Search Results (46)

Objectives: Sprinting and jumping abilities are key determinants of basketball performance. This study aims to analyze changes in sprinting and jumping performance among professional basketball players during the preseason and to determine whether these adaptations are influenced by specific playing positions (Guards vs. Bigs). Methods: A total of 106 professional basketball players from European leagues were evaluated twice over a 6-week preseason. Neuromuscular assessments included linear sprints (5, 10, and 20 m), a change of direction test, curved sprints, and multiple jump tests: Squat Jump (SJ), Countermovement Jump (CMJ), Single-Leg CMJ (SL-CMJ) and Arm-Swing CMJ (CMJA), Single Leg Hop for Distance (SHDJ), Lateral Bound Jump (LBJ), and Single-Leg Repeated Jumps (SLRJ). The training program integrated 6–8 weekly basketball-specific technical–tactical sessions with two to three strength and conditioning sessions targeting maximal strength, power, and hypertrophy. Results: Players significantly improved linear and curved sprint performance, and jumping ability, particularly CMJ, CMJA, and right-leg SHDJ. Minimal changes were observed in SJ, LBJ, and SLRJ. Positional differences were small, with Guards showing greater gains in CMJA than Bigs (6.85% vs. 1.87%). Conclusions: A 6-week preseason training program may be associated with improvements in sprinting (linear 5, 10, 20 m, and curved sprint) and vertical jump performance (CMJ, CMJA, SHDJ) in professional basketball players, with limited influence of playing position. Guards appear to benefit more from arm-swing vertical jump development. Full article

This work focuses on time and frequency domain analyses of IMU-based orientation estimation algorithms, including indirect Kalman (IKF), Madgwick (MF), and complementary (CF) filters. Euler angles and quaternions are used for orientation representation. A 6-DoF IMU is attached to a 6-joint UR5e robotic arm, with the robot’s orientation serving as the reference. Robotic arm data is obtained via an RTDE interface and IMU data via a CAN bus. Test signals include pose sequences, which are big-amplitude, slowly changing signals used to evaluate stationary and low-dynamics responses in the time domain, and small-amplitude, fast-changing generalized binary noise (GBN) signals used to evaluate dynamic responses in the frequency domain. To prevent poor filters’ performance, their parameters are tuned. In the time domain, RMSE and MaxAE are calculated for roll and pitch. In the frequency domain, composite frequency response and coherence are calculated using the Ockier method. RMSEs are computed for response magnitude and coherence, and averaged equivalent time delay (AETD) is derived from the response phase. In the time domain, MF and CF show the best overall performance. In the frequency domain, they again perform similarly well. IKF consistently performs the worst in both domains but achieves the lowest AETD. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big data analytics, the performance of big data frameworks, especially on ARM-based HPC systems, remains underexplored. This paper presents an extensive experimental study on deploying Apache Spark 3.0.2, the de facto standard in-memory processing system, on an ARM-based HPC system. This study conducts a comprehensive performance evaluation of Apache Spark through representative big data workloads, including K-means clustering, to assess the effects of latency variations, such as those induced by network delays, memory bottlenecks, or computational overheads, on application performance in industrial IoT and edge computing environments. Our findings contribute to an understanding of how big data frameworks like Apache Spark can be effectively deployed and optimized on ARM-based HPC systems, particularly when leveraging vectorized instruction sets such as SVE, contributing to the broader goal of enhancing the integration of cloud–edge computing paradigms in modern industrial environments. We also discuss potential improvements and strategies for leveraging ARM-based architectures to support scalable, efficient, and real-time data processing in Industry 4.0 and beyond. Full article

Robots and 3D scanning systems are essential in modern industrial production, enhancing quality control, reducing costs, and improving production efficiency. Such systems align with Industry 4.0 trends, incorporating the Internet of Things (IoT), Big Data, Cyber–Physical Systems, and Artificial Intelligence to drive innovation. This paper focuses on the physical adaptation of old or out-of-use articulated robot arms for new tasks such as manipulation with a handheld 3D scanner, with the goal of automated quality control. The adaptation was carried out using a methodology that features the application of several techniques such as 3D digitization (photogrammetry), reverse engineering and 3D modeling (SolidWorks), the CAD search engine (3Dfindit), and 3D printing (fused deposition modeling—FDM). Reconstructed 3D models were used to design connecting elements, such as gripper jaws. The final results show that it is possible to create a connecting element utilizing this approach with very little expenditure of resources and time. Full article

