Search Results (1,127)

Background: Chronic pain affects about 20% of total knee arthroplasty (TKA) patients, with high pain catastrophizing being a key predictor. Screening and addressing this modifiable factor may improve postoperative outcomes. Objective: We aimed to compare the effectiveness of two preoperative home-based multimodal physical therapy interventions on pain catastrophizing in high-catastrophizing TKA patients. Secondarily, the study aimed to assess postoperative outcomes over six months. Methods: A total of 40 patients with symptomatic osteoarthritis and moderate pain catastrophizing were randomly allocated to the control, therapeutic patient education (TPE), and multimodal physiotherapy (MPT) groups. Preoperative interventions comprised pain neuroscience education, coping skills training, and therapeutic exercise, differing in the number of sessions and degree of supervision. All outcomes were assessed before and after the treatment in the preoperative period, and 1, 3, and 6 months post-surgery. The primary outcome measure was pain catastrophizing. Results: Both intervention groups showed a preoperative reduction in pain catastrophizing. TPE patients had lower pain ratings at rest and lower catastrophizing scores at 1 and 6 months post-surgery, reduced kinesiophobia and improved dynamic balance at 3 and 6 months post-surgery, and higher self-efficacy at 1 month post-surgery. MPT patients exhibited lower pain catastrophizing and pain intensity during walking at 1 month post-surgery, and better outcomes in kinesiophobia, self-efficacy, and dynamic balance at 1, 3, and 6 months post-surgery, along with higher walking speed at 6 months post-surgery. Conclusions: Preoperative physiotherapy reduces preoperative pain catastrophizing and improves postoperative pain-related outcomes, behaviors, and cognitions in high-catastrophizing TKA patients. Registration is with the United States Clinical Trials Registry (NCT03847324). Full article

The vibration suppression of flexible robotic arms is challenging due to their nonlinear spatiotemporal dynamics. This paper presents a novel physics-informed neural network (PINN)-based input-shaping method for the vibration suppression problem. Through a two-phase training process of a neural network based on a loss function that follows both the physical model constraints and the vibration modal conditions, we identify optimal input-shaping parameters to minimize residual vibration. With the use of powerful computational resources to handle multimode information about the vibration, the PINN-based approach outperforms traditional input-shaping methods in terms of computational efficiency and performance. Extensive simulations are carried out to validate the effectiveness of the method and highlight its potential for complex control tasks in flexible robotic systems. Full article

In the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual growth, and prevention of theft and fraud. These advancements are pivotal to the efficient and sustainable development of the livestock industry. Recently, visual livestock biometrics have emerged as a highly promising research focus due to their non-invasive nature. This paper aims to comprehensively survey the techniques for individual livestock identification based on computer vision methods. It begins by elucidating the uniqueness of the primary biometric features of livestock, such as facial features, and their critical role in the recognition process. This review systematically overviews the data collection environments and devices used in related research, providing an analysis of the impact of different scenarios on recognition accuracy. Then, the review delves into the analysis and explication of livestock identification methods, based on extant research outcomes, with a focus on the application and trends of advanced technologies such as deep learning. We also highlight the challenges faced in this field, such as data quality and algorithmic efficiency, and introduce the baseline models and innovative solutions developed to address these issues. Finally, potential future research directions are explored, including the investigation of multimodal data fusion techniques, the construction and evaluation of large-scale benchmark datasets, and the application of multi-target tracking and identification technologies in livestock scenarios. Full article

