Search Results (25,196)

Traditional training methods for first responders are often limited by time, resources, and safety constraints, which reduces their consistency and effectiveness. This study focused on two main issues: whether exposure to virtual reality training scenarios induces measurable physiological changes in heart rate and heart rate variability, and whether these responses differ between police and firefighter contexts. The aim of this study was to explore the integration of virtual reality technologies into responder training and to evaluate how biomedical monitoring can be used to assess training effectiveness. A pilot measurement was conducted with ten participants who completed systematic crime scene investigation scenarios in both domains. Heart activity was continuously recorded using a wearable sensor and analyzed for heart rate and heart rate variability parameters, while cognitive load and task performance were also assessed. The collected data were statistically evaluated using tests of normality and paired comparisons between baseline and virtual reality phases. The results showed a significant increase in heart rate and a decrease in heart rate variability during virtual reality exposure compared to baseline, with higher cognitive load and success rates in police scenarios compared to firefighter scenarios. These findings indicate that virtual reality scenarios can elicit measurable psychophysiological responses and highlight the potential of combining immersive technologies with biomedical monitoring for the development of adaptive and effective training methods for first responders. Full article

Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article

This study introduces a novel mathematical framework for a machine learning algorithm tailored to address linear regression problems in the presence of non-Gaussian estimation noise. In particular, we focus on Bernoulli–Gaussian noise, which frequently occurs in practical scenarios such as wireless communication channels and signal processing systems. We apply our framework within the context of wireless systems, particularly emphasizing its utility in channel estimation tasks. This article demonstrates the efficacy of linear regression in estimating wireless channel fading coefficients under the influence of additive Bernoulli–Gaussian noise. Through comparative analysis with Gaussian noise scenarios, we underscore the indispensability of the proposed framework. Additionally, we evaluate the performance of the maximum-likelihood estimator using gradient descent, highlighting the superiority of estimators tailored to non-Gaussian noise assumptions over those relying solely on simplified Gaussian models. Full article

Background: Uchi-mata is one of the most frequently used throwing techniques in judo, yet little is known about the kinematic factors distinguishing specialists from non-specialists. This study compared lower-limb kinematics during uchi-mata across its three phases in elite judokas. Methods: Forty athletes (12 female, 28 male; 24.5 ± 5.9 years) were classified as specialists (n = 20) or non-specialists (n = 20). Photogrammetry assessed hip, knee, and foot displacement, velocity, acceleration, and timing during the Approach, Turning, and Throw phases. Analyses were performed using mixed-effects models with group, phase, and sex as fixed effects, plus exploratory multivariate tests (p < 0.05). Results: Specialists executed faster movements in the Approach (p = 0.036, d = 0.69) and Throw phases (p = 0.010, d = 0.85), showed greater hip displacement during Approach (p = 0.008, d = 0.89), and achieved superior knee and foot displacement in Throw (p = 0.005 and p = 0.003). Final positioning also differed, with specialists displaying higher knee (98.5 ± 14.5 vs. 86.3 ± 17.8 cm, p ≤ 0.001) and foot (121.0 ± 19.7 vs. 104.4 ± 27.4 cm, p = 0.034) heights, but lower hip position (61.9 ± 4.2 vs. 75.6 ± 7.5 cm, p = 0.021). Sex showed no significant effects or interactions, indicating that these group differences were consistent across male and female athletes. Conclusions: Uchi-mata specialists demonstrated superior displacement and velocity control, particularly in the Approach and Throw phases, reflecting greater neuromuscular coordination and efficiency. These findings provide practical markers for coaches and athletes to guide training focused on mobility, strength, and technical drills that enhance hip, knee, and foot displacement, supporting the optimization of uchi-mata performance in elite judo. Full article

