Search Results (2,049)

In today’s increasingly data-driven educational landscape, teachers are expected to use data to inform instructional decisions. However, effective data use depends not only on statistical competence but also on motivation, attitudes, and academic self-concept. This study examines how these factors influence student teachers’ readiness to engage with standardized assessment data. A survey of 164 Flemish primary education student teachers assessed their motivation, attitudes toward data use, and academic self-concept. Cluster analysis identified four distinct profiles, ranging from highly competent yet disengaged users to low-performing but externally motivated individuals, highlighting significant variability in data engagement. A pre- and post-test study design involving an e-course on basic statistical concepts demonstrated that targeted instruction can enhance perceived competence, particularly in areas such as box plot interpretation. Findings suggest that technical training alone is insufficient to promote sustained data use; fostering intrinsic motivation, positive attitudes, and a strong academic self-concept is essential for long-term engagement with data. Full article

Virtual fencing (VF) offers a promising alternative to conventional or electrified fences for managing livestock grazing distribution. This study evaluated the behavioral responses of 25 Rarámuri Criollo cows fitted with Nofence^® collars in Pine Valley, CA, USA. The VF system was deployed in chaparral rangeland pastures. The study included a 14-day training phase followed by an 18-day testing phase. The collar-recorded variables, including audio warnings and electric pulses, animal movement, and daily typical behavior patterns of cows classified into a High or Low virtual fence response group, were compared using repeated-measure analyses with mixed models. During training, High-response cows (i.e., resistant responders) received more audio warnings and electric pulses, while Low-response cows (i.e., active responders) had fewer audio warnings and electric pulses, explored smaller areas, and exhibited lower mobility. Despite these differences, both groups showed a time-dependent decrease in the pulse-to-warning ratio, indicating increased reliance on audio cues and reduced need for electrical stimulation to achieve similar containment rates. In the testing phase, both groups maintained high containment with minimal reinforcement. The study found that Rarámuri Criollo cows can effectively adapt to virtual fencing technology, achieving over 99% containment rate while displaying typical diurnal patterns for grazing, resting, or traveling behavior. These findings support the technical feasibility of using virtual fencing in chaparral rangelands and underscore the importance of accounting for individual behavioral variability in behavior-based containment systems. Full article

The implementation of Building Information Modelling (BIM) in public design processes enhances efficiency, transparency, and sustainability. However, public agencies often encounter significant barriers, particularly regarding organisational and managerial readiness. This study develops a BIM implementation framework tailored to the specific needs of an Italian public agency. The research adopts a qualitative approach, combining 15 semi-structured interviews with process mapping Using (Business Process Modeling Notation) BPMN. The current as-is workflows were analysed and validated by internal stakeholders. Based on this analysis, strategic objectives were defined, relevant (Building Information Modelling) BIM uses were selected, and revised to-be processes were proposed, integrating new roles and responsibilities according to the standards. The framework addresses both technical and organisational dimensions of BIM adoption, highlighting the need for training, coordination, and stakeholder engagement. The main outcomes include a structured process model, a priority-based selection of BIM uses, and a role matrix supporting organisational transformation. The added value for researchers lies in the replicable methodology that combines empirical process mapping with implementation planning. For practitioners, especially consultants in sustainable design, the study offers a practical roadmap for aligning BIM adoption with project goals, regulatory compliance, and environmental performance targets in complex public sector contexts. Full article

Small-scale fisheries and aquaculture play a crucial role in securing food, income, and nutrition for millions, especially in the Global South. Rural small-scale aquaculture (SSA) is characterized by limited investment and technical training among farmers, diversification and dispersion of farms over large areas, reduced access to competitive markets for inputs and products, and family labor. Small-scale integrated circular aquaponic (ICAq) systems, in which systems’ component outputs are transformed into component inputs, have significant potential to increase circularity and promote economic development, especially in a rural context. We offer an integrated and comprehensive approach centered on aquaponics or aquaponic farming for small-scale aquaculture units. It aims to identify and describe a series of circular processes and causal links that can be implemented based on deep study in SSA and ICAq. Circular processes to treat by-products in ICAq include components like composting, vermicomposting, aerobic and anaerobic digestion, silage, and insect production. These processes can produce ICAq inputs such as seedling substrates, plant fertilizers, bioenergy, or feed ingredients. In addition, the plant component can supply therapeutic compounds. Further research on characterization of aquaponic components outputs and its quantifications, the impact of using circular inputs generated within the ICAq, and the technical feasibility and economic viability of circular processes in the context of SSA is needed. Full article

