Search Results (938)

To address the challenges posed by dense shipping traffic and the difficulty of identifying navigation risks in open waters, this paper introduces an Especial Cautious Navigation Angle (ECNA) model for ships, grounded in ship collision avoidance geometry. The ECNA model dynamically identifies the range of navigation angles where collision risks may arise between ships. Building upon this model, a comprehensive scoring algorithm is proposed to assess ship collision risks in open waters. This algorithm not only effectively tracks the evolving risk of collisions but also prioritizes ships with the most imminent danger of collision. Experimental results demonstrate that the ECNA model can accurately define the range of collision risk navigation angles. Furthermore, the scoring algorithm provides a quantitative analysis of the development trends in collision risks and offers continuous monitoring of these risks during navigation in open waters. The proposed model and algorithm exhibit strong practical applicability and operability in identifying ship collision risks in both open and dense navigable areas. These findings not only offer valuable guidance for real-world collision risk identification but also contribute to the theoretical advancement of ship collision risk analysis, presenting a novel solution to this pressing issue. Full article

This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance. Full article

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article

This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS data from an RTK-enabled IMU-GNSS positioning module—both as satellite fixes and internally fused interpolated pose estimates. The tracks are formatted as ROS1 and ROS2 bags, with separately available calibration and ground truth data. In addition to the filtered positioning module outputs, a second form of sparse ground truth pose annotation is provided using independently surveyed visual fiducial markers as a reference. This enables the meaningful evaluation of systems that directly utilize data from the positioning module into their localization estimates, and serves as an alternative when the GNSS reference is disrupted by intermittent signals or multipath scattering. In this paper, we describe the methods used to collect the dataset, its contents, and its intended use. Full article

The design and industrial innovation of intelligent agricultural machinery and equipment for saline alkali land are important means for comprehensive management and capacity improvement of saline alkali land. The autonomous and unmanned agricultural tractor is the inevitable trend of the development of intelligent machinery and equipment in saline alkali land. As an underactuated system with non-holonomic constraints, the independent trajectory planning and lateral stability control of the tractor-trailer system (TTS) face challenges in saline alkali land. In this study, based on the nonlinear underactuation characteristics of the TTS and the law of passive trailer steering, a dual-trajectory collaborative control model was designed. By solving the TTS kinematic/dynamic state space, a nonlinear leading system that can generate the reference pose of a tractor-trailer was constructed. Based on the intrinsic property of the lateral deviation of the TTS, a collaborative trajectory prediction algorithm that satisfies the time domain and system constraints is proposed. Combining the dual-trajectory independent offset and lateral stability parameter of the TTS, an energy function optimization control parameter was constructed to balance the system trajectory tracking performance and lateral control stability. The experimental results showed good agreement between the predicted trailer trajectory and the collaborative control trajectory, with an average lateral error not exceeding 0.1 m and an average course angle error not exceeding 0.054 rad. This ensures that the dynamic controller designed around the tractor-trailer underactuation system can guarantee the smoothness of the trailer trajectory and the controlling stability of the tractor in saline alkali land. Full article

Climatic changes lead to many extreme weather events throughout the globe. These extreme weather events influence our behavior, exposing us to different environmental conditions, such as poor indoor quality. Poor indoor air quality (IAQ) poses a significant concern in the modern era, as people spend up to 90% of their time indoors. Ventilation influences key IAQ elements such as temperature, relative humidity, and particulate matter (PM). Children, considered a vulnerable group, spend approximately 30% of their time in educational settings, often housed in old structures with poorly maintained ventilation systems. Extreme weather events lead young students to stay indoors, usually behind closed doors and windows, which may lead to exposure to elevated levels of air pollutants. In our research, we aim to demonstrate how real-time monitoring of air pollutants and other environmental parameters under extreme weather is important for regulating the indoor environment. A study was conducted in a school building with limited ventilation located in an arid region near the Red Sea, which frequently suffers from high PM concentrations. In this study, we tracked the indoor environmental conditions and air quality during the entire month of May 2022, including an extreme outdoor weather event of sandstorms. During this month, we continuously monitored four classrooms in an elementary school built in 1967 in Eilat. Our findings indicate that PM2.5 was higher indoors (statistically significant) by more than 16% during the extreme event. Temperature was also elevated indoors (statistically significant) by more than 5%. The parameters’ deviation highlights the need for better indoor weather control and ventilation systems, as well as ongoing monitoring in schools to maintain healthy indoor air quality. This also warrants us as we are approaching an era of climatic instability, including higher occurrence of similar extreme events, which urge us to develop real-time responses in urban areas. Full article

