Search Results (147)

In magnetic separation processes, the capture radius R_c of magnetic particles achieved by the magnetic matrix constitutes a critical parameter governing the separation efficiency and operational performance of magnetic separation equipment. Through a systematic study of the characteristics of R_c and the factors influencing it, the application capability of separation systems can be notably improved. To address the lack of systematic research on R_c under low magnetic field intensities (<0.6 T), a key gap compared to conventional high gradient magnetic separation (HGMS) operating at ≥0.6 T, the motion trajectories of magnetic particles adjacent to a rod-shaped matrix, as well as their final capture or repulsion behaviors, were observed via a high-speed camera. Concurrently, these processes were accurately reproduced using the finite element method (FEM). This study innovatively integrates experimental validation and FEM simulation, achieving mutual verification that single-method studies cannot provide. Based on the experimentally validated FEM model, the effects of magnetic field intensity H, rod-shaped matrix diameter Φ, magnetic particle diameter d, and fluid viscosity η on the motion of magnetic particles were methodically investigated. The velocity characteristics of particles at critical positions between the capture and repulsion zones were analyzed to determine the capture radius of the rod-shaped matrix under specified conditions. Drawing on the identified parametric effects, the developed capture radius prediction model fills the research gap in low-intensity HGMS and serves as a theoretical reference for optimizing both the spacing design of industrial-scale rod-shaped matrix arrays and their matching with relevant operating parameters, and the development of energy-efficient magnetic separation equipment. Full article

Background and Objectives: Hamstring strain injuries (HSIs) are frequent and recurrent in athletes who perform high-speed running. The long head of the biceps femoris (BFlh) is often affected by HSIs. While the Nordic hamstring exercise (NHE) is used for prevention, evidence shows it mainly activates the semitendinosus (ST) instead of the biceps femoris (BF). It was argued that hamstrings may contract isometrically during sprinting’s late swing phase; exercises like the single-leg Roman Chair-Hold (RCH) might better mimic sprinting. Limited electromyographic (EMG) data compare NHE and RCH. This study examined EMG activation of BF and ST during both exercises in athletes. Materials and Methods: Thirty-six professional handball players (17 females, 19 males) were randomly assigned to NHE (n = 18; mean age 22.1 ± 3.9 years) or RCH (n = 18; mean age 22.6 ± 4.9 years). A wireless EMG system recorded dominant leg BFlh and ST activity, normalised to maximal voluntary isometric contraction (MVIC%). NHE participants completed one set of ten repetitions; RCH participants performed three sets of ten repetitions with progressive loads (bodyweight, +10 kg, +20 kg). Results: RCH led to a significantly higher mean BFlh activation in the third set with +20 kg weight compared to NHE (72.9% versus 46.5%; p < 0.001, g = 1.52). BFlh activation steadily increased across RCH sets, coinciding with additional load increments (p < 0.001). Conversely, NHE produced greater ST activation than the RCH at the first set, where RCH was performed with only bodyweight (p < 0.001). Conclusions: NHE primarily activates the ST, while RCH gradually increases BFlh activation, particularly under load. Future research should investigate which exercises are more effective at reducing HSIs to draw more robust conclusions based on the study’s findings. Full article

Promoting the coupled and coordinated development of new-type urbanization and rural revitalization is important for achieving high-quality and sustainable growth in China. This study follows a people-centered and coordinated development approach and is aligned with the Sustainable Development Goals (SDGs). It builds a comprehensive evaluation framework for the two systems and measures and interprets their coupling and coordination. On this basis, and under the background of China’s territorial spatial planning, the study draws implications for land and spatial governance. The core of the study is to answer the following questions: What are the spatiotemporal patterns of the coupling coordination level between new-type urbanization and rural revitalization in China from 2014 to 2023? How has the coordination of their development speed evolved? What are the main sources of regional differences? Which factors are the key drivers that promote coordinated development between the two systems? The main findings are as follows. (1) The national coupling coordination degree increases steadily. Spatially, there is a pattern of “eastern region leading, central and northeastern regions catching up, and western region showing internal divergence”. This pattern is consistent with differences in development intensity and accessibility across regions. (2) From 2019 to 2023, the coordination of development speed improved in most provinces. A few developed or special provinces show short-term mismatch, which may reflect timing gaps between land-use controls and the provision of public services. (3) Gaps between regions are the main source of overall differences, and there is a trend toward convergence. This is in line with interregional equalization and the narrowing of efficiency gaps. (4) Well-being of residents, social development, and digital innovation are the core driving forces. Digital inclusive finance and the intensity of parcel delivery services also provide important support. There are clear interaction effects among the driving factors, and these effects are stronger in areas where planning improves accessibility and reduces transaction costs. Full article

