Search Results (1,352)

Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems. Full article

Background: A rapid intervention team is a broad category of special teams used by police and emergency respondents to cover various needs. It is essential to ensure the safety and well-being of people in emergencies, minimising the risk of harm and maximising the chances of survival. Objective: This study aimed (i) to identify the fitness profiles and levels of POs from the EIR of the Lisbon Metropolitan Command (COMETLIS, PSP, Portugal), considering age classes; (ii) to directly compare the observed fitness profiles to previous research and normative data; and (iii) to compare the fitness profile of POs from the EIR with cadets from the Police Academy. Methods: This cross-sectional observational study included the participation of 121 male POs from the EIR of the Lisbon Metropolitan Command (Portugal) and 92 male cadets from the Police Academy (Lisbon, Portugal). The assessment protocol sequence involved the collection of biosocial data (age classes: ≤29 years; 30–39 years; 40–49 years), a body size assessment, and a fitness assessment (horizontal jump, handgrip strength, 60 s sit-ups and 20 m shuttle run). Results: (i) In the ≤29 years age class, POs performed better in all fitness tests (highlighting that the age class had a statistically significant effect on performance in the horizontal jump, sit-ups, 20 m shuttle run, and predicted VO₂max), and they showed significantly better performance than cadets in handgrip (left, right, and sum), and significantly worse performance in sit-ups and predicted VO₂max. (ii) In the 30–39 years age class, POs had significantly worse performance than cadets in the horizontal jump, sit-ups, 20 m shuttle run, and predicted VO₂max, even after controlling for age. Conclusions: (i) The fitness performance decreased as the age class became older; (ii) the handgrip strength and cardiovascular capacity attributes were between the standard and excellent levels according to the ACSM guidelines for the general population; (iii) POs from the EIR were stronger than cadets in terms of handgrip strength but weaker in terms of lower limb power, abdominal muscular endurance, and aerobic capacity; and (iv) the differences observed between POs from the EIR and cadets in the 30–39 years age class emphasise the importance of physical training after the training period and throughout professional life. Full article

Mobility limitations due to congenital disabilities, accidents, or illnesses pose significant challenges to the daily lives of individuals with disabilities. This study presents a novel design for a multifunctional intelligent vehicle, integrating head recognition, eye-tracking, Bluetooth control, and ultrasonic obstacle avoidance to offer an innovative mobility solution. The smart vehicle supports three driving modes: (1) a nostril-based control system using MediaPipe to track displacement for movement commands, (2) an eye-tracking control system based on the Viola–Jones algorithm processed via an Arduino Nano board, and (3) a Bluetooth-assisted mode for caregiver intervention. Additionally, an ultrasonic sensor system ensures real-time obstacle detection and avoidance, enhancing user safety. Extensive experimental evaluations were conducted to validate the effectiveness of the system. The results indicate that the proposed vehicle achieves an 85% accuracy in nostril tracking, over 90% precision in eye direction detection, and efficient obstacle avoidance within a 1 m range. These findings demonstrate the robustness and reliability of the system in real-world applications. Compared to existing assistive mobility solutions, this vehicle offers non-invasive, cost-effective, and adaptable control mechanisms that cater to a diverse range of disabilities. By enhancing accessibility and promoting user independence, this research contributes to the development of inclusive mobility solutions for disabled and elderly individuals. Full article