DC micro-grids are emerging as a promising solution for efficiently integrating renewable energy into power systems. These systems offer increased flexibility and enhanced energy management, making them ideal for applications such as heat pump (HP) systems. However, the integration of intermittent renewable energy sources with optimal energy management in these micro-grids poses significant challenges. This paper proposes a novel control strategy designed specifically to improve the performance of DC micro-grids. The strategy enhances energy management by leveraging an environmental mission profile that includes real-time measurements for energy generation and heat pump performance evaluation. This micro-grid application for heat pumps integrates photovoltaic (PV) systems, wind generators (WGs), DC-DC converters, and battery energy storage (BS) systems. The proposed control strategy employs an intelligent maximum power point tracking (MPPT) approach that uses optimization algorithms to finely adjust interactions among the subsystems, including renewable energy sources, storage batteries, and the load (heat pump). The main objective of this strategy is to maximize energy production, improve system stability, and reduce operating costs. To achieve this, it considers factors such as heating and cooling demand, power fluctuations from renewable sources, and the MPPT requirements of the PV system. Simulations over one year, based on real meteorological data (average irradiance of 500 W/m², average annual wind speed of 5 m/s, temperatures between 2 and 27 °C), and carried out with Matlab/Simulink R2022a, have shown that the proposed model predictive control (MPC) strategy significantly improves the performance of DC micro-grids, particularly for heat pump applications. This strategy ensures a stable DC bus voltage (±1% around 500 V) and maintains the state of charge (SoC) of batteries between 40% and 78%, extending their service life by 20%. Compared with conventional methods, it improves energy efficiency by 15%, reduces operating costs by 30%, and cuts CO₂; emissions by 25%. By incorporating this control strategy, DC micro-grids offer a sustainable and reliable solution for heat pump applications, contributing to the transition towards a cleaner and more resilient energy system. This approach also opens new possibilities for renewable energy integration into power grids, providing intelligent and efficient energy management at the local level. Full article

Knee joint disorders pose a significant and growing challenge to global healthcare systems. Recent advancements in robotics, sensing technologies, and artificial intelligence have driven the development of robot-assisted therapies, reducing the physical burden on therapists and improving rehabilitation outcomes. This study presents a novel knee exoskeleton designed for safe and adaptive rehabilitation, specifically targeting bed-bound stroke patients to enable early intervention. The exoskeleton comprises a leg splint, thigh splint, and an actuator, incorporating a series elastic actuator (SEA) to enhance torque density and provide intrinsic compliance. A variable impedance control method was also implemented to achieve accurate position tracking of the exoskeleton, and performance tests were conducted with and without human participants. A preliminary clinical study involving two stroke patients demonstrated the exoskeleton’s potential in reducing muscle spasticity, particularly at faster movement velocities. The key contributions of this study include the design of a compact SEA with improved torque density, the development of a knee exoskeleton equipped with a cascaded position controller, and a clinical test validating its effectiveness in alleviating spasticity in stroke patients. This study represents a significant step forward in the application of SEA for robot-assisted rehabilitation, offering a promising approach to the treatment of knee joint disorders. Full article