In major pineapple-producing regions of China, conventional manual harvesting is challenged by high labor intensity and cost. Existing mechanical harvesters, still largely in the research and development stage, often suffer from low efficiency and high susceptibility to fruit damage, failing to meet large-scale production demands. This study focuses on the Tainung 16 pineapple, determining that the tensile force required to separate the fruit stem at the calyx ranges from 100.42 N to 165.38 N. Drawing on the biomimetic principles of manual stem-breaking, we designed a harvesting device featuring a curved fixed baffle and a rotating unit. Using theoretical analysis and ADAMS simulation, a mechanical model of the device–stem interaction was established to simulate the force application, bending, and separation processes. This led to the identification of optimal operational parameters: a forward speed of 1.5 m/s, a harvesting unit rotational speed of 37 r/min, and a motion trajectory parameter of 1.3. Field tests demonstrated an average harvesting success rate of 81.23% with a fruit damage rate as low as 9.35%. The device thus effectively addresses the critical industry challenges of low efficiency and high damage. This work provides a direct technical reference and theoretical foundation for the engineering development, refinement, and standardized field operation of pineapple harvesters, facilitating the transition to mechanized large-scale harvesting. Full article

Background: The chronic manifestations of stroke are commonly multisystemic, affecting motor function, perception, cognition, and more. Conventional interventions have limitations when it comes to cost and their mundane nature, which are often perceived as boring. A high prevalence of risk factors has resulted in the adult population experiencing a stroke, many of whom require medical intervention, whose limitations strain both the patient and the healthcare system. Recently, extended reality (XR) has demonstrated promise as a rehabilitative aid for cognition, proprioception, and motor function following stroke without conventional therapy constraints. Methods: This case series explores the relationship between mixed reality (MR; one modality of XR) and cognitive performance in three post-stroke patients. Three post-stroke participants completed 12, one-hour MR training sessions over 4 weeks. Cognitive performance was assessed and changes were compared across three timepoints: baseline, immediately following the intervention, and following a 90-day washout period. Results: Participants demonstrated improvement in memory, executive function, and processing speed. Additionally, two out of the three participants demonstrated trends for improvement in attention and working memory. Conclusions: While these promising results tentatively suggest that 12 h of mixed reality training may yield cognitive improvement in post-stroke patients, a larger sample size is needed before drawing definitive conclusions. Full article

In recent years, humanoid robots have made substantial advances in motion control and multimodal interaction. However, full-size humanoid robots face significant technical challenges due to their inherent geometric and physical properties, leading to large inertia of humanoid robots and substantial driving forces. These characteristics result in issues such as limited biomimetic capabilities, low control efficiency, and complex system integration, thereby restricting practical applications of full-size humanoid robots in real-world settings. To address these limitations, this paper incorporates a biomimetic design approach that draws inspiration from biological structures and movement mechanisms to enhance the robot’s human-like movements and overall efficiency. The platform introduced in this paper, Loong, is designed to overcome these challenges, offering a practically viable solution for full-size humanoid robots. The research team has innovatively used highly biomimetic joint designs to enhance Loong’s capacity for human-like movements and developed a multi-level control architecture along with a multi-master high-speed real-time communication mechanism that significantly improves its control efficiency. In addition, Loong incorporates a modular system integration strategy, which offers substantial advantages in mass production and maintenance, which improves its adaptability and practical utility for diverse operational environments. The biomimetic approach not only enhances Loong’s functionality but also enables it to perform better in complex and dynamic environments. To validate Loong’s design performance, extensive experimental tests were performed, which demonstrated the robot’s ability to traverse complex terrains such as 13 cm steps and 20° slopes and its competence in object manipulation and transportation. These innovations provide a new design paradigm for the development of full-size humanoid robots while laying a more compatible foundation for the development of hardware platforms for medium- and small-sized humanoid robots. This work makes a significant contribution to the practical deployment of humanoid robots. Full article