Background: China’s pharmaceutical regulatory framework is undergoing a pivotal shift from a traditional “command-control” model to a “lifecycle regulation” approach, aiming to balance drug safety, innovation, and accessibility. This study systematically examines the evolution, achievements, and challenges of China’s regulatory reforms, offering insights for global pharmaceutical governance. Methods: Using a mixed-methods approach integrating historical analysis, policy text mining, and case studies, we reviewed the pharmaceutical laws and regulations enacted since 1949, supplemented by case studies (e.g., COVID-19 vaccine emergency approvals) and a comparative analysis with international models (e.g., U.S. FDA and EU EMA frameworks). The data were sourced from authoritative platforms such as the PKULAW database, criminal law amendments, and international regulatory texts. Results: China’s regulatory evolution is categorized into four phases: Emergence (1949–1984), Foundational (1985–2000), Deepening Reform (2001–2018), and Lifecycle Regulation (2019–present). The revised Drug Administration Law (2019) institutionalized risk management, dynamic GMP inspections, and post-market surveillance, marking a transition to holistic lifecycle oversight. Key milestones include the introduction of the Vaccine Management Law (2019) and stricter penalties under the Criminal Law Amendment (XI) (2020). Conclusions: China’s lifecycle regulation model demonstrates potential to harmonize safety and innovation, evidenced by improved API export compliance (e.g., 15% increase in international certifications by 2023) and accelerated approvals for breakthrough therapies (e.g., domestically developed PD-1 inhibitors). However, challenges persist, including uneven enforcement capacities, tensions between conditional approvals and risk mitigation, and reliance on global supply chains. These findings provide critical lessons for developing countries navigating similar regulatory dilemmas. Full article

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article

This study introduces an integrated lower limb robotic orthosis with near-field electrospinning (NFES) piezoelectric sensors and a fuzzy logic-based gait phase detection system to enhance mobility assistance and rehabilitation. The exoskeleton incorporates embedded pressure sensors within the insoles to capture ground reaction forces (GRFs) in real-time. A fuzzy logic inference system processes these signals, classifying gait phases such as stance, initial contact, mid-stance, and pre-swing. The NFES technique enables the fabrication of highly oriented nanofibers, improving sensor sensitivity and reliability. The system employs a master–slave control framework. A Texas Instruments (TI) TMS320F28069 microcontroller (Texas Instruments, Dallas, TX, USA) processes gait data and transmits actuation commands to motors and harmonic drives at the hip and knee joints. The control strategy follows a three-loop methodology, ensuring stable operation. Experimental validation assesses the system’s accuracy under various conditions, including no-load and loaded scenarios. Results demonstrate that the exoskeleton accurately detects gait phases, achieving a maximum tracking error of 4.23% in an 8-s gait cycle under no-load conditions and 4.34% when tested with a 68 kg user. Faster motion cycles introduce a maximum error of 6.79% for a 3-s gait cycle, confirming the system’s adaptability to dynamic walking conditions. These findings highlight the effectiveness of the developed exoskeleton in interpreting human motion intentions, positioning it as a promising solution for wearable rehabilitation and mobility assistance. Full article

Background. The SARS-CoV-2 pandemic had a significant impact on sexual health and human behavior, revealing a widespread decline in sexual function and behaviors. Objective. To summarize these findings and highlight their importance for public health, this article discusses the changes observed in sexual function and behavior during the pandemic, as well as potential explanations for these trends. Methods. This study followed the PRISMA-ScR guidelines, using the keyword search commands: “sexual function” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) and “sexual behavior*” AND (“SARS-CoV-2” OR “COVID-19” OR coronavirus) in the Scopus and PubMed databases. The search was conducted on 10 March 2024, including articles published from January 2019 to March 2024. Inclusion criteria required studies focusing on sexual health/function during the SARS-CoV-2 pandemic, excluding non-English articles and non-adult populations. Studies were screened based on relevance, methodological rigor, and sample size, with data extraction focusing on sexual behavior/function metrics. Results were synthesized to identify trends and propose explanatory models. Results. While some individuals experienced reductions in sexual desire and activities, others reported increases, indicating varied individual responses to stressors such as a pandemic. Two hypotheses are presented to explain these changes: terror management theory and the dual control model of sexual response. The critical role of public health in addressing sexual health and well-being needs during a health crisis is discussed, emphasizing the importance of providing clear information, ensuring access to remote sexual health services, and reducing stigma. The need to integrate sexual health into the global response to future health crises is highlighted to ensure a comprehensive approach to human well-being. Conclusions. This review shows the multifaceted impact of the pandemic and social distancing in people’s sexual function and behaviors, underscoring the importance of considering sexual health as an integral part of the emergency health planning and response, to promote the physical and mental well-being of the population during crises such as the SARS-CoV-2 pandemic. Full article

