Search Results (41,715)

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and physiological circuits. This study builds on machine learning techniques—such as dimensionality reduction, boosted decision trees, and anomaly classification—to develop a conceptual framework for biological circuits. One problem is that machine learning circuits tend to be unrealistically large for use by cellular and physiological systems. I therefore focus on minimal circuits inspired by machine learning concepts, reduced to the cellular scale. Through illustrative models, I demonstrate that small circuits can provide useful classification of anomalies. The analysis also shows how principles from machine learning—such as temporal and atemporal anomaly detection, multivariate signal integration, and hierarchical decision-making cascades—can inform hypotheses about the design and evolution of cellular circuits. This interdisciplinary approach enhances our understanding of cellular circuits and highlights the universal nature of computational strategies across biological and artificial systems. Full article

Inattention and hyperactivity/impulsivity that feature Attention-Deficit/Hyperactivity Disorder (ADHD) may be challenging in the classroom setting. However, little is known about language teachers’ self-efficacy and the approaches they would employ to deal with context-specific ADHD-like behaviours. Therefore, this mixed-method study used the vignette methodology to investigate the self-reported efficacy and teaching approaches of 62 pre-service English language teachers from Japan, Poland, Turkey, and Ukraine in managing ADHD-like behaviours in six hypothetical classroom scenarios. By comparing diverse educational and cultural contexts, the study aimed to identify convergences and divergences in coping with these behaviours to promote evidence-based approaches in inclusive language teaching. Data were gathered using an online questionnaire with both open- and closed-ended questions on a Likert-type scale. The findings indicate that participants feel moderately confident in managing ADHD-like behaviours; however, some statistically significant country-related differences were observed. A number of similar teaching approaches were identified across the sample, but prominent country-specific differences in approaching specific ADHD-like behaviours were present. The approaches used by participants align with evidence-based recommendations for teaching students with ADHD to some extent. The discussed implications of the study inform pre-service teachers’ education and call for approaches that are more universal in design and language-skill-development-oriented. Full article

Nowadays, the use of technology has become increasingly indispensable, leading to prolonged exposure to computers and other screen devices. This situation is common in work areas related to Information and Communication Technologies (ICTs), where people spend long hours in front of a computer. This exposure has been associated with the development of musculoskeletal disorders, among which nonspecific back pain is particularly prevalent. This observational study presents the design of a telerehabilitation strategy based on 3D animations, which is aimed at enhancing the musculoskeletal health of individuals working or studying in ICT-related fields. The intervention was developed through the Moodle platform and designed using the ADDIE instructional model, incorporating educational content and therapeutic exercises adapted to digital ergonomics. The sample included university students in the field of computer science who were experiencing symptoms associated with prolonged computer use. After a four-week intervention period, the results show favorable changes in pain perception and knowledge of postural hygiene. These findings suggest that a distance-based educational and therapeutic strategy may be a useful approach for the prevention and treatment of back pain in academic settings. Full article

In complex electromagnetic environments, especially those with pulsed interference sources, long-period pulsed interference can repeatedly disrupt the real-time data within navigation messages, preventing receivers from obtaining complete message information and significantly extending the time to first fix (TTFF). To address this problem, the interference mechanism is modeled and analyzed from the perspective of navigation message structure. An anti-interference strategy based on navigation message scrambling is proposed, including two key techniques: random scrambling of subframe order and message interleaving encoding. Simulation and experimental results demonstrate that various pulsed interference patterns, with different periods and duty cycles, can significantly impact TTFF. The subframe scrambling method is effective against interference whose period exceeds the subframe duration but is limited when the period is equal to or shorter than the subframe. In contrast, the interleaving method provides more universal resistance across interference patterns. When both techniques are combined, the overall anti-interference performance is further enhanced. Specifically, for interference patterns with periods longer than the subframe duration, the probability that the receiver fails to achieve positioning across multiple consecutive frames is reduced by at least 50% compared to the case without interference mitigation. Full article

