therapeutic or educational settings. Eight studies met inclusion criteria and were analyzed using the PICO framework. Results: The reviewed interventions included humanoid and non-humanoid robots, gaze-tracking systems, and theory of mind-oriented applications. Across studies, AI-based interventions were associated with improvements in joint attention, social communication and reciprocity, emotion recognition and regulation, theory of mind, and task engagement. Outcomes were assessed using standardized behavioral measures, observational coding, parent or therapist reports, and physiological or sensor-based indices. However, the studies were characterized by small and heterogeneous samples, short intervention durations, and variability in outcome measures. Conclusions: Current evidence suggests that AI-based systems may serve as valuable adjuncts to conventional interventions for children with ASD, particularly for supporting structured social and emotional skill development. Nonetheless, methodological limitations and limited long-term data underscore the need for larger, multi-site trials with standardized protocols to better establish efficacy, generalizability, and ethical integration into clinical practice. Full article

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems. Full article

Attempting to make machines mimic human walking, grasping, balancing, and other behaviors is a deep exploration of cognitive science and biological principles. Due to the existing prediction lag problem, an error compensation mechanism that integrates historical motion data is proposed. By constructing a humanoid autonomous walking control system, this paper aims to use a three-dimensional linear inverted pendulum model to plan the general framework of motion. Firstly, the landing point coordinates of the single foot support period are preset through gait cycle parameters. In addition, it is substituted into dynamic equation to solve the centroid (COM) trajectory curve that conforms to physical constraints. A hierarchical whole-body control architecture is designed, with a task priority based on quadratic programming solver used at the bottom to decompose high-level motion instructions into joint space control variables and fuse sensor data. Furthermore, the numerical iterative algorithm is used to solve the sequence of driving angles for each joint, forming the control input parameters for driving the robot’s motion. This algorithm solves the limitations of traditional inverted pendulum models on vertical motion constraints by optimizing the centroid motion trajectory online. At the same time, it introduces a contact phase sequence prediction mechanism to ensure a smooth transition of the foot trajectory during the switching process. Simulation results demonstrate that the proposed framework improves disturbance rejection capability by over 30% compared to traditional ZMP tracking and achieves a real-time control loop frequency of 1 kHz, confirming its enhanced robustness and computational efficiency. Full article

Data-driven marketing analytics has advanced targeting and optimization, yet its underlying infrastructure now functions as a complex sociotechnical system with overlooked ecological costs. This study conceptualizes programmatic advertising through a systems lens. It introduces the Ecological Delivery Paradox, a structural incongruity where environmentally friendly advertising messages are transmitted via energy-intensive delivery pipelines. Using an interpretivist–abductive design, we conducted 38 in-depth interviews with consumers and professionals, which were analyzed using reflexive thematic analysis in MAXQDA. Results show that awareness of hidden delivery costs emerges through a concretization threshold and crystallizes into metaphors such as “clean message, dirty conduit,” which trigger differentiated cognitive–affective pathways. These pathways shape trust trajectories across four profiles: cliff erosion, slow seep, suspended risk, and resilient cores. System-level moderators, including rationalization buffers, efficiency beliefs, and the visibility of low-data alternatives, determine outcomes. The findings extend marketing systems theory by reframing greenwashing as message–infrastructure misalignment and by integrating delivery congruence into advertising trust models. We propose a data-driven control architecture that aligns predictive analytics with ecological proportionality through mechanisms such as lightweight creatives, carbon-aware bidding coefficients, frequency–data quotas, and ad-level transparency labels. This systemic approach advances legitimacy, audience trust, and sustainability as joint objectives in programmatic advertising. Full article

Multiple sclerosis is an inflammatory and neurodegenerative disease that leads to motor, cognitive, and sensory impairments, significantly affecting walking and quality of life. This study aimed to analyze the relationship between quality of life and kinematic walking parameters in individuals with multiple sclerosis, as well as to evaluate the influence of fatigue, balance, and cognitive performance on different aspects of quality of life. A cross-sectional observational study was conducted with 32 patients diagnosed with multiple sclerosis with Expanded Disability Status Scale scores of ≤5.5. Quality of life was assessed using the MusiQoL questionnaire, and clinical variables included fatigue (Fatigue Scale for Motor and Cognitive Functions, Borg scale), balance (Berg Balance Scale), and cognitive performance (Trail Making Test). Walking kinematics were analyzed using the Vicon motion capture system to obtain walking speed, step frequency, and joint asymmetry indices. Spearman correlations and linear regression models were applied. Results showed significant correlations between quality of life and walking speed (rho = 0.506), step frequency (rho = 0.508), and knee asymmetry (rho = −0.525), as well as strong associations with cognitive fatigue (rho = −0.796) and balance (rho = 0.635). Regression models explained up to 58.4% of the variance in the Activities of Daily Living dimension. These findings indicate that quality of life in multiple sclerosis is influenced by both clinical and biomechanical factors, highlighting the importance of comprehensive assessments to guide physiotherapeutic interventions. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