Background and Objectives: Peripheral vascular access in infants is a frequent but technically challenging procedure due to the anatomical characteristics of this population. Repeated failed attempts may increase complications and emotional stress for both patients and healthcare professionals. This systematic review aimed to evaluate the efficacy and safety of ultrasound-guided peripheral vascular cannulation compared to the conventional or “blind” technique in infants. Materials and Methods: A systematic review was conducted in accordance with PRISMA guidelines. The PubMed database was searched for studies published between 2017 and 2025. Studies comparing both techniques in infants under two years of age were selected, evaluating variables such as the number of punctures, first-attempt success, healthcare staff perception, associated stress, and the role of simulation in training. Results: Eleven studies were included, comprising clinical trials, observational studies, and training program assessments from different countries. Most reported a higher first-attempt success rate with the ultrasound-guided technique (often exceeding 85%), along with fewer punctures and complications, particularly among less-experienced professionals. Improvements in staff perception were also observed following structured training. The impact on stress experienced by patients and families was less frequently assessed directly, although some studies reported indirect benefits. Conclusions: Ultrasound-guided peripheral vascular cannulation appears to be more effective and safer than the conventional technique in infants, particularly in complex or critical care contexts. Its implementation requires specific training and appropriate resources but could significantly improve clinical outcomes and the pediatric patient experience. Full article

Background/Objectives: Pathology is often perceived as a technical medical specialty that lacks direct contact with the patient. However, oncological histopathological diagnosis requires a high degree of moral and emotional responsibility. The objective of this study was to investigate how empathy is manifested toward the “invisible” patient, the emotional impact on pathology staff, and potential repercussions in their personal lives. Method: We conducted a descriptive, cross-sectional study with a quantitative component, using an anonymous 22-item questionnaire among Romanian pathologists and medical personnel working in pathology services. The questionnaire was focused on three research directions: professional empathy in the absence of direct patient contact, the emotional impact of oncologic diagnosis on medical personnel in pathology departments, and the carryover of emotions from professional to personal life. A total of 165 respondents were included in the study (physicians, technicians, registrars). Results: Most of the respondents consider that the absence of the patient’s direct contact does not cancel the empathy, this being felt in a cognitive and more natural way. Over 60% of the respondents see oncologic histopathological diagnosis as an emphatic medical act. Over 80% of the respondents experience a sense of emotional responsibility and 70% consider that professional training does not include adequate emotional support. There is a high interest in empathy and psychological support. The professional activity of a pathologist may influence sleep, dreams, and the perception on their own health status. Diagnosing pediatric or young patients is perceived as particularly emotionally challenging. Collegial support is moderate and discussion about professional stress is rare. Conclusions: Empathy is present and relevant in pathology, despite the absence of direct patient interaction. Oncological diagnostics has a significant emotional impact on pathology department personnel, with the need to acknowledge the emotional dimension of the profession and to integrate psychological support mechanisms into pathology practice. Full article

Poultry feces, a critical biomarker for health assessment, requires timely and accurate pathological identification for food safety. Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. Key innovations include the following: (1) Integrating MASA(Manhattan self-attention)and DSconv (Depthwise Separable convolution) into the backbone network to mitigate feature confusion. (2) Utilizing a pre-trained BERT to extract textual semantic features, reducing annotation dependency and cost. (3) Designing a lightweight Gated Cross-Attention (GCA) module for dynamic multimodal fusion, achieving a 41% parameter reduction versus cross-modal transformers. Experiments demonstrate that MMCD significantly outperforms single-modal baselines in Accuracy (+8.69%), Recall (+8.72%), Precision (+8.67%), and F1 score (+8.72%). It surpasses simple feature concatenation by 2.51–2.82% and reduces parameters by 7.5M and computations by 1.62 GFLOPs versus the base ResNet50. This work validates multimodal fusion’s efficacy in pathological fecal detection, providing a theoretical and technical foundation for agricultural health monitoring systems. Full article