Evidence of possible cerebellar involvement in spatial processing, place learning and other types of higher order functions comes mainly from clinical observations, as well as from mutant mice and lesion studies. The latter, in particular, have reported deficits in spatial learning and memory following surgical or neurotoxic cerebellar ablation. However, the low specificity of such manipulations has often made it difficult to precisely dissect the cognitive components of the observed behaviors. Likewise, due to conflicting data coming from lesion studies, it has not been possible so far to conclusively address whether a cerebellar dysfunction is sufficient per se to induce learning deficits, or whether concurrent damage to other regulatory structure(s) is necessary to significantly interfere with cognitive processing. In the present study, the immunotoxin 192 IgG-saporin, selectively targeting cholinergic neurons in the basal forebrain and a subpopulation of cerebellar Purkinje cells, was administered to adult rats bilaterally into the basal forebrain nuclei, the cerebellar cortices or both areas combined. Additional animals underwent injections of the toxin into the lateral ventricles. Starting from two–three weeks post-lesion, the animals were tested on paradigms of motor ability as well as spatial learning and memory and then sacrificed for post-mortem morphological analyses. All lesioned rats showed no signs of ataxia and no motor deficits that could impair their performance in the water maze task. The rats with discrete cerebellar lesions exhibited fairly normal performance and did not differ from controls in any aspect of the task. By contrast, animals with double lesions, as well as those with 192 IgG-saporin given intraventricularly did manifest severe impairments in both reference and working memory. Histo- and immunohistochemical analyses confirmed the effects of the toxin conjugate on target neurons and fairly similar patterns of Purkinje cell loss in the animals with cerebellar lesion only, basal forebrain-cerebellar double lesions and bilateral intraventricular injections of the toxin. No such loss was by contrast seen in the basal forebrain-lesioned animals, whose Purkinje cells were largely spared and exhibited a normal distribution pattern. The results suggest important functional interactions between the ascending regulatory inputs from the cerebellum and those arising in the basal forebrain nuclei that would act together to modulate the complex sensory–motor and cognitive processes required to control whole body movement in space. Full article

This paper proposes a Tibetan judicial event argument extraction method based on machine reading comprehension (MRC) to address the challenges of data scarcity and insufficient model generalization in low-resource language scenarios. Unlike traditional methods, this work models event argument extraction as an MRC task, progressively identifying and extracting various event arguments through a question-guided approach. First, a strategy for constructing event knowledge-enhanced questions tailored to the Tibetan judicial domain is designed. Specifically, interrogative words are formulated for different types of event arguments, and event semantic information is incorporated into questions to effectively disambiguate questions. Second, a deep semantic understanding architecture for Tibetan judicial events based on the CINO (Chinese Minority Pretrained Language Model) is proposed, incorporating a multi-head self-attention mechanism to enhance semantic alignment and global understanding between event sentences and questions. Finally, a two-stage training strategy is proposed for low-resource languages. Training is performed on a general Tibetan machine reading comprehension dataset, followed by task-adaptive fine-tuning on judicial domain data, effectively alleviating the data scarcity issue. Experimental results show that the proposed method achieved an F1-score of 76.59% in the Tibetan judicial event argument extraction task. This research offers new ideas for low-resource language event extraction and is of great significance for promoting intelligent information processing of minority languages. Full article

As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of existing methods, this paper proposes the Hierarchical Triple-Feature Interaction Fusion Network (TFI-Fusion). Based on a hierarchical triple-stream feature interaction mechanism, the network achieves high-quality fusion through a two-stage, separate-model processing approach: In the first stage, a single model extracts low-rank components (representing global structural features) and sparse components (representing local detail features) from source images via the Low-Rank Sparse Decomposition (LSRSD) module, while capturing cross-modal shared features using the Shared Feature Extractor (SFE). In the second stage, another model performs fusion and reconstruction: it first enhances the complementarity between low-rank and sparse features through the innovatively introduced Bi-Feature Interaction (BFI) module, realizes multi-level feature fusion via the Triple-Feature Interaction (TFI) module, and finally generates fused images with rich scene representation through feature reconstruction. This separate-model design reduces memory usage and improves operational speed. Additionally, a multi-objective optimization function is designed based on the network’s characteristics. Experiments demonstrate that TFI-Fusion exhibits excellent fusion performance, effectively preserving image details and enhancing feature complementarity, thus providing reliable visual data support for downstream tasks. Full article

This study examines how the organizational work climate shapes the effectiveness of supervision on employee performance. While traditional management theory assumes supervision universally enhances productivity, we observe a puzzling paradox: facing identical tasks and wage systems, some firms rely heavily on hierarchical supervision while others thrive with minimal oversight. Through a four-month field experiment across two Chinese agricultural enterprises (5851 observations), we test whether the supervision’s effectiveness depends on the alignment between leadership practices and organizational climate. In formal management firms (FMFs) characterized by hierarchical governance and arm’s-length employment relationships, directive supervision significantly reduces task completion times by 0.126 standard deviations, equivalent to approximately 4.3 s or 2.8% of the average completion time, with this effect remaining stable throughout the workday. Conversely, in network-embedded firms (NEFs) operating through trust-based relational contracts and social norms, identical supervisory practices yield no performance gains, as informal social control mechanisms already ensure high effort levels, rendering formal supervision redundant. These findings challenge the “best practices” paradigm in strategic HRM, demonstrating that HR success requires a careful alignment between leadership approaches and the organizational climate—an effective HR strategy is not about implementing standardized practices but about achieving a strategic fit between supervisory leadership styles and existing work climates. This climate–leadership partnership is essential for optimizing both employee performance and organizational success. Full article

