Search Results (100)

Dairy cattle are particularly susceptible to welfare challenges that can negatively impact their behaviour, physiological responses, milk yield, and overall health. These challenges include rough handling by unfamiliar people, extreme climatic conditions, inconsistent feeding, regrouping, and transportation. This pilot study investigated the interrelationships between behavioural and physiological responses and milk production of dairy cows reared in a smallholder stall housing system, restricting cow movement to standing and lying down in the Rajshahi district of Bangladesh. Fifty cows were observed, with all cows assessed over two days. Video recordings were made using a smartphone during the morning milking routine, focusing on specific behaviours: ear position (upright, forward, backward, downward), sniffing, stepping, and tail movement. Observations were conducted during the first two minutes after the start of the milking process manually. Physiological responses recorded included rectal temperature, vaginal temperature, and heart rate. Productivity outcomes include body condition score (BCS), milk yield per milking, daily milk yield, and milking frequency. Environmental data were collected to calculate the Temperature-Humidity Index (THI), which averaged 84.5 (±1.78 SD), exceeding the thermal comfort zone for dairy cattle (THI < 72). Statistical analyses comprised principal component analysis (PCA) to determine the most descriptive variables. Pearson correlation assesses the direction and strength of relationships between variables, and simple linear regression to evaluate milk production variables using all other variables as predictors. Results showed that cows displaying an upright ear position during milking had a positive relation with average daily milk yields (r = 0.456, p = 0.001). Milk yield was negatively correlated with THI (r = −0.331, p = 0.02), forward ear position (r = −0.356, p = 0.01), kicking (r = −0.531, p < 0.001), and sniffing behaviour during milking (r = −0.511, p < 0.001). The daily milk yields were positively correlated with body condition scores of milking cows (r = 0.661, p < 0.001). Rectal and vaginal temperatures were positively associated with each other (r = 0.777, p < 0.001). Similarly, rectal temperature and heart rate were positively associated with each other (r = 0.511, p < 0.001). Therefore, ear position, kicking, and sniffing behaviour during milking were used to assess emotional states, with forward ears associated with signs of stress. Healthy cows with higher body condition scores (BCS) produced more milk, requiring farmers to milk them more than twice a day. This pilot study highlights key behaviour and physiological responses relevant to stress and milk production, and thus overall welfare. For greater generalisability, future research should incorporate larger sample sizes and repeated environmental assessments over extended periods. Full article

This article introduces the Evidence Propagation Calculus (EPiC), an operational framework for first-order reasoning built on a simple but productive observation: familiar inference patterns such as Modus Ponens and Modus Tollens behave like the movement of evidential markers across a structured graph. Positive evidence at an antecedent propagates forward to the consequent; negative evidence at a consequent propagates backward. When both markers coexist at a node, the system is locally inconsistent but not operationally broken. To make this observation precise, EPiC grounds reasoning in a four-valued evidential domain $\mathbb{V}=\{N,T,F,B\}$, where N denotes absence of evidence, T positive evidence, F negative evidence, and B their coexistence. Each logical connective is assigned a local evidential table, and inference is treated uniformly as the progressive restriction of admissible configurations under an evidential order: inadmissible values are eliminated, minimal surviving values are selected as the next effective evidential states, and the resulting restrictions propagate across shared variables. Compound formulas are decomposed into families of local unary and binary constraints through auxiliary variables, making the propagation process explicit and structurally uniform. Within this setting, Modus Ponens, Modus Tollens, and polarity-switching negation are not postulated as primitive rules. They emerge as derived consequences of the same local table calculus. The framework distinguishes different operational routes of justification. In some cases, positive support reaches the target formula directly through successive local restrictions. In others, propagation first stabilizes the relevant components and the target occurrence is then fixed by the corresponding connective table. Consistency is not a second basic notion of justification but a distinguished property of certain justified outcomes. The article establishes local and global soundness, conservativity over the classical fragment, and a conditional adequacy result. It further develops a translation between decomposed formulas and informational graphs, with a reverse reconstruction theorem for well-formed graphs. The result is a unified operational account of first-order reasoning situated between model-theoretic and proof-theoretic approaches, in which semantics, propagation, and graphical structure are mutually supporting rather than independently layered. Full article