The solution to the problem of insufficient accuracy in determining the position and speed of human movement during interaction with a treadmill-based training complex is considered. Control command generation based on the training complex user’s actions may be performed with a delay, may not take into account the specificity of movements, or be inaccurate due to the error of the initial data. The article introduces a technology for improving the accuracy of predicting a person’s position and speed on a running platform using machine learning and computer vision methods. The proposed technology includes analysing and processing data from the tracking system, developing machine learning models to improve the quality of the raw data, predicting the position and speed of human movement, and implementing and integrating neural network methods into the running platform control system. Experimental results demonstrate that the decision tree (DT) model provides better accuracy and performance in solving the problem of positioning key points of a human model in complex conditions with overlapping limbs. For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. Prediction of the person’s position (based on 10 previous frames) is performed using the DT model, which is optimal in terms of accuracy and computation time relative to other options. The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. The results of the research confirmed the achievement of the primary objective (increasing the accuracy of human position and speed prediction), making the proposed technology promising for application in human-machine systems. Full article

This paper proposes an improved Maximum Torque Per Ampere (MTPA) control method based on The Direct Torque Control-Space Vector Modulation (DTC-SVM) control algorithm using d-axis flux optimization. The proposed algorithm simplifies the existing DTC-SVM control method by geometrically interpreting its complex equations thereby providing a more straightforward and efficient approach. The proposed algorithm geometrically computes the d-axis flux reference and compensation values for the MTPA control by continuously monitoring the q-axis flux in real time. Additionally, the compensation value of the d-axis flux reference is employed to compute the magnitude and phase reference values of the DTC-SVM voltage vector, which in turn generates the stator current values that align with the MTPA curve. The effectiveness of the proposed algorithm was validated through simulation results in MATLAB Simulink. When the proposed algorithm was applied, the torque response to the torque command improved compared to the DTC-SVM control. Additionally, for the same torque production, the stator current consumption of the IPMSM was reduced by approximately 12.55%, demonstrating improved efficiency. To further validate the effectiveness of the proposed algorithm, a dynamometer test system was established, and the IPMSM was tested across various speed ranges below the base speed while generating different torque outputs. The torque response dynamics and stator current consumption of the proposed algorithm were then compared with those of the DTC-SVM algorithm, confirming its enhanced performance. Full article

Unmanned aerial vehicles must achieve precise flight maneuvers despite disturbances, parametric uncertainties, modeling inaccuracies, and limitations in onboard sensor information. This paper presents a robust adaptive control for trajectory tracking under nonlinear disturbances. Firstly, parametric and modeling uncertainties are addressed using model reference adaptive control principles to ensure that the dynamics of the aerial vehicle closely follow a reference model. To address the effects of disturbances, a modified nonlinear disturbance observer is designed based on estimated state variables. This observer effectively attenuates constant, nonlinear disturbances with variable frequency and magnitude, and noises. In the next step, a two-stage sliding mode control strategy is introduced, incorporating adaptive laws and a commanded-filter to compute numerical derivatives of the state variables required for control design. An error compensator is integrated into the framework to reduce numerical and computational delays. To address sensor inaccuracies and potential failures, a high-gain observer-based state estimation technique is employed, utilizing the separation principle to incorporate estimated state variables into the control design. Finally, Lyapunov-based stability analysis demonstrates that the system is uniformly ultimately bounded. Numerical simulations on a DJI F450 quadrotor validate the approach’s effectiveness in achieving robust trajectory tracking under disturbances. Full article