Symmetry is widely present in science and daily life. And the internal structure of surface-mounted permanent magnet synchronous motors (PMSMs) has good symmetry. This article is dedicated to studying the tracking problem of PMSMs with adaptive and backstepping control methods. The research objective of this study is to design new adaptive controllers Uq and Ud, which enable the state of the motor position servo system to asymptotically and stably track the given signals of the system. They can suppress the impact of changes in B, J, and TL and can also enhance the robustness of the system. (i) The strongly coupled current and speed, variation of parameters over time, and nonlinearity of motor torque objectively pose significant challenges in the design of adaptive tracking controllers for PMSMs. (ii) Adaptive control technology and backstepping control methods are used for designing controllers for the PMSMs. (iii) After rigorous reasoning, an intelligent adaptive tracking control strategy for the PMSMs has been derived, which is for the direct axis current and the angle. (iv) The new adaptive tracking controllers are superior to existing controllers in that they can strongly suppress the disturbance of system parameters J, TL, and B, make the system state asymptotically stable, and achieve good tracking performance for the given signals. The results of the simulation indicate the validity of the designed control strategy. Full article

Since its conceptualization, click chemistry in all its variants has proven to be a superior synthesis protocol, compared to conventional methods, for forming new covalent bonds under mild conditions, orthogonally, and with high yields. If a term like reactive resilience could be established, click reactions would be good examples, as they perform better under increasingly challenging conditions. Particularly, highly hindered couplings that perform poorly with conventional chemistry protocols—such as those used to conjugate biomacromolecules (e.g., proteins and aptamers) or multiple drugs onto macromolecular platforms—can be more easily achieved using click chemistry principles, while also promoting high stereoselectivity in the products. In this review, three molecular platforms relevant in the field of nanomedicine are considered: polymers/copolymers, cyclodextrins, and fullerenes, whose functionalization poses a challenge due to steric hindrance, either from the intrinsic bulk behavior (as in polymers) or from the proximity of confined reactive sites, as seen in cyclodextrins and fullerenes. Their functionalization with biologically active groups (drugs or biomolecules), primarily through copper-catalyzed azide–alkyne cycloaddition (CuAAC), strain-promoted azide–alkyne cycloaddition (SPAAC), inverse electron-demand Diels–Alder (IEDDA) and thiol–ene click reactions, has led to the development of increasingly sophisticated systems with enhanced specificity, multifunctionality, bioavailability, delayed clearance, multi-targeting, selective cytotoxicity, and tracking capabilities—all essential in the field of nanomedicine. Full article

Social determinants of health (SDHs) and the impact of colonization can make Canadian Arctic Indigenous communities susceptible to infectious diseases, including the coronavirus disease 2019 (COVID-19). This scoping review followed the PRISMA guidelines for scoping reviews and studied what is known about selected pandemics (COVID-19, tuberculosis, and H1N1 influenza) and SDHs (healthcare accessibility, food insecurity, mental health, cultural continuity, housing, community infrastructure, and socioeconomic status (SES)) for Canadian Arctic Indigenous communities. Original studies published in English and French up to October 2024 were located in databases (PubMed, Medline, and CINAHL), AlterNative: An International Journal of Indigenous Peoples, and through reference tracking. We included 118 studies: 6 relating to COVID-19, 5 to influenza, 5 to TB, 27 to food insecurity, 26 to healthcare access, 22 to mental health, 9 to SES, 8 to housing, 7 to cultural continuity, and 3 to community infrastructure. SDHs affecting Indigenous individuals include food insecurity, limited healthcare access, mental health challenges, low SES, suboptimal housing, and limited cultural continuity. These findings are relevant to other Arctic regions. It is crucial to understand how SDHs impact the health of Arctic communities and to utilize this information to inform policy and practice decisions for pandemic prevention, management, and treatment. Many SDHs pose challenges for preventing and managing infectious diseases. Full article