Rheumatoid arthritis (RA) is a systemic autoimmune disease that, beyond joint destruction, contributes to neuropsychiatric symptoms such as depression, anxiety, and cognitive impairment. These symptoms are often underrecognized despite their major impact on quality of life. Accumulating evidence suggests that pro-inflammatory cytokines, particularly tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), play a key role in this neuroimmune interface. This narrative review examined 16 clinical studies evaluating the effects of biologic therapies targeting TNF-α and IL-6 on mental health outcomes in RA. The total study population comprised 9939 patients, including 2467 treated with TNF-α inhibitors and 7472 with IL-6 or IL-6 receptor inhibitors. TNF-α inhibitors were associated with improved depressive symptoms and emotional well-being. IL-6 inhibitors demonstrated similar psychiatric benefits, particularly in patients with elevated IL-6 levels. The findings highlight that biological therapies in RA may influence not only physical symptoms but also mental health, likely through modulation of neuroimmune pathways including blood–brain barrier permeability, microglial activation, and HPA axis regulation. Future research is needed to clarify these effects in populations stratified by psychiatric comorbidity and inflammatory biomarkers. Clinical implications: Incorporating psychiatric symptom screening and considering neuroinflammatory profiles may help guide the selection of biologic therapy in RA, particularly in patients with comorbid depression or fatigue. Full article

Mucopolysaccharidosis (MPS) type II, or Hunter syndrome, is an X-linked lysosomal storage disorder caused by a deficiency of iduronate-2-sulfatase. Glycosaminoglycan (GAG) accumulation leads to progressive multisystemic involvement, with coarse facial features, hepatosplenomegaly, short stature, recurrent upper respiratory infections, hearing loss, hernias, dysostosis multiplex, joint contractures, and cardiac valve disease. Individuals with the neuronopathic form of the disease also have central nervous system (CNS) involvement with developmental delay and progressive cognitive decline. Enzyme replacement therapy (ERT), idursulfase, is the only FDA-approved treatment for MPS II. MPS II was added to the Recommended Uniform Screening Panel (RUSP) in the United States in 2022, and screening is ongoing in several other countries, including Taiwan. Here, we report seven individuals from four families identified through newborn screening sharing the same IDS variant: c.817C>T, p.Arg273Trp. Confirmatory testing demonstrated low iduronate-2-sulfatase activity level and elevated GAGs in every individual, but they had no signs or symptoms of MPS II. They were aged 8 months to 60 years old according to the most recent assessment and all remained asymptomatic. ERT was not initiated for any of them. Our findings suggest that the IDS c.817C>T variant is associated with abnormal biochemical findings but no clinical phenotype of MPS II. Newborn screening will likely identify additional cases and provide a better understanding of the clinical significance of this variant. Full article

Concurrent cognitive tasks, such as avoiding visual, auditory, chemical, and electrical hazards, and concurrent motor tasks, such as load carriage, are prevalent in ergonomic settings. Trips are extremely common in the workplace, leading to fatal and non-fatal fall-related injuries. Intrinsic factors, such as attention, fatigue, and anticipation, as well as extrinsic factors, including tasks at hand, affect trip recovery responses. Objective: The purpose of this study was to investigate the ankle joint kinematics in unexpected and expected trip responses during single-tasking (ST), dual-tasking (DT), and triple-tasking (TT), before and after a physically fatiguing protocol among young, healthy adults. Methods: Twenty volunteers’ (10 females, one left leg dominant, age 20.35 ± 1.04 years, height 174.83 ± 9.03 cm, mass 73.88 ± 15.55 kg) ankle joint kinematics were assessed using 3D motion capture system during unperturbed gait (NG), unexpected trip (UT), and expected trip (ET), during single-tasking (ST), cognitive dual-tasking (CDT), motor dual-tasking (MDT), and triple-tasking (TT), under both PRE and POST fatigue conditions. Results: Greater dorsiflexion angles were observed during UT compared to NG, MDT compared to ST, and TT compared to ST. Significantly greater plantar flexion angles were observed during ET compared to NG and during POST compared to PRE. Conclusions: Greater dorsiflexion angles during dual- and triple-tasking suggest that divided attention affects trip recovery. Greater plantar flexion angles following fatigue are likely an anticipatory mechanism due to altered muscle activity and increased postural control demands. Full article