This study examined the effects of a short-term ballistic training program on neuromuscular performance and strength-deficit (SDef) in elite youth female soccer players. Twenty-two under-20 athletes completed a 4-week intervention during the pre-season phase, comprising 12 loaded and 8 unloaded ballistic training sessions performed at maximal intended velocity. Pre- and post-intervention assessments included vertical jumps (squat jump [SJ], countermovement jump [CMJ]), sprinting speed (5, 10, and 20 m), one-repetition maximum (1RM) and peak force (PF) in the half-squat (HS), and peak power and velocity during jump squats (JS) at 30% of 1RM. SDef was calculated as the percentage difference in PF between 1RM in the HS and 30% 1RM. Significant improvements were observed in SJ, CMJ, sprint speed, 1RM-strength, and bar-derived mechanical outputs (ES = 1.18–1.66; p < 0.05), with no significant changes in SDef. These results indicate that elite youth female soccer players can improve strength-, power-, and speed-related capacities without compromising force production at higher movement velocities (thus maintaining their SDef). The improvements observed likely reflect the combined effect of a high-frequency, velocity-oriented training approach and a concurrent reduction in traditional technical–tactical (i.e., soccer-specific) training volume. This is the first study to demonstrate that ballistic exercises alone—when properly structured—can enhance neuromuscular performance in female soccer players without increasing SDef. These findings provide practical guidance for practitioners aiming to optimize physical development in team-sport athletes without relying on heavier training loads or extended resistance training sessions—and, especially, without compromising their ability to apply force at higher velocities. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

The shortage of skilled professionals in technical fields is further compounded by a lack of qualified teachers in STEM subjects, particularly in craft education, which is vital for developing technical competencies at the elementary level. The present study investigates the professionalisation of teachers in craft education and explores the prevalence and reasons for out-of-field teaching across three German federal states. The data presented herein were collected through an online survey administered in 2023 among teaching professionals in Mecklenburg-Vorpommern, Sachsen, and Thüringen. The questionnaire was disseminated via head teachers to 1467 elementary schools, yielding a self-selection sample of 284 craft education teachers. The survey incorporated both closed- and open-ended questions, encompassing inquiries into teacher qualifications, subject-specific competence, and lesson planning. Quantitative data were analysed descriptively. The evaluation of open-ended responses employed a content-structuring content analysis approach, utilising categories that were inductively developed. The findings indicate that a considerable proportion of craft education is taught by educators who lack formal qualifications, thereby giving rise to concerns regarding the quality of instruction. The underlying factors contributing to this phenomenon include teacher shortages, personal interests, prior experience, and limited professional development opportunities. The findings emphasise the pressing necessity for enhanced teacher education and targeted training programmes to bolster instructional quality in technically oriented subjects. Full article

With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. However, existing models often overlook the spatial deflection correlations among monitoring points. Therefore, this study proposes a novel deep learning framework, CGCA (Convolutional Gated Recurrent Unit with Cross-Attention), which integrates ConvGRU and cross-attention mechanisms. The model achieves spatio-temporal feature extraction and deformation prediction via an encoder–decoder architecture. Specifically, the convolutional structure captures spatial dependencies between monitoring points, while the recurrent unit extracts time-series characteristics of deformation. A cross-attention mechanism is introduced to dynamically weight the interactions between spatial and temporal data. Additionally, the model incorporates multi-dimensional inputs, including full-depth inclinometer measurements, construction parameters, and tube burial depths. The optimization strategy combines AdamW and Lookahead to enhance training stability and generalization capability in geotechnical engineering scenarios. Case studies of foundation pit engineering demonstrate that the CGCA model exhibits superior performance and robust generalization capabilities. When validated against standard section (CX1) and complex working condition (CX2) datasets involving adjacent bridge structures, the model achieves determination coefficients (R²) of 0.996 and 0.994, respectively. The root mean square error (RMSE) remains below 0.44 mm, while the mean absolute error (MAE) is less than 0.36 mm. Comparative experiments confirm the effectiveness of the proposed model architecture and the optimization strategy. This framework offers an efficient and reliable technical solution for deformation early warning and dynamic decision-making in foundation pit engineering. Full article