Subterranean locomotion challenges animals to maintain orientation and efficiently navigate confined spaces where vision is limited and local geometry is uncertain. Mole crickets (Gryllotalpidae), which regularly travel through self-excavated tunnels, provide a useful model for studying mechanosensory control of locomotion under these conditions. We hypothesized that backward walking, a prominent component of the mole cricket’s behavioral repertoire, is supported by complementary tactile input from the antennae and cerci. Adult Gryllotalpa tali were filmed while walking forward and backward in a narrow, straight tunnel arena under red light using high-speed video. Markerless pose tracking was used to quantify antennal and cercal tip movements, orientations, and wall-contact events in body and arena coordinates. Backward walking produced clear changes in tactile sampling behavior: antennae were reoriented and extended more posteriorly, while lateral cercal movements increased sensory coverage and posterior tactile input. Wall-contact monitoring suggested more frequent touching during backward locomotion by the antennae. These findings indicate that mole crickets adaptively reorganize active and passive mechanosensory sampling when moving backward, potentially improving boundary detection, stabilizing body posture, negotiating tunnel constraints, and supporting locomotion-related decision making during locomotion in tunnels. Full article

School physical education (PE) has often relied on linear progressions in which teachers demonstrate, pupils practise prescribed techniques, and achievement is judged through visible reproduction of preferred movement forms. Non-linear pedagogy (NLP) and the constraints-led approach (CLA) offer an alternative ecological-dynamics rationale for supporting pupils’ integral development, including motor competence, adaptable movement capability, and dispositions for lifelong physical activity and physical literacy. However, existing review work has not sufficiently explained why principled NLP/CLA designs remain unevenly enacted across ordinary school PE systems. We conducted a theory-informed interpretive synthesis drawing on critical interpretive synthesis and thematic synthesis. A structured English-language search of ERIC, SPORTDiscus, Scopus, and Google Scholar (2010–2025) was combined with title-and-abstract screening, full-text assessment, backward and forward citation chaining, and purposive retention of foundational or Singapore-context records, and reporting was strengthened through PRISMA-like transparency aids adapted to interpretive synthesis. The final coded corpus comprised 36 included sources: 9 empirical studies, 3 reviews, 9 conceptual or practitioner texts, 6 theoretical or critical sources, 4 review-method papers, and 5 Singapore policy, context, or professional-learning documents used as an illustrative policy lens. Through iterative coding, descriptive theme development, and analytical integration, we identified six coherence domains shaping enactment: teacher beliefs and knowledge; curriculum and lesson structure; assessment and accountability; systemic and resource constraints; professional development ecosystems; and stakeholder and cultural factors. These domains informed a Systemic Coherence Framework spanning micro, meso, and macro levels. The synthesis suggests that assessment coherence may be a high-leverage condition because it links curriculum legitimacy, reporting, and teacher defensibility, but its comparative influence across domains remains a hypothesis for future empirical testing. The framework is offered as an analytic heuristic rather than a prescriptive model and is intended to help researchers, teacher educators, school leaders, and policy actors diagnose where curriculum intent, assessment language, professional learning, and organisational routines support or inhibit ecologically informed practice. Full article

Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context. Full article

Manual wheelchair users with spinal cord injury (SCI) rely heavily on upper-limb function for independent mobility, which often leads to cumulative musculoskeletal loading due to repetitive propulsion. To address limitations associated with conventional unidirectional pushrim propulsion, this study presents the design and development of a detachable bidirectional wheelchair propulsion system that enables mode-dependent push and pull inputs through a mechanically reconfigurable lever mechanism. The proposed system allows conventional forward propulsion through forward pushing, while enabling alternative propulsion patterns through lever mode switching. Depending on the selected mode, either pushing or pulling inputs can be mechanically coupled to forward or backward wheel rotation, without requiring powered actuation or permanent modification of the wheelchair structure. This design expands the range of feasible propulsion strategies by allowing a selectable relationship between propulsion input direction and wheelchair movement direction through mechanical mode switching via a purely mechanical transmission architecture. The system is designed as a modular add-on compatible with standard manual wheelchairs, incorporating a clamp-based detachable interface and a gear-driven bidirectional transmission mechanism. Design considerations emphasize mechanical simplicity, controllability, and compatibility with existing wheelchair configurations, while preserving baseline pushrim functionality. This design-focused study reports the engineering rationale, mechanical architecture, and feasibility of a detachable bidirectional propulsion concept for manual wheelchairs. By explicitly documenting the system configuration and mode-switching logic, this work aims to provide a transparent design framework that can support future experimental validation and user-centered evaluation of bidirectional propulsion strategies for manual wheelchair users with SCI. Full article

In complex urban traffic scenes, reliable pedestrian trajectory prediction is essential for Automated and Connected Electric Vehicles (ACEVs) and active safety systems. Despite recent progress, many existing approaches still suffer from limited long-term prediction accuracy, redundant temporal features, and high computational cost, which restricts their deployment on vehicles with constrained onboard resources. To address these issues, this paper presents a lightweight trajectory prediction framework named BiTraP-DGF. The model adopts parallel Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) temporal encoders to extract motion information at different time scales, allowing both short-term motion changes and longer-term movement tendencies to be captured from observed trajectories. A conditional variational autoencoder (CVAE) with a bidirectional GRU decoder is further employed to model multimodal uncertainty, where forward prediction is combined with backward goal estimation to guide trajectory generation. In addition, a gated sparse attention mechanism is introduced to suppress irrelevant temporal responses and focus on informative time segments, thereby reducing unnecessary computation. Experimental results on the JAAD dataset show that BiTraP-DGF consistently outperforms the BiTraP-NP baseline. For a prediction horizon of 1.5 s, CADE is reduced by 20.9% and CFDE by 22.8%. These results indicate that the proposed framework achieves a practical balance between prediction accuracy and computational efficiency for autonomous driving applications. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

In modern sports, straight-line sprinting alone is insufficient for assessing overall sprint performance, as athletes must also decelerate and change direction efficiently. Existing methods lack a single metric that integrates all abilities, enabling holistic assessment. This study aimed to develop a comprehensive and novel measurement of multidirectional sprinting ability. Fifty-four university athletes (21.0 ± 1.5 years; 69.6 ± 9.1 kg; 172.6 ± 7.8 cm) performed linear sprints, decelerations, and 45°, 90°, and 135° change of direction (COD) tests in both directions over 30 m. Sprint accelerations and decelerations were recorded using a Stalker ATS II radar gun, while COD times were measured with stationary time gates. Sprint velocities were used to generate a multidirectional sprint area (MDSA), which was divided into forward, backward, left, and right sections. The MDSA method is calculated as the area of the octagonal polygon created by plotting eight velocity vectors from different angles of sprints. Paired t-tests compared area differences across directions, and ANOVA tests were used to compare sporting codes and sex. The resulting model reported differences across sporting codes (p < 0.001), sex (p < 0.001), the total area value (p < 0.001), and total area percentage (p < 0.001). The results showed a significant difference between forward and backward accelerations (p < 0.001), but no significant difference between left and right movements (p = 0.244). The MDSA method offers a reliable, quantitative intra-session approach for assessing athletes’ multidirectional sprint abilities by calculating the octagonal area on the basis of velocity data. This holistic analysis identifies asymmetries and performance weaknesses, providing valuable insights for coaches. Full article