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains. Full article

Background/Objectives: Walking coordination is crucial for maintaining independence and quality of life, but it is significantly affected by aging and cognitive decline. This study investigates how age and cognitive status relate to lower limb coordination during gait, using a network-based analysis of joint kinematics. Methods: Fifty-six healthy participants (31–82 years old) underwent gait analysis with a stereophotogrammetric system and cognitive assessment through standardized neuropsychological tests. Kinematic data were processed to build “kinectomes”, representing the inter-joint coordination across the gait cycle. Results: The results showed that the mean lower limb coordination on the sagittal plane negatively correlated with age and positively with cognitive performance. Detailed analysis revealed that age-related declines in coordination were primarily driven by reduced synchronization at the knees, while cognitive status was associated with overall coordination rather than joint-specific changes. Conclusion: These findings emphasize the knees’ critical role in preserving gait coordination with aging and underline the involvement of cognitive aspects in global coordination mechanisms. In summary, our network-based approach provides a refined perspective on gait dynamics, highlighting the relationship between coordination and both age and cognition. Full article

Systemic autoimmune rheumatic diseases (SARDs), such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Sjögren’s syndrome (SS), are traditionally characterized by chronic inflammation and immune-mediated damage to joints and other tissues. However, many patients also experience symptoms such as widespread pain, persistent fatigue, cognitive dysfunction, and autonomic disturbances that cannot be attributed directly or entirely to peripheral inflammation or structural pathology. These conditions suggest the involvement of interactions between the nervous and immune systems, which probably include both peripheral and central components. This review summarizes the current knowledge of neurological and neuroimmune mechanisms that may contribute to these symptoms in SARDs. Glial cell activation and neuroinflammation within the central nervous system (CNS), small-fiber neuropathy (SFN) affecting peripheral nociceptive pathways, central pain sensitization, and autonomic nervous system dysfunction will be discussed. In addition, the role of molecular mediators, including cytokines, neuropeptides, and microRNAs, that could potentially modulate neuroimmune signaling will be highlighted. Integrating findings from pathology, immunology, and neuroscience, this review seeks to provide a useful framework for understanding neuroimmune dysregulation in SARDs. It also highlights the clinical relevance of these mechanisms and summarizes new directions for diagnosis and treatment. Full article

Pedestrian detection is an important research topic in the field of visual cognition and autonomous driving systems. The proposal of the YOLO model has significantly improved the speed and accuracy of detection. To achieve full day detection performance, multimodal YOLO models based on RGB-FIR image pairs have become a research hotspot. Existing work has focused on the design of fusion modules after feature extraction of RGB and FIR branch backbone networks, achieving a multimodal backbone network framework based on back-end fusion. However, these methods overlook the complementarity and prior knowledge between modalities and scales in the front-end raw feature extraction of RGB and FIR branch backbone networks. As a result, the performance of the backend fusion framework largely depends on the representation ability of the raw features of each modality in the front-end. This paper proposes a novel RGB-FIR multimodal backbone network framework based on a cross-modality context attentional model (CCAM). Different from the existing works, a multi-level fusion framework is designed. At the front-end of the RGB-FIR parallel backbone network, the CCAM model is constructed for the raw features of each scale. The RGB-FIR feature fusion results of the lower-level features of the RGB and FIR branch backbone networks are fully utilized to optimize the spatial weight of the upper level RGB and FIR features, to achieve cross-modality and cross-scale complementarity between adjacent scale feature extraction modules. At the back-end of the RGB-FIR parallel network, a channel-space joint attention model (CBAM) and self-attention models are combined to obtain the final RGB-FIR fusion features at each scale for those RGB and FIR features optimized by CCAM. Compared with the current RGB-FIR multimodal YOLO model, comparative experiments on different performance evaluation indicators on multiple RGB-FIR public datasets indicate that this method can significantly enhance the accuracy and robustness of pedestrian detection. Full article

Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system’s performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%. Full article