Objectives: This study aimed to evaluate the effects of a 12-week Active Breaks (ABs) program on physical, cognitive, and academic outcomes in primary school children. Methods: Eighty primary school students (age: 7.52 ± 0.50) (BMI: 18.35 ± 3.07) were recruited and randomly assigned to three experimental groups—involving creativity-based (CRE) (age: 7.97 ± 0.18 years) (BMI: 20.01 ± 3.59), fitness-based (FIT) (age: 7.93 ± 0.26 years) (BMI: 16.74 ± 1.76), and combined (COM) (age: 7.97 ± 0.18 years) (BMI: 19.38 ± 4.24) ABs—and a control group (CON) (age: 7.42 ± 0.49 years) (BMI: 18.31 ± 2.64). The intervention consisted of two daily sessions (10 min each) three times per week over a 12-week period. Numerical skills, calculation abilities, and arithmetic problem-solving performance were evaluated using the “Test for the Assessment of Calculation and Problem-Solving Skills” (AC-MT 6-11). Attention and concentration performance were assessed using the Reynolds Interference Task (RIT). Motor skill performance was assessed using the MOTORFIT tests. Results: The FIT and CRE groups showed higher improvement in physical performances (p < 0.05). Regarding cognitive outcomes, the COM group outperformed the CON group in the Total Correct Index (p = 0.032). Regarding mathematical performance, all EGs achieved higher results than the CON group (p < 0.042), with the COM group achieving the highest scores in operations, problem-solving, and total scores (p < 0.032). Conclusions: Incorporating structured physical activity through ABs during curricular hours is an effective strategy to enhance physical, cognitive, and academic performance in primary school children. A combined approach appears to be especially beneficial, supporting both physical and cognitive development simultaneously. Full article

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article

Mathematical performance in early education is influenced by a complex interplay of cognitive and affective factors, including language skills, working memory, and anxiety. This study investigated whether working memory and math anxiety, in both explicit numerical situations (ENS) and general classroom situations (GCS), mediate the relationship between general and math-specific vocabulary and math performance in a sample of 467 second-grade students in Chile. Structural equation modelling was employed to test a dual-pathway model in which both working memory and math anxiety served as mediators between vocabulary knowledge and math performance. Results indicated that both general and math-specific vocabulary positively predicted working memory and negatively predicted math anxiety in ENS. In turn, working memory and ENS significantly predicted math outcomes, whereas GCS was not a significant predictor. Indirect effects supported a dual mediation structure, with vocabulary influencing math performance through both cognitive and affective mechanisms. Math-specific vocabulary exerted a slightly stronger total effect than general vocabulary, consistent with its closer alignment to the semantic demands of mathematical tasks. These findings suggest that vocabulary supports early mathematical learning not only by enhancing cognitive processing capacity but also by reducing anxiety in task-specific contexts. Full article

Mobile crowd computing (MCdC) leverages the collective computational resources of nearby mobile devices to execute complex tasks without relying on remote cloud infrastructure. However, existing MCdC systems struggle with device heterogeneity and complex application dependencies, often leading to inefficient resource utilization and poor scalability. This paper presents Honeybee-Tx, a novel dependency-aware work stealing framework designed for heterogeneous mobile device clusters. The framework introduces three key contributions: (1) capability-aware job selection that matches computational tasks to device capabilities through lightweight profiling and dynamic scoring, (2) static dependency-aware work stealing that respects predefined task dependencies while maintaining decentralized execution, and (3) staged result transfers that minimize communication overhead by selectively transmitting intermediate results. We evaluate Honeybee-Tx using two applications: Human Activity Recognition (HAR) for sensor analytics and multi-camera video processing for compute-intensive workflows. The experimental results on five heterogeneous Android devices (OnePlus 5T, Pixel 6 Pro, and Pixel 7) demonstrate performance improvements over monolithic execution. For HAR workloads, Honeybee-Tx achieves up to 4.72× speed-up while reducing per-device energy consumption by 63% (from 1.5% to 0.56% battery usage). For video processing tasks, the framework delivers 2.06× speed-up compared to monolithic execution, with 51.4% energy reduction and 71.6% memory savings, while generating 42% less network traffic than non-dependency-aware approaches. These results demonstrate that Honeybee-Tx successfully addresses key challenges in heterogeneous MCdC environments, enabling efficient execution of dependency-aware applications across diverse mobile device capabilities. The framework provides a practical foundation for collaborative mobile computing applications in scenarios where cloud connectivity is limited or unavailable. Full article

Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting structure from complex, high-dimensional data. In this work, we study the training dynamics of Matrix Product State (MPS) classifiers applied to three-class problems, using both fashion MNIST and hyperspectral satellite imagery as representative datasets. We investigate the phenomenon of grokking, where generalization emerges suddenly after memorization, by tracking entanglement entropy, local magnetization, and model performance across training sweeps. Additionally, we employ information-theory tools to gain deeper insights: transfer entropy is used to reveal causal dependencies between label-specific quantum masks, while O-information captures the shift from synergistic to redundant correlations among class outputs. Our results show that grokking in the fashion MNIST task coincides with a sharp entanglement transition and a peak in redundant information, whereas the overfitted hyperspectral model retains synergistic, disordered behavior. These findings highlight the relevance of high-order information dynamics in quantum-inspired learning and emphasize the distinct learning behaviors that emerge in multi-class classification, offering a principled framework to interpret generalization in quantum machine learning architectures. Full article

The proliferation of social media has made Sentiment Analysis an essential tool for understanding user opinions, particularly in underrepresented language variants such as Mexican Spanish. Recent advances in Large Language Models have made effective sentiment analysis through in-context learning techniques, reducing the need for supervised training. This study compares the performance of zero and few-shot with traditional fine-tuning approaches of tourism-related texts in Mexican Spanish. Two annotated datasets from the REST-MEX 2022 and 2023 shared tasks were used for this purpose. Results show that fine-tuning, particularly with the MarIA model, achieves the best overall performance. However, modern LLMs that use in-context learning strategies, such as Mixtral 8x7B for zero-shot and Mistral 7B for few-shot, demonstrate strong potential in low-resource settings by closely approximating the accuracy of fine-tuned models, suggesting that in-context learning is a viable alternative to fine-tuning for sentiment analysis in Mexican Spanish when labeled data is limited. These approaches can enable intelligent, data-driven digital services with applications in tourism platforms and urban information systems that enhance user experience and trust in large-scale socio-technical ecosystems. Full article

Objectives: The increasing incidence of unruptured intracranial aneurysms (UIAs) has led to significant demands on neurovascular boards. Large language models (LLMs), such as ChatGPT-4, ChatGPT-3.5, Claude, and Atlas GPT, have emerged as tools to support clinical decision-making. This study compares treatment recommendations from these AI models with those of an interdisciplinary neurovascular board to evaluate their accuracy and alignment. Methods: We retrospectively included all 57 patients with UIAs discussed by the neurovascular board in 2023. The board’s consensus decision served as the reference standard. Key clinical and radiographic data, including PHASES, ELAPSS, and UIATS scores, were provided to the AI models. Each model was tasked with recommending either conservative or operative management and specifying the treatment modality (clipping, coiling, flow diverter, or WEB device/flow diverter) where appropriate. AI model recommendations were compared with the board’s decisions for management and the specific treatment modality of the UIA. Results: ChatGPT-4 achieved the highest accuracy in correctly predicting conservative or operative management (89%) and specific treatment types (73%), followed by Atlas GPT (74% accuracy in conservative/operative decisions and 55% accuracy in specific treatment types), Claude (70% accuracy in conservative/operative decisions and 50% accuracy in specific treatment types), and ChatGPT-3.5 (82% accuracy in conservative/operative decisions and 27% accuracy in specific treatment types). ChatGPT-3.5 displayed a strong preference for clipping (94.3%). ELAPSS scores significantly influenced AI recommendations and decision-making, particularly for ChatGPT-4 and ChatGPT-3.5. Follow-up recommendations for conservative management were shorter among AI models, with Claude suggesting the shortest interval (7.72 months) compared to the neurovascular board’s 13.36 months. Conclusions: AI models, particularly ChatGPT-4, align closely with expert neurovascular board decisions and offer promising support for initial clinical decision-making, particularly in resource-limited settings. However, interdisciplinary neurovascular boards remain unreplaceable for UIA management, and AI should be viewed as a complementary tool. The observed improvement from ChatGPT-3.5 to ChatGPT-4 underscores the rapid evolution of AI technology, and further advancements are expected to enhance both performance and accuracy in the future. Full article