Background and Objectives: Kidney transplantation (KT) is an important treatment modality for renal failure. However, moderate-to-severe pain often occurs in KT recipients. Multimodal analgesia using combined analgesic measures has been recommended to enhance postoperative recovery. This retrospective study explored the additional analgesic efficacy of paracetamol and nefopam infusions in living-donor KT recipients who received a transversus abdominis plane (TAP) block. Materials and Methods: Consecutive living-donor KT recipients at our institute between January 2020 and March 2022 were divided into groups that received a TAP block with paracetamol and nefopam infusions (Group TA) or a TAP block without analgesics (Group T) during surgery. Following propensity-score (PS) matching, 103 patients were included in each group. Postoperative pain intensity assessed using the visual analog scale (VAS), opioid consumption via patient-controlled analgesia (PCA) devices over 24 h, and postoperative outcomes were compared between the two groups. Results: VAS pain intensity at rest was lower in group TA than in group T at 1 and 6 h after surgery [1 h: 29 (15–41) vs. 41 (29–51) mm, p < 0.001; 6 h: 32 (23–43) vs. 40 (32–54) mm, p < 0.001]. The VAS pain intensity during coughing was lower in group TA [1 h: 46 (30–58) vs. 59 (48–69) mm, p < 0.001; 6 h: 51 (40–63) vs. 60 (45–71) mm, p < 0.001]. Moreover, PCA consumptions during the first 6 h and between 6–24 h post-surgery was significantly lower in group TA. Other postoperative outcomes did not differ between the two groups. Conclusions: Multimodal analgesia with intraoperative paracetamol and nefopam infusions improved postoperative pain control in living-donor KT recipients who received a preoperative TAP block. Our findings demonstrate the efficacy of paracetamol and nefopam infusions in KT recipients. Full article

This study presents a multimodal data fusion system to identify and impact rocks in mining comminution tasks, specifically during the crushing stage. The system integrates information from various sensory modalities to enhance data accuracy, even under challenging environmental conditions such as dust and lighting variations. For the strategy selected in this study, 15 rock characteristics are extracted at neighborhood radii of 5 mm, 10 mm, 15 mm, 20 mm, and 25 mm to determine the suitable impact points. Through processes like the Ball−Pivoting Algorithm (BPA) and Poisson Surface Reconstruction techniques, the study achieves a detailed reconstruction of filtered points based on the selected characteristics. Unlike related studies focused on controlled conditions or limited analysis of specific rock shapes, this study examines all rock faces, ensuring the more accurate identification of impact points under adverse conditions. Results show that rock faces with the largest support areas are most suitable for receiving impacts, enhancing the efficiency and stability of the crushing process. This approach addresses the limitations of manual operations and provides a pathway for reducing operational costs and energy consumption. Furthermore, it establishes a robust foundation for future research to develop fully autonomous systems capable of maintaining reliable performance in extreme mining environments. Full article

The grey wolf optimization (GWO) algorithm is a simple and effective meta-heuristic algorithm that mimics the leadership of grey wolves and the social behavior of wolves in nature. However, the updating of GWO population positions only relies on the guidance of α-wolf, β-wolf, and δ-wolf, and individuals are updated with equal weights. This results in the GWO search process being unable to utilize the knowledge of superior wolves better. Therefore, in this study, we propose for the first time an attention mechanism-based GWO (AtGWO). Firstly, when each position is updated, the attention strategy can adaptively assign the weight of the corresponding leader wolf to improve the global exploration ability. Second, with the introduction of omega-wolves, each position update is not only guided by the three leader wolves but also learns from their current optimal values. Finally, a hyperbolic tangent nonlinear function is used to control the convergence factor to better balance exploration and exploitation. To validate its effectiveness, AtGWO is compared with the latest GWO variant with other popular algorithms on the CEC-2014 (dim 30, 50) and CEC-2017 (dim 30, 50, 100) benchmark function sets. The experimental results indicate that AtGWO outperforms the GWO-related variants almost all the time in terms of mean, variance, and best value, which indicates its superior ability and robustness to find optimal solutions. And it is also competitive when compared to other algorithms in multimodal functions. AtGWO outperforms the comparison algorithms in terms of the mean and best value in six real-world engineering optimization problems. Full article

Autonomous vehicles (AVs) rely heavily on multi-modal sensors to perceive their surroundings and make real-time decisions. However, the increasing complexity of these sensors, combined with the computational demands of AI models and the challenges of synchronizing data across multiple inputs, presents significant obstacles for AV systems. These challenges of the AV domain often lead to performance latency, resulting in delayed decision-making, causing major traffic accidents. The data concentrator unit (DCU) concept addresses these issues by optimizing data pipelines and implementing intelligent control mechanisms to process sensor data efficiently. Identifying and addressing bottlenecks that contribute to latency can enhance system performance, reducing the need for costly hardware upgrades or advanced AI models. This paper introduces a delay measurement tool for multi-node analysis, enabling synchronized monitoring of data pipelines across connected hardware platforms, such as clock-synchronized DCUs. The proposed tool traces the execution flow of software applications and assesses time delays at various stages of the data pipeline in clock-synchronized hardware. The various stages are represented with intuitive graphical visualization, simplifying the identification of performance bottlenecks. Full article