Background/Objectives: Children with dyslexia report poor motor control; several studies have shown poor eye movements control during reading and important body instability in these children. The present study aimed to test in children whether reading and postural abilities in children with dyslexia could benefit from a short combined reading and postural training program. Methods: Thirty-two children with dyslexia were randomly assigned to training group (G1) or control group (G2). All participants completed eye movements recording during reading and postural recording under an unstable support before and after the intervention. G1 underwent a 10 min combined reading and postural training while G2 had a 10 min rest. During reading, the reading time, the duration of fixations, as well as the occurrence, amplitude, and number of forward saccades (saccades from the left to the right) and backward saccades (saccades from the right to the left) were measured. The PII (postural instability index) was measured under unstable support. G1 exhibited a significant decrease after training in reading time, fixation duration, and the number of forward saccades. Furthermore, we observed a significant reduction in postural instability. In contrast, G2 failed to show any significant changes in eye movements and postural recordings. Conclusions: We suggest such a combined reading and postural training approach could help dyslexic children to improve motor abilities. Adaptive mechanisms through improved cerebellar activity could be responsible for such motor enhancement. Full article

This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels. Full article

Background: At present, the rotational function of the hip adductor muscle group remains unclear. This study aimed to clarify the rotational function and stabilizing role of the pectineus, adductor longus, and adductor brevis (adductor muscle group) based on anatomical findings and T₂ values (ms) obtained from magnetic resonance imaging (MRI). T₂ values are prolonged in tissues with higher water content, and in skeletal muscle, it has been demonstrated that T₂ values increase in proportion to exercise intensity. Methods: Using fixed specimens (n = 6, aged 61–96 years), we observed the three-dimensional arrangement of muscles in the neutral position of the hip joint and observed the extension or shortening of muscles associated with passive maximum internal and external rotation of the hip joint. In addition, we evaluated the activity of the adductor muscle group by T₂ values (ms) from MRI pre- and post-internal rotation (forward step with the left leg) and pre- and post-external rotation (backward step with the left leg) movements of the right hip joint in a standing position (n = 8, healthy adult subjects, mean age 29.1 ± 5.3 years). Results: Regarding functional anatomy, the arrangement of the gluteus minimus and adductor muscle groups was almost parallel across the femoral neck. In the evaluation of adductor muscle group activity using MRI, the percent change in T₂ values (%) of the pectineus was 6.38 ± 1.35 pre- and post-internal rotation and 1.35 ± 0.71 pre- and post-external rotation, whereas that of the adductor longus and brevis was 4.84 ± 1.31 pre- and post-internal rotation and 1.31 ± 0.68 pre- and post-external rotation. The percent change in T₂ values pre- and post-internal rotation exercise was significantly greater than that pre- and post-external rotation exercise in the pectineus, adductor longus, and brevis muscles (p < 0.05). Conclusions: The adductor muscle groups are suggested to contribute to joint stability in the coronal plane and provide joint internal rotation in the standing position. Full article

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

Background/Objectives: Obesity in adolescence is associated with a deterioration in cognitive functions, with significant implications for psychophysical well-being and academic performance. Recent studies highlight the importance of integrated interventions that combine nutrition education and physical activity to promote the overall health of students. The present study aims to evaluate the efficacy of an integrated intervention based on nutritional education and conscious body movement in improving cognitive functions, perceived well-being and nutritional knowledge in lower secondary school students with indicators of overweight and obesity. Methods: A quasi-experimental design with randomization at the class level was adopted, involving 60 students divided into an experimental group and control group. The intervention was divided into twelve weeks of activities, divided between nutritional education modules and physical activity courses. Standardized tests for the assessment of cognitive functions (Digit Span Forward, Digit Span Backward, Stroop Test, Trail Making Test B), motor tests (6-Minute Walk Test, Sit and Reach Test) and a food knowledge questionnaire were administered before and after the intervention. Results: The experimental group showed significant improvements compared to the control group in all cognitive, motor, and nutritional knowledge measures, indicating the effectiveness of the integrated intervention in promoting cognitive and physical well-being. Conclusions: The findings support the role of school as a generative environment of integrated well-being, suggesting the need to develop and implement curricular programs that integrate nutrition education and physical activity to counteract the negative effects of obesity on cognitive function in adolescents. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article