Basketball neuromuscular demands are highly position-dependent, making it important to consider this factor in performance assessment. This study aimed to analyze the validity and reliability of jumping and linear sprinting tests for professional basketball players based on their playing position. A total of 102 professional basketball players, classified as Bigs and Guards, were assessed during the preseason through Squat Jump (SJ), Countermovement Jump (CMJ), Single-Leg CMJ (SL-CMJ), Arm Swing CMJ (CMJA), and linear sprinting over 5, 10, and 20 m. Relative reliability analysis was carried out by calculating the Intraclass Correlation Index (ICC), and the coefficient of variation (CV) was used as an absolute reliability indicator. The jumping and linear sprinting tests showed good to excellent relative reliability (ICC: 0.81–0.97) and absolute reliability (CV: 0.1–2.6) with a minimum detectable change ranging from 5.38 to 20.82% and from 4.76 to 10.43% for jumping and linear sprinting tests, respectively. Both Bigs and Guards showed excellent absolute reliability in all tests. Bigs showed greater ICC than Guards in SJ, CMJ, CMJA, and the 10 and 20 m sprints, while Guards outperformed in the 5 m sprint. SL-CMJ showed greater absolute reliability for Bigs, while relative reliability was higher for Guards. In conclusion, these findings may aid basketball physical coaches in the selection of the most suitable jumping and sprinting tests for preseason neuromuscular performance monitoring based on players’ playing position. Full article

Background/Objectives: Individuals with spinal cord injuries have a higher incidence of chronic conditions such as hypertension and cardiovascular diseases due to a sedentary lifestyle and low levels of physical activity caused by their disability. Additionally, their physical fitness levels are lower compared to those without disabilities. This cross-sectional study aimed to investigate the relationship between hypertension and the fitness of individuals with spinal cord injuries in South Korea while considering differences across sexes and spinal cord injury levels. Methods: This study used data for 835 individuals with spinal cord injuries aged 20–64 years who visited the Korea Paralympic Committee fitness standard test centers from 2018 to 2022, obtained from the Korea Culture Information Sports Association’s big data market. The data were analyzed using a series of t-tests, a one-way analysis of variance, a logistic regression analysis, and the four-quartile method. Results: The prevalence of hypertension was 24.4%, and it was different according to the spinal cord injury impairment level. A lower grip strength, a lower arm curl, and a higher body mass index were associated with increased blood pressure. Conclusions: Therefore, a high level of physical strength in people with spinal cord injuries is thought to contribute to lowering blood pressure. Full article

The analysis of the impact of the star polymer topology on depletion interaction potentials, depletion forces, and monomer density profiles is carried out analytically using field theory methods and techniques as well as molecular dynamic simulations. The dimensionless depletion interaction potentials and the dimensionless depletion forces for a dilute solution of ideal star polymers with three and five legs (arms) in a $\mathsf{\Theta }$-solvent confined in a slit between two parallel walls with repulsive surfaces and for the case where one of the surfaces is repulsive and the other inert are obtained. Furthermore, the dimensionless layer monomer density profiles for ideal star polymers with an odd number ($\tilde{f}$ = 3, 5) of arms immersed in a dilute solution of big colloidal particles with different adsorbing or repelling properties in respect of polymers are calculated, bearing in mind the Derjaguin approximation. Molecular dynamic simulations of a dilute solution of star-shaped polymers in a good solvent with N = 901 (3 × 300 + 1 -star polymer with three arms) and 1501 (5 × 300 + 1 -star polymer with five arms) beads accordingly confined in a slit with different boundary conditions are performed, and the results of the monomer density profiles for the above-mentioned cases are obtained. The numerical calculation of the radius of gyration for star polymers with $\tilde{f}$ = 3, 5 arms and the ratio of the perpendicular to parallel components of the radius of gyration with respect to the wall orientation for the above-mentioned cases is performed. The obtained analytical and numerical results for star polymers with an odd number ($\tilde{f}$ = 3, 5) of arms are compared with our previous results for linear polymers in confined geometries. The acquired results show that a dilute solution of star polymer chains can be applied in the production of new functional materials, because the behavior of these solutions is strictly correlated with the topology of polymers and also with the nature and geometry of confined surfaces. The above-mentioned properties can find extensive practical application in materials engineering, as well as in biotechnology and medicine for drug and gene transmission. Full article

The concept of “all cores are created equal” has been popular for several decades due to its simplicity and effectiveness in CPU (Central Processing Unit) design. The more cores the CPU has, the higher performance the host owns and the higher the power consumption. However, power-saving is also one of the key goals for servers in data centers and embedded devices (e.g., mobile phones). The big.LITTLE multicore architecture, which contains high-performance cores (namely big core) and power-saved cores (namely little core), has been developed by ARM (Advanced RISC Machine) and Intel to trade off performance and power efficiency. Facing the new heterogeneous computing architecture, the traditional lock algorithms, which are designed to run on homogeneous computing architecture, cannot work optimally as usual and drop into the performance issue for the difference between big core and little core. In our preliminary experiment, we observed that, in the big.LITTLE multicore architecture, all these lock algorithms exhibit sub-optimal performance. The FIFO-based (First In First Out) locks experience throughput degradation, while the performance of competition-based locks can be divided into two categories. One of them is big-core-friendly, so their tail latency increases significantly; the other is little-core-friendly. Not only does the tail latency increase, but the throughput is also degraded. Motivated by this observation, we propose a Core-Aware Lock for the big.LITTLE multicore architecture named CAL, which keeps each core having an equal opportunity to access the critical section in the program. The core idea of the CAL is to take the slowdown ratio as the matric to reorder lock requests of these big and little cores. By evaluating benchmarks and a real-world application named LevelDB, CAL is confirmed to achieve fairness goals in heterogeneous computing architecture without sacrificing the performance of the big core. Compared to several traditional lock algorithms, the CAL’s fairness has increased by up to 67%; and Its throughput is 26% higher than FIFO-based locks and 53% higher than competition-based locks, respectively. In addition, the tail latency of CAL is always kept at a low level. Full article