This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle. To pretrain the architecture, a synthetic multimodal dataset for autonomous driving applications was created. The dataset includes driving data from 100 diverse weather and traffic scenarios, gathered from multiple sensors including cameras and an IMU as well as from vehicle control variables. The results show that including edge detection layers in the architecture improves performance for transfer learning when using synthetic and real-world data. In addition, pretraining with synthetic data reduces training time and enhances model performance when using real-world data. Full article

Background and Objectives: Video-assisted thoracoscopic surgery (VATS) is associated with less postoperative pain than traditional open thoracotomy. However, trocar and chest tube placement may damage the intercostal nerves, causing significant discomfort. An ultrasound-guided serratus anterior plane block (SAPB) is a promising mode of pain management; this reduces the need for opioids and the associated side-effects. This study evaluated whether SAPB, compared to intravenous analgesia alone, reduces opioid consumption after thoracoscopic lung wedge resection. Materials and Methods: In total, 22 patients undergoing VATS lung wedge resections were randomized into two groups (SAPB and control): both received intravenous patient-controlled analgesia (PCA), and one group received additional SAPB. The primary outcome was the cumulative intravenous fentanyl consumption at 8 h postoperatively. The visual analog scale (VAS) pain scores and the incidence of postoperative complications were assessed over 48 h post surgery. Results: Fentanyl consumption by 8 h post surgery was significantly lower in the SAPB group than in the control group (183 ± 107 μg vs. 347 ± 202 μg, p = 0.035). Although the VAS scores decreased with time in both groups, the differences were not statistically significant. The SAPB group required fewer opioids by 48 h. No significant between-group differences were observed in postoperative complications, including nausea and vomiting. Conclusions: SAPB effectively reduced opioid consumption after VATS lung wedge resection. SABP may serve as a valuable component of multimodal pain management. Full article

Thoracic surgery is associated with significant postoperative pain, which can hinder recovery and elevate morbidity risks. Traditionally, epidural anesthesia has been the cornerstone for pain management, but its drawbacks including technical challenges, side effects, and complications necessitate exploring alternative methods. This narrative review examined recent advances in perioperative analgesic strategies in thoracic surgery, focusing on regional anesthetic techniques like paravertebral blocks (PVBs), erector spinae plane blocks (ESPBs), intercostal blocks, and serratus anterior blocks. Each approach was evaluated for efficacy, safety, and impact on patient outcomes. PVB can provide effective unilateral analgesia with fewer systemic complications compared to epidurals. ESPB provides analgesia through a superficial, ultrasound-guided approach, minimizing risks and offering an alternative for various thoracic procedures. Intercostal blocks are effective but are limited by the need for multiple injections, increasing the complication risks. Serratus anterior blocks, targeting intercostal and thoracic nerves, show promise in managing lateral thoracic wall pain with a low complication rate. Advancements in surgical techniques including minimally invasive approaches further optimize pain control and recovery. A multimodal analgesic approach combining regional anesthesia and systemic therapies enhances outcomes by addressing somatic and visceral pain components. Despite the efficacy of epidural analgesia, alternative regional techniques offer comparable pain relief with fewer complications, suggesting their growing role in thoracic surgery. Collaborative efforts between surgical, anesthetic, and emergency teams are crucial for tailoring pain management strategies to individual patients, improving recovery and reducing long-term morbidity. Future research should continue exploring these methods to refine their application and broaden their accessibility. Full article