Viruses, known for causing widespread biological harm and even extinction, pose significant challenges to public health. Virus detection is crucial for accurate disease diagnosis and preventing the spread of infections. Recently, the outstanding analytical performance of CRISPR/Cas biosensors has shown great potential and they have been considered as augmenting methods for reverse-transcription polymerase chain reaction (RT-PCR), which was the gold standard for nucleic acid detection. We herein utilized Cas12a with universal CRISPR RNA (crRNA) for indiscriminate virus detection by attaching the target to a longer track strand for isothermal amplification. The amplified products contain a domain that is recognized by the Cas12a/crRNA complex, triggering the cleavage of surrounding reporters to produce signals, thereby escaping the target dependence of crRNA recognition. The proposed method allows the same crRNA to detect multiple viral nucleic acids with high sensitivity, including but not limited to SARS-CoV-2, human papillomaviruses (HPV), HCOV-NL63, HCOV-HKU1, and miRNA biomarkers. Taking SARS-CoV-2 and HPV16 pseudoviruses as examples, this method was proved as a versatile and sensitive platform for molecular diagnostic applications. Full article

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains. Full article

The inconsistency in acceleration and deceleration performance between high and low altitudes is a significant challenge in aircraft engine control today. In the past, neither open-loop fuel–air ratio control nor closed-loop N-dot control could resolve this issue perfectly; the difference in acceleration and deceleration performance between high and low altitudes is even more than three times. The operational characteristics of aircraft engines vary significantly between high and low altitudes, posing challenges for transient state control in high-performance aircraft engines. To address these transient performance inconsistencies due to altitude uncertainties, a μ-synthesis adaptive tracking transition control design method with hybrid open-loop and closed-loop direct thrust control is proposed. The main innovation lies in proposing a new segmented hybrid control scheme. Under a high-power state, it employs a dual closed-loop μ-synthesis adaptive tracking framework, using fuel flow to control thrust and nozzle area to control the turbine pressure ratio. In a low-power state, a single-variable closed-loop and open-loop control architecture is applied. Simulation results show that the hybrid open/closed-loop control method can suppress the inconsistency of acceleration and deceleration performance caused by altitude uncertainties in turbofan engines, ensuring consistent robustness in acceleration and deceleration performance across different altitudes. From the ground to an altitude of 11 km, the new method has an acceleration time range of 3.44 s–3.84 s and a deceleration time range of 4.83 s–5.98 s; compared with the previous fuel–air ratio acceleration time of 4.17 s–9.12 s and deceleration time of 6.12 s–14.48 s, its high and low-altitude acceleration and deceleration consistency performance is greatly improved. Full article

Quadrotor unmanned aerial vehicles (QUAVs) are widely recognized for their versatility and advantages across diverse applications. However, their inherent instability and underactuated dynamics pose significant challenges, particularly under external disturbances and parametric model uncertainties. This paper presents an advanced observer-based control framework to address these challenges, introducing a high-gain disturbance observer (HGDO) integrated with a neural-network-based adaptive fractional sliding mode control (NN-AFSMC) scheme. The proposed HGDO-NN-AFSMC ensures robust position and attitude tracking by effectively compensating for external disturbances and model uncertainties. A direct control approach is employed, significantly reducing computational complexity by minimizing the need for frequent online parameter updates while maintaining high tracking precision and robustness. The stability of the control system is rigorously analyzed using Lyapunov theory, and comprehensive simulation studies validate the proposed scheme’s superior performance compared to other advanced control approaches, particularly in dynamic and uncertain operational environments. The proposed HGDO-NN-AFSMC achieves a position tracking error of less than 0.03 m and an attitude tracking error below 0.02 radians, even under external disturbances and parametric uncertainties of 20%. Compared to conventional robust feedback linearization (RFBL) and nonsingular fast terminal sliding mode control (NFTSMC), the proposed method improves position tracking accuracy by 25% and reduces settling time by approximately 18%. Full article