Light alloy wheel rims, despite their widespread use, remain vulnerable to fatigue-related defects and mechanical damage. This study presents a method for assessing their technical condition based on acoustic parameter analysis and classification using a deep neural network. Diagnostic data were collected using a custom-developed ADF (Acoustic Diagnostic Features) system, incorporating the reverberation time (T₆₀), sound absorption coefficient (α), and acoustic energy (E). These parameters were measured during laboratory fatigue testing on a Wheel Resistance Test Rig (WRTR) and from used rims obtained under real-world operating conditions. The neural network was trained on WRTR data and subsequently employed to classify field samples as either “serviceable” or “unserviceable”. Results confirmed the high effectiveness of the proposed method, including its robustness in detecting borderline cases, as demonstrated in a case study involving a mechanically damaged rim. The developed approach offers potential support for diagnostic decision-making in workshop settings and may, in the future, serve as a foundation for sensor-based real-time rim condition monitoring. Full article

The preparedness of healthcare workers for disaster situations depends on their technical skills, disaster knowledge, and psychosocial strength, including teamwork and emotional regulation. This study aims to assess disaster preparedness among healthcare professionals in primary healthcare centers (PHCs) in Alqurayat, Saudi Arabia, with a specific focus on evaluating technical competencies, psychosocial readiness, and predictive modeling of preparedness levels. A mixed-methods approach was employed, incorporating structured questionnaires, semi-structured interviews, and observational data from disaster drills to evaluate the preparedness levels of 400 healthcare workers, including doctors, nurses, and administrative staff. The results showed that while knowledge (mean: 3.9) and skills (mean: 4.0) were generally moderate to high, notable gaps in overall preparedness remained. Importantly, 69.5% of participants reported enhanced readiness following simulation drills. Machine learning models, including Random Forest and Artificial Neural Networks, were used to predict preparedness outcomes based on psychosocial variables such as emotional intelligence, teamwork, and stress management. Sentiment analysis and topic modeling of qualitative responses revealed key themes including communication barriers, psychological safety, and the need for ongoing training. The findings highlight the importance of integrating both technical competencies and psychosocial resilience into disaster management programs. This study contributes an innovative framework for evaluating preparedness and offers practical insights for policymakers, disaster planners, and health training institutions aiming to strengthen the sustainability and responsiveness of primary healthcare systems. Full article

Emergency preparedness training in organizations frequently involves simple evacuation drills triggered by fire alarms, limiting the opportunities for broader skill development. Digital technologies, particularly virtual reality (VR), offer promising methods to enhance learning for handling incidents and evacuations. However, implementing VR-based training remains challenging due to unclear integration strategies within organizational practices and a lack of empirical evidence of VR’s effectiveness. This paper explores how VR-based training tools can be implemented in schools to enhance emergency preparedness among students, teachers, and staff. Following a design science research process, data were collected from a questionnaire-based study involving 12 participants and an exploratory study with 13 participants. The questionnaire-based study investigates initial attitudes and willingness to adopt VR training, while the exploratory study assesses the VR prototype’s usability, realism, and perceived effectiveness for emergency preparedness training. Despite a limited sample size and technical constraints of the early prototype, findings indicate strong student enthusiasm for gamified and immersive learning experiences. Teachers emphasized the need for technical and instructional support to regularly utilize VR training modules, while firefighters acknowledged the potential of VR tools, but also highlighted the critical importance of regular drills and professional validation. The relevance of the results of utilizing VR in this context is further discussed in terms of how it can be integrated into university curricula and aligned with other accessible digital preparedness tools. Full article

Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize fabric-based resistive stretching sensors, focusing on both static and dynamic performance, for application in a smart vascular simulator for surgical training. Five sensors, called #1–#5, were developed using conductive fabrics integrated into soft silicone. Stability and fatigue tests were performed to evaluate their behavior. The surface structure and fiber distribution were analyzed using digital microscopy and scanning electron microscopy, while element analysis was performed via Energy-Dispersive X-ray Spectroscopy. Sensors #1 and #3 are the most stable with a low relative standard deviation and good sensitivity at low strains. Sensor #3 showed the lowest hysteresis, while sensor #1 had the widest operating range (0–30% strain). Although all sensors showed non-monotonic behavior across 0–100% strain, deeper investigation suggested that the sensor response depends on the configuration of conductive paths within and between fabric layers. Soft fabric-based resistive sensors represent a promising technical solution for physical simulators for surgical training. Full article