The Equilibrium Optimizer (EO) is an optimization algorithm inspired by a physical law called mass balance, which represents the amount of mass entering, leaving, and being produced in a control volume. Although the EO is a well-accepted and successful algorithm in the literature, it needs improvements in the search, exploration, and exploitation phases. Its main problems include low convergence, getting stuck in local minima, and imbalance between the exploration and exploitation phases. This paper introduces the Boosted Equilibrium Optimizer (BEO) algorithm, where improvements are proposed to solve these problems and improve the performance of the EO algorithm. New methods are proposed for the three important phases of the algorithm: initial population, candidate pool generation, and updating. In the proposed algorithm, the exploration phase is strengthened by using a uniformly distributed random initial population instead of the traditional random initial population and a versatile concentration pool strategy. Furthermore, the balance between the exploration and exploitation phases is improved with two new approaches proposed for the updating phase. These novel methods enhance the algorithm’s performance by more effectively balancing exploration and exploitation. The proposed algorithm is tested using a total of 23 standard test functions, including unimodal, multimodal, and fixed-size multimodal. The results are supported by numerical values and graphs. In addition, the proposed BEO algorithm is applied to solve real-world engineering design problems. The BEO outperforms the original EO algorithm on all problems. Full article

As power system equipment gradually ages, the automated disassembly of transformers has become a critical area of research to enhance both efficiency and safety. This paper presents a transformer disassembly system designed for power systems, leveraging multimodal perception and collaborative processing. By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. The system employs an enhanced YOLOv8 model for positioning and identifying screw-fastened covers while also utilizing the STDC network for segmentation and cutting path planning of welded covers. In addition, the system captures 3D point cloud data of the transformer’s interior using multi-view RGB-D cameras and performs multimodal semantic segmentation and object detection via the ODIN model, facilitating the high-precision identification and cutting of complex components such as windings, studs, and silicon steel sheets. Experimental results show that the system achieves a recognition accuracy of 99% for both cover and internal component disassembly, with a disassembly success rate of 98%, demonstrating its high adaptability and safety in complex industrial environments. Full article

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings. Full article

This study presents an innovative hybrid evolutionary algorithm that combines the Arctic Puffin Optimization (APO) algorithm with the JADE dynamic differential evolution framework. The APO algorithm, inspired by the foraging patterns of Arctic puffins, demonstrates certain challenges, including a tendency to converge prematurely at local minima, a slow rate of convergence, and an insufficient equilibrium between the exploration and exploitation processes. To mitigate these drawbacks, the proposed hybrid approach incorporates the dynamic features of JADE, which enhances the exploration–exploitation trade-off through adaptive parameter control and the use of an external archive. By synergizing the effective search mechanisms modeled after the foraging behavior of Arctic puffins with JADE’s advanced dynamic strategies, this integration significantly improves global search efficiency and accelerates the convergence process. The effectiveness of APO-JADE is demonstrated through benchmark tests against well-known IEEE CEC 2022 unimodal and multimodal functions, showing superior performance over 32 compared optimization algorithms. Additionally, APO-JADE is applied to complex engineering design problems, including the optimization of engineering structures and mechanisms, revealing its practical utility in navigating challenging, multi-dimensional search spaces typically encountered in real-world engineering problems. The results confirm that APO-JADE outperformed all of the compared optimizers, effectively addressing the challenges of unknown and complex search areas in engineering design optimization. Full article

Quadruped robots, with their biomimetic structure, are capable of stable locomotion in complex terrains and are vital in rescue, exploration, and military applications. However, developing multi-modal robots that feature simple motion control while adapting to diverse amphibious environments remains a significant challenge. These robots need to excel at obstacle-crossing, waterproofing, and maintaining stability across various locomotion modes. To address these challenges, this paper introduces a novel leg–fin integrated propulsion mechanism for a bionic quadruped robot, utilizing rapidly advancing soft materials and integrated molding technologies. The robot’s motion is modeled and decomposed using an improved central pattern generator (CPG) control network. By leveraging the control characteristics of the CPG model, global control of the single-degree-of-freedom drive mechanism is achieved, allowing smooth transitions between different motion modes. The design is verified through simulations conducted in the Webots environment. Finally, a physical prototype of the quadruped compliant robot is constructed, and experiments are carried out to test its walking, turning, and obstacle-crossing abilities in various environments. The experimental results demonstrate that the robot shows a significant speed advantage in regions where land and water meet, reaching a maximum speed of 1.03 body lengths per second (bl/s). Full article