This study deals with the path-following guidance of a fixed-wing unmanned aerial system (UAS) in conjunction with parameter adaptation. Utilizing a backstepping control design approach, a path-following control algorithm is formulated for the roll command, accounting for the approximated closed-loop roll control. The inaccurate time constant is estimated by employing a parameter adaptation algorithm. The proposed guidance algorithm is first validated via the hardware-in-the-loop simulation environment, followed by flight tests on an actual UAV platform to demonstrate that both tracking performance and control robustness are improved over various shape of reference paths. Full article

Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose a novel prescribed-time command filtered backstepping approach to effectively tackle the issues of complexity explosion and singularities. By utilizing a state-transition function, we manage asymmetric time-varying state constraints, including limitations on speed, roll, yaw, and pitch angles in UAVs. The theoretical analysis demonstrates that all signals in the 6-DOF UAV system remain bounded, with tracking errors converging to the origin within the prescribed time. Finally, simulation results validate the effectiveness of the proposed control strategy. Full article

This study aimed to develop a real-time localization system for an AMR (autonomous mobile robot), which utilizes the Robot Operating System (ROS) Noetic version in the Ubuntu 20.04 operating system. RTAB-MAP (Real-Time Appearance-Based Mapping) is employed for localization, integrating with an RGB-D camera and a 2D LiDAR for real-time localization and mapping. The navigation was performed using the A* algorithm for global path planning, combined with the Dynamic Window Approach (DWA) for local path planning. It enables the AMR to receive velocity control commands and complete the navigation task. RTAB-MAP is a graph-based visual SLAM method that combines closed-loop detection and the graph optimization algorithm. The maps built using these three methods were evaluated with RTAB-MAP localization and AMCL (Adaptive Monte Carlo Localization) in a high-similarity long corridor environment. For RTAB-MAP and AMCL methods, three map optimization methods, i.e., TORO (Tree-based Network Optimizer), g2o (General Graph Optimization), and GTSAM (Georgia Tech Smoothing and Mapping), were used for the graph optimization of the RTAB-MAP and AMCL methods. Finally, the TORO, g2o, and GTSAM methods were compared to test the accuracy of localization for a long corridor according to the RGB-D camera and the 2D LiDAR. Full article

Velocity saturation constraints are a significant issue for wheeled mobile robots (WMRs) when designing kinematics-based control laws. To handle the problem of velocity saturation constraints, a novel monocular visual servoing controller is developed for WMRs to solve tracking problems and enable unknown depth estimation. By analyzing the kinematic model of the robot system and employing the homography decomposition technique, measurable signals are obtained to develop a visual tracking error model for non-holonomic mobile robots. To ensure that the velocity commands are consistently constrained within the allowed limits, a saturation function is employed in the designed visual servoing control law. Furthermore, an adaptive updating law is designed to estimate the unknown depth information. The boundedness of the velocity commands is analyzed to evaluate the saturation performance of the developed visual servoing controller. With the aid of Lyapunov techniques and Barbalat’s lemma, the stability of this scheme is demonstrated. The simulation and experiment verify the performance of the proposed method. Full article

Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according to the force tracking error. However, the low-level motion tracking error is generally ignored completely. Recognizing this limitation, this paper first formulates the low-level motion tracking error as an unknown input disturbance, based on which a dynamic interaction model capturing both structured and unstructured uncertainties is developed. With the developed interaction model, an observer-based adaptive robust force controller is proposed to achieve accurate and robust force modulation for a robotic manipulator. Alongside the theoretical stability analysis, comparative experiments with the classical admittance control (AC), the adaptive variable impedance control (AVIC), and the adaptive force tracking admittance control based on disturbance observer (AFTAC) are conducted on a robotic manipulator across four scenarios. The experimental results demonstrate the significant advantages of the proposed approach over existing methods in terms of accuracy and robustness in interaction force control. For instance, the proposed method reduces the root mean square error (RMSE) by $91.3\%$, $87.2\%$, and $75.5\%$ in comparison to AC, AVIC, and AFTAC, respectively, in the experimental scenario where the manipulator is directed to follow a time-varying force while experiencing significant low-level motion tracking errors. Full article