The increase in Internet of Things (IoT) devices has introduced significant security challenges, primarily due to their inherent constraints in computational power, memory, and energy. This study provides a comparative performance analysis of selected modern cryptographic algorithms on a resource-constrained IoT platform, the Nordic Thingy:53. We evaluated a set of ciphers including the NIST lightweight standard ASCON, eSTREAM finalists Salsa20, Rabbit, Sosemanuk, HC-256, and the extended-nonce variant XChaCha20. Using a dual test-bench methodology, we measured energy consumption and performance under two distinct scenarios: a low-data-rate Bluetooth mesh network and a high-throughput bulk data transfer. The results reveal significant performance variations among the algorithms. In high-throughput tests, ciphers like XChaCha20, Salsa20, and ASCON32 demonstrated superior speed, while HC-256 proved impractically slow for large payloads. The Bluetooth mesh experiments quantified the direct relationship between network activity and power draw, underscoring the critical impact of cryptographic choice on battery life. These findings offer an empirical basis for selecting appropriate cryptographic solutions that balance security, energy efficiency, and performance requirements for real-world IoT applications. Full article

This study investigates the impact of digitalisation and intangible investment—specifically digital skills and software adoption—on productivity in the United Kingdom’s banking sector. Software adoption is captured through banks’ investment in enterprise systems (CRM/ERP, cloud computing, and related applications), rather than a single software version. Drawing on detailed bank-level data from six major UK banks over the period 2007–2022, this research provides empirical evidence that higher intensities of digital human capital and intangible assets are positively associated with improvements in both employee productivity and overall bank performance. A standard deviation increase in software specialist employment is associated with productivity gains of 10.3% annually, though this upper-bound estimate likely combines direct effects with complementary factors such as concurrent IT investments (e.g., cloud infrastructure) and managerial innovations. The findings also highlight substantial heterogeneity across banks, with younger institutions experiencing more pronounced benefits from intangible investment due to their greater flexibility and innovation capacity. Furthermore, this study reveals that the adoption of high-speed internet and investment in IT hardware have a strong positive effect on bank productivity, particularly in the wake of the COVID-19 pandemic, which accelerated digital transformation across the sector. However, the observational nature of the study and the limited sample size necessitate caution in generalising the findings. While the results have implications for digital workforce development and technology infrastructure, policy recommendations should be interpreted as preliminary, pending further validation in broader samples and diverse institutional settings. This study concludes by advocating for targeted strategies to expand digital skills, promote software diffusion, and modernise infrastructure to facilitate productivity convergence, while emphasising the need for future research to address potential endogeneity and external validity limitations. Full article

This paper presents a procedural method aimed at protecting maritime critical infrastructure, which is essential for the functioning of developed nations. A novel approach, developed by the authors, is introduced—focusing on the behavioral analysis of vessels to enable early identification of suspicious maritime activity and to prevent damage or destruction to key infrastructure elements. An integrated system is proposed, combining real-time electronic surveillance with continuous access to and analysis of data from both national and international databases. Drawing inspiration from medical sciences, a screening-based methodology has been developed. Data on vessels collected from various sources are processed according to the criteria adopted by the authors, using a multi-criteria decision analysis (MCDA) approach. MCDA is a decision-support method that considers multiple criteria simultaneously. It allows for the comparison and evaluation of different options, even when they are difficult to compare directly. This characteristic is used to select high-risk vessels for further monitoring. An initial classification of a vessel as suspicious does not constitute proof of criminal activity but rather serves as a trigger for further coordinated actions. Data on vessels is collected from the AIS (automatic identification system) and platforms that store vessel history. The AIS is a powerful tool that processes parameters such as a ship’s speed and course. This article presents sample results from surveillance and pre-selection analyses using the AIS, followed by a multi-criteria assessment of the behavior of vessels identified through this process. The results are presented both graphically and numerically. The authors conducted several scenarios, analyzing different groups of vessels. Based on this analysis, recommendations were developed for the interpretation of the findings. Full article

In the era of precision medicine, effective data visualisation plays a pivotal role in supporting clinical decision-making by translating complex, multidimensional datasets into intuitive and actionable insights. This paper explores the foundational principles of visual perception, with a specific focus on pre-attentive attributes such as colour, shape, size, orientation, and spatial position, which are processed automatically by the human visual system. Drawing from cognitive psychology and perceptual science, we demonstrate how these attributes can enhance the clarity and usability of medical visualisations, reducing cognitive load and improving interpretive speed in high-stakes clinical environments. Through detailed case studies and visual examples, particularly within the field of oncology, we highlight best practices and common pitfalls in the design of dashboards, nomograms, and interactive platforms. We further examine the integration of advanced tools—such as genomic heatmaps and temporal timelines—into multidisciplinary workflows to support personalised care. Our findings underscore that visually intelligent design is not merely an aesthetic concern but a critical factor in clinical safety, efficiency, and communication, advocating for user-centred and evidence-based approaches in the development of health data interfaces. Full article

In this paper, we present the concept of “sequencing economics”, consisting of (A) segmentation, (B) construction sequencing, and (C) functions. An agglomeration is organized into segments, and sequencing economics examines the sequential process of efficiently building such segments. The functions (C) of the segments act as a master switch, an accelerator, a brake, etc. in the implementation of agglomeration policy. In this paper, we identify a master switch and an accelerator in scientific city agglomeration policy and draw two conclusions. First, in agglomeration policy, the construction of the master switch lowers “transport costs”, as derived from the monocentric city model of spatial economics by Fujita and Krugman. Second, the accelerator segment represents the activities of the service sector that have the highest forward-linkage effect in an input–output relationship. Regarding science city agglomeration policy, it can be concluded that the master switch is high-speed rail and the accelerator is research and education activities. In this paper, the new scientific urban agglomeration that emerges from monocentric cities is referred to as railroad-driven agglomeration (RDA), which is a type of transit-oriented development (TOD). This paper demonstrates that the Tsukuba Express, as a case study of RDA, caused the agglomeration of Tsukuba Science City. This paper establishes the concept of sequencing economics, a policy implementation rule that differs from Tinbergen’s rule. The latter is based on the concept of simultaneous equations, whereas the rule of sequencing economics is based on sequential equations. RDA enables middle-income countries to surpass their middle-income status. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

The aim of this study was threefold: (i) to analyze the influence of previous match outcome on subsequent weekly training load (TL); (ii) to examine whether accumulated weekly TL varies throughout the season; and (iii) to investigate the influence of performance streaks got during competition on subsequent weekly TL. Twenty-one Spanish male professional soccer players from the same team were involved in the study. Total distance (TD), medium-speed running (MSR, distance 10.8–18.0 km·h⁻¹), high-speed running (HSR, >21 km·h⁻¹), very high-speed running (VHSR, 18.0–25.2 km·h⁻¹), sprinting speed running distance (sprint, >25.2 km·h⁻¹), player load (PL), number of accelerations (ACC), and decelerations (DEC) were recorded during training sessions using 10 Hz GPS devices. Previous match outcome, period of the season, and the performance streaks were also considered. Linear mixed models showed that team covered significantly less TD during the week after draw than after win (p < 0.05). In addition, most of the variables decreased as the season progressed. Finally, after medium-performance streaks, team covered significantly higher TD compared to high-performance streaks (p < 0.05) and low performance streaks (p < 0.01). These findings showed that low-performance streaks could reduce weekly external TL. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms. Full article