Introduction: The radiofrequency catheter ablation (RFCA) of ventricular arrhythmias (VAs) originating from the right ventricular outflow tract (RVOT) is a well-established therapy. Traditionally, RFCA is guided using electroanatomical 3D mapping systems involving manual catheter navigation within cardiac chambers. While effective, this approach may be time-consuming, and it carries a potential risk of cardiac wall perforation. Although the risk is low, it cannot be underestimated. Therefore, alternative mapping methods are sought to reduce procedural times and improve the overall efficiency of RVOT-VAs ablation. Aim: To evaluate the safety, feasibility, and efficacy of a universal RVOT 3D model implementation for the ablation of idiopathic RVOT-VAs. Methods: Consecutive patients undergoing VA ablation supported with a universal RVOT 3D model (3D-MODEL group) were included in the study. The RVOT universal model in this group was created by processing DICOM images for the improved segmentation of anatomical structures, followed by production using 3D printing technology. Patients who underwent classic endocardial electroanatomical mapping served as controls (EAM group). Results: A total of 228 patients were included in the study (143 women, age 50 ± 17 years): 149 in the 3D-MODEL group and 79 in the EAM group. The acute complete elimination of clinical VAs was achieved for 133 (89%) of patients in the 3D-MODEL group vs. 65 (82%) in the EAM group (p = 0.14). The procedural time was significantly shorter in the 3D-MODEL group compared to the EAM group (38 ± 14 min vs. 80 ± 39 min, p < 0.001). A significant difference was also observed in the radiofrequency time between the 3D-MODEL and EAM groups (251 ± 176 s vs. 503 ± 425 s, p < 0.001). No significant difference in fluoroscopy time was found between the groups (284 ± 167 s vs. 260 ± 327 s, p = 0.49). Two cases of cardiac tamponade occurred, both in patients from the EAM group. During follow-up, lasting 14 ± 10 months, 87% of patients in the 3D-MODEL group and 75% in the EAM group remained arrhythmia-free (p = 0.45). Conclusions: The use of universal RVOT 3D modeling is a feasible, safe, and effective alternative to classic electroanatomical mapping in the ablation of idiopathic RVOT-VAs. Full article

An airship is a lighter-than-air vehicle that offers static lift without consuming much power. This property makes it a potential candidate for many commercial applications. The target applications include rescue operations, surveillance, communication, a data collection platform for research activities and payload delivery that requires hovering capabilities, etc. To successfully apply airships in these applications and many others, airship autonomous control development is of paramount importance. To accomplish this goal, the initial step is to model airship dynamics that cover the complete flight envelope accurately. The goal is to develop a flight simulator that can test the advanced autonomous control algorithms. In the proposed work, first, the nonlinear six-degree-of-freedom equations of motion are developed using Newtonian mechanics. These equations are used to develop a flight simulator for the University of Engineering and Technology Taxila (UETT) airship. Airship responses to different control inputs are investigated, and the results are validated with the available data in the literature for other airship projects. Also, the obtained longitudinal and lateral eigenmodes show good agreement with the experimental flight data of the UETT airship. The extensive simulation results favour the dynamic analysis of the airship. Full article

Objective: This study investigated the association between paternal preconception paternal body mass index (BMI) categories and physical/neurodevelopmental outcomes in Chinese small-for-gestational-age (SGA) children. Methods: A prospective cohort study enrolled 412 singleton SGA infants born at Peking University People’s Hospital in 2020–2022. Fathers were stratified into underweight, normal-weight, overweight, and obese groups. Follow-up assessments at 24–36 months evaluated growth parameters weight, height, BMI Z-scores and neurodevelopment using the Ages and Stages Questionnaire-3 (ASQ-3) and ASQ: Social–Emotional (ASQ:SE). Multivariable regression was adjusted for paternal covariates. Results: In SGA offspring, paternal underweight correlated with lower birth weights vs. normal/obese paternal BMI and the highest severe SGA rates. Prospective monitoring identified elevated BMI Z-scores (ΔZ = +0.40) and 8.7-fold heightened obesity risk in the paternal obesity group versus normal-weight counterparts. Neurodevelopmental evaluations demonstrated gross motor impairments in both underweight (ΔZ = −0.22) and obese paternal subgroups (ΔZ = −0.25) compared with the normal-weight group, with the obesity cohort additionally exhibiting problem-solving deficiencies (ΔZ = −0.19). The paternal obesity group manifested three-fold greater likelihood of social–emotional delays than the normal-weight group. The underweight and obese paternal groups showed 3.46-fold and 2.73-fold higher probabilities of gross motor deficits, respectively, while obesity was linked to 3.27-fold elevated problem-solving impairment risk-all comparisons versus normal paternal BMI. Overweight status showed no significant links to growth or neurodevelopmental outcomes. Normal-weight fathers had lower risks of obesity and neurodevelopmental issues. Conclusions: This study revealed U-shaped paternal BMI–neurodevelopment links in SGA offspring. Paternal obesity raised offspring obesity/neurodevelopmