Search Results (317)

The aim of this study was to evaluate the effect of 11 additional soccer training sessions among youth soccer players according to their performance in a skill course. A total of 90 participants, aged 9 to 12, were randomly assigned to either an intervention group (IG) (n = 54) or a control group (CG) (n = 36) and have validated data. The trainings focused upon enhancing ball mastery and decision-making and included a combination of one vs. one situations and small-sided games (SSGs). Pre- and post-tests measured passing and dribbling skills through a skill course. The best time with additional time penalties for each dribbling and passing error was used for further analysis. An independent t-test revealed no significant differences in improvement between the two groups. However, paired t-tests revealed significant improvements for both the IG and the CG from pre- to post-test (7.9 and 3.9 s, respectively). Furthermore, no significant differences in the development of track time, cone touches, or passing errors between the groups were detected. These findings suggest that soccer players aged 9 to 12 improve their performance in a skill course through increased familiarity with the course and natural development of technical skills based on participation in soccer training and recreational soccer play. We argue that the lack of significant differences between the groups’ performances can be attributed to the short duration and few sessions of the intervention, and a somehow low similarity between the skill course and the activities in the sessions. Full article

Aiming at the uncertainty problem caused by the time-varying modeling parameters associated with ship speed in the course tracking control of underactuated surface vessels (USVs), this paper proposes a control algorithm based on the dynamic neural fuzzy model (DNFM). The DNFM simultaneously adjusts the structure and parameters during learning and fully approximates the inverse dynamics of ships. Online identification and modeling lays the model foundation for ship motion control. The trained DNFM, serving as an inverse controller, is connected in parallel with the fractional-order PI^λD^μ controller to be used for the tracking control of the ship’s course. Moreover, the weights of the model can be further adjusted during the course tracking. Taking the actual ship data of a 5446 TEU large container ship, simulation experiments are conducted, respectively, for course tracking, course tracking under wind and wave interferences, and comparison with five different controllers. This proposed controller can overcome the influence of the uncertainty of modeling parameters, tracking the desired course quickly and effectively. Full article

Cardiac amyloidosis, encompassing both transthyretin (ATTR) and light-chain (AL) types, poses considerable challenges in patient management due to its intricate pathophysiology and progressive course. This narrative review elucidates the pivotal role of cardiopulmonary exercise testing (CPET) in the assessment of these patients. CPET is essential for evaluating disease progression by measuring cardio-respiratory performance and providing prognostic insights. This functional test is crucial not only for tracking the disease trajectory, but also for assessing the effectiveness of disease-modifying therapies. Moreover, CPET facilitates the customization of therapeutic strategies based on individual patient performance, enhancing personalized care. By objectively measuring parameters such as peak oxygen uptake (VO₂ peak), ventilatory efficiency, and exercise capacity, clinicians can gain a deeper understanding of the degree of functional impairment and make informed decisions regarding treatment initiation, adjustment, and anticipated outcomes. This review emphasizes the importance of CPET in advancing personalized medicine approaches, ultimately striving to improve the quality of life and clinical outcomes for patients with cardiac amyloidosis. Full article

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions. Full article

Over the past 50 years, global plastic production has surged exponentially. Around 40% of this plastic is used for packaging, most of which is single-use, while 20% is used in construction. Despite the vast quantities produced, only about 6% of discarded plastics are properly recycled, 10% are incinerated, and the majority are disposed of without proper management. With low recycling rates and some plastics being non-recyclable or with limited recycling cycles, it is important to explore new ways of reusing this waste as secondary raw materials. This study explores the potential of incorporating non-recyclable plastic waste into bituminous mixtures. The objective is to develop a sustainable solution for surface courses with similar or better performance than traditional bituminous mixtures by incorporating plastic waste using the dry method. A bituminous mixture containing 10% non-recyclable plastic was formulated and tested for water sensitivity, wheel tracking, and stiffness modulus. Additionally, environmental and economic comparisons were performed with a standard surface mixture. Results showed increased water resistance, high resistance to permanent deformation, reduced stiffness, lower susceptibility to frequency and temperature variations, and greater flexibility. These findings suggest that adding plastic not only enhances mechanical properties but also reduces costs, offering a sustainable alternative for non-recyclable plastics in road construction. Full article

This study proposes an enhanced ship-type classification model that employs a sequential processing methodology integrating hidden Markov model (HMM), deep neural network (DNN), and convolutional neural network (CNN) techniques. Four different ship types—fishing boat, passenger, container, and other ship—were classified using multiple ship trajectory features extracted from the automatic identification system (AIS) and small fishing vessel tracking system. For model optimization, both ship datasets were transformed into various formats corresponding to multiple models, incorporating data enhancement and augmentation approaches. Speed over ground, course over ground, rate of turn, rate of turn in speed, berth distance, latitude/longitude, and heading were used as input parameters. The HMM–DNN–CNN combination was obtained as the optimal model (average F-1 score: 97.54%), achieving individual classification performances of 99.03%, 97.46%, and 95.83% for fishing boats, passenger ships, and container ships, respectively. The proposed approach outperformed previous approaches in prediction accuracy, with further improvements anticipated when implemented on a large-scale real-time data collection system. Full article

Current higher education trends are moving towards interdisciplinary curricula to provide new tools for solving complex issues. However, course design and learning tracks still create divisions between scientific disciplines. This study aimed to evaluate the disciplinary bias of second-year undergraduate students of biotechnology engineering in the organic chemistry laboratory class through a laboratory setting involving blended disciplines. An experiment on antibiotic discovery that integrates parallel and combinatorial organic chemistry syntheses with microbiology techniques was chosen. As a part of an activity, students had free choice in designing the arrangement of the organic compounds and the two bacterial species by setting up the layout for a 96-well plate. The study visually analyzed students’ plate layouts (n = 74) according to discipline classification and the spatial arrangements of organic compounds (e.g., products and libraries). The results identified four themes that are suggested to reflect students’ vertical, lateral, and interdisciplinary thinking, as most were found to be in the procedural knowledge range and between Bloom’s application and analysis dimensions. Using this study’s thematic analysis methodology in chemistry and related educational fields can provide a pedagogical reflective tool and advance personalized teaching and interdisciplinarity. Full article

The rapid growth of e-learning increased the use of digital reviews to influence consumer purchases. In a pioneering approach, we employed AI-powered eye tracking to evaluate the accuracy of predictions in forecasting purchasing patterns. This study examined customer perceptions of negative, positive, and neutral reviews by analysing emotional valence, review content, and perceived credibility. We measured ‘Attention’, ‘Engagement’, ‘Clarity’, ‘Cognitive Demand’, ‘Time Spent’, ‘Percentage Seen’, and ‘Focus’, focusing on differences across review categories to understand their effects on customers and the correlation between these metrics and navigation to other screen areas, indicating purchasing intent. Our goal was to assess the predictive power of online reviews on future buying behaviour. We selected Udemy courses, a platform with over 70 million learners. Predict (version 1.0.), developed by Stanford University, was used with the algorithm on the consumer neuroscience database (n = 180,000) from Tobii eye tracking (Tobii X2-30, Tobii Pro AB, Danderyd, Sweden). We utilised R programming, ANOVA, and t-tests for analysis. The study concludes that AI neuromarketing techniques in digital feedback analysis offer valuable insights for educators to tailor strategies based on review susceptibility, thereby sparking interest in the innovative possibilities of using AI technology in neuromarketing. Full article

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates. Full article

Private supplementary tutoring (PST) is a flourishing market for edupreneurs whose services relate closely to mainstream education. While international research elaborates on geographical variations in PST and edupreneurs’ marketing strategies, the PST market in Switzerland has been largely understudied. This paper contributes to fill this research gap by presenting a thematic analysis of the websites of edupreneurs offering preparation programmes for the highly selective central entrance examination (CEE) to the academically focused public school track of Gymnasium in Zurich. Conceptually, we draw on “problematization” and “commodificiation” as key terms elaborated by “geographies of marketization”. With this performative conceptualisation of the education market, we examine the marketing strategies of the edupreneurs offering CEE preparation courses. We offer two contributions to the existing research. First, the analysis elaborates that private CEE preparation courses are advertised as a market solution that compensates for an omission caused by public education. Second, we show how edupreneurs use rational and emotional arguments to convince families of the need of booking CEE preparation courses for their children. In the discussion, we engage with the findings of our local case study and argue that selective educational transitions have much wider relevance for policy and society elsewhere. Full article

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness. Full article

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. Full article

Acute (AW) and chronic (CW) workload imbalances, including their ratio (ACWR), are largely associated with increased injury risk. However, the inclusion of personal endurance performance (EP) in this calculation as a means of improving accuracy has been neglected in previous studies. The aim of this longitudinal observational study was to evaluate the relevance of the high metabolic load distance (ACWR_HMLD) to EP in relation to non-contact injuries. Twenty-three German male first division soccer players (age: 24.5 ± 3.5 years; VO_2max: 53.7 ± 4.9 mL/min/kg; v4: 15.2 ± 0.9 km/h) were analyzed. Eleven players with non-contact injuries were identified and matched with players without any injuries within the same time interval. Players were monitored using GPS and LPS tracking to calculate ACWR_HMLD on a daily basis over the course of one competitive season. Relationships between different endurance performance parameters (v2, v4, v_LT, VO_2max) and the ACWR_HMLD, AW, CW were established for statistical analysis. An area under the curve analysis (AUC) was performed. Based on the four weeks preceding the non-contact injuries, the CW, especially for the last two weeks before the injury, proved to be the most suitable parameter to estimate the risk of injury. The highest significant AUC value (0.81, 95% CI: 0.59–1.00) was calculated for the CW (last week before injury) in relation to the v_LT (suitable cut-off: 0.04 km; sensitivity: 78%, specificity: 80%). With regard to the injury rate, the ACWR_HMLD seems to be the most appropriate method of calculation, especially for CW related to EP (v_LT). The sole use of ACWR, AW, and CW is not recommended. Full article

This paper proposes a novel path-following method for autonomous ships that optimizes overall performance by balancing course changes and convergence to the original route. The proposed method extends the line-of-sight (LOS) guidance law by dynamically adjusting key parameters based on the ship’s cross-track error (XTE) and the distance of new course considering maneuvering characteristics. By incorporating these maneuvering characteristics, the method enables more precise adjustments during course changes, improving overall path-following performance. Simulation results showed that the proposed method outperformed three existing methods, including the traditional LOS guidance law, by minimizing overshoot and maintaining reasonable XTE during larger course changes. It achieved the lowest mean absolute cross-track error (MAE) while also significantly reducing the total time required to follow the path, highlighting its superior accuracy and efficiency in path following. These outcomes highlight the method’s potential to enhance significantly the path-following capabilities of autonomous vessels, contributing to greater efficiency and accuracy in pre-determined route navigation. Full article

Although the precise molecular mechanisms responsible for neuronal death and motor dysfunction in late-onset Parkinson’s disease (PD) are unknown, evidence suggests that mitochondrial dysfunction and neuroinflammation occur early, leading to a collective increase in reactive oxygen species (ROS) production and oxidative stress. However, the lack of methods for tracking oxidative stress in the living brain has precluded its use as a potential biomarker. The goal of the current study is to address this need through the evaluation of the first superoxide (O₂^•−)-sensitive radioactive tracer, [¹⁸F]ROStrace, in a model of late-onset PD. To achieve this goal, MitoPark mice with a dopaminergic (DA) neuron-specific deletion of transcription factor A mitochondrial (Tfam) were imaged with [¹⁸F]ROStrace from the prodromal phase to the end-stage of PD-like disease. Our data demonstrate [¹⁸F]ROStrace was sensitive to increased oxidative stress during the early stages of PD-like pathology in MitoPark mice, which persisted throughout the disease course. Similarly to PD patients, MitoPark males had the most severe parkinsonian symptoms and metabolic impairment. [¹⁸F]ROStrace retention was also highest in MitoPark males, suggesting oxidative stress as a potential mechanism underlying the male sex bias of PD. Furthermore, [¹⁸F]ROStrace may provide a method to identify patients at risk of Parkinson’s before irreparable neurodegeneration occurs and enhance clinical trial design by identifying patients most likely to benefit from antioxidant therapies. Full article