Background: Vasomotor symptoms (VMSs) associated with menopause represent a significant challenge for many patients after cancer treatment, particularly if conventional menopausal hormone therapy (MHT) is contraindicated. Methods: The Menopause after Cancer (MAC) Study (NCT04766229) was a single-arm phase II trial examining the impact of a composite intervention consisting of (1) the use of non-hormonal pharmacotherapy to manage VMS, (2) digital cognitive behavioral therapy for insomnia (dCBT-I) using Sleepio (Big Health), (3) self-management strategies for VMS delivered via the myPatientSpace mobile application and (4) nomination of an additional support person/partner on quality of life (QoL) in women with moderate-to-severe VMS after cancer. The primary outcome was a change in cancer-specific global QoL assessed by the EORTC QLC C-30 v3 at 6 months. Secondary outcomes included the frequency of VMS, the bother/interference of VMS and insomnia symptoms. Results: In total, 204 women (82% previous breast cancer) with a median age of 49 years (range 28–66) were recruited. A total of 120 women completed the protocol. Global QoL scores increased from 62.2 (95%CI 58.6–65.4) to 70.4 (95%CI 67.1–73.8) at 6 months (p < 0.001) in the intention to treatment (ITT) cohort (n = 204) and from 62 (95%CI 58.6–65.4) to 70.4 (95%CI 67.1–73.8) at 6 months (p < 0.001) in the per-protocol (PP) cohort (n = 120). At least 50% reductions were noticed in the frequency of VMS as well as the degree of bother/interference of VMS at six months. The prevalence of insomnia reduced from 93.1% at the baseline to 45.2% at 6 months (p < 0.001). The Sleep Condition Indicator increased from 8.5 (SEM 0.4) to 17.3 (SEM 0.5) (p < 0.0005) in the ITT cohort and 7.9 (SEM 0.4) to 17.3 (SEM 0.5) (p < 0.001) in the PP cohort. Conclusions: A targeted composite intervention improves the quality of life for cancer patients with frequent and bothersome vasomotor symptoms with additional benefits on frequency, the bother/interference of VMS and insomnia symptoms. Full article

In view of existing problems, such as the seed and fertilizer supply link for wheat seeders still relying on manual installation and the lack of practical application equipment, a seed–fertilizer replenishment device based on the three-degree-of-freedom mechanical arm and screw conveying principle is designed using the seed box installation and supply as the operation scenario to replace the manual installation process. Combined with the requirements of the seed box replenishment operation, the key parameters of the replenishment robot arm and the screw conveyor auger are determined. Then, the kinematic model of the replenishment robot arm is established based on the modified D-H method, forward and inverse kinematics calculations are performed, and the workspace is analyzed using the Monte Carlo method. Based on this, the robotic arm task path is designed, the fifth-degree polynomial interpolation method is used to complete the trajectory planning, and MATLAB R2016a software is used to simulate the motion trajectories of each joint, verifying the feasibility of the trajectory planning solution. Finally, a prototype is trial-produced and quadratic regression orthogonal testing and response surface analyses are conducted to obtain the optimal working parameters of the replenishment device. The verification test shows that when the angular velocity of the lumbar joint of the replenishment device is 4°/s, the speed of screw conveyor is 90 r/min, and the angle of the big arm is 12°, the conveying loss rate is 3.98%, and the conveying efficiency is 0.833 kg/s. The relative errors with the theoretical optimal values are 4.2% and 2.4%, respectively, both less than 5%. The supply trajectory is reasonable, and the robot arm runs smoothly. This study can provide reference for the design of seed–fertilizer replenishment device for wheat seeders. Full article

The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors. Full article

A low-cost embedded system for high-energy radiation detection applications was developed for national security proposes, mainly to detect nuclear material and send the detection event to the cloud in real time with tracking capabilities. The proof of concept was built with state-of-the-art electronics such as an adequate Si-based photodetector, a trans-impedance amplifier, an ARM Cortex M4 microcontroller with sufficient ADC capture capabilities, an ESP8266 Internet of Things (IoT) module, an optimized Message Queuing Telemetry Transport (MQTT) protocol, a MySQL data base, and a Python handler program. The system is able to detect alfa particles and send the nuclear detection events to the CloudMQTT servers. Moreover, the detection message records the date and time of the ionization event for the tracking application, and due to a particular MQTT-optimized protocol the message is sent with low latency. Furthermore, the designed system was validated with a standard radiation instrumentation preamplifier 109A system from ORTEC company, and more than one node was demonstrated with an internet connection employing a 20,000 bits/s CloudMQTT plan. Therefore, the design can be escalated to produce a robust big data multisensor network. Full article

With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body’s structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body’s motion posture, which has great application prospects in the field of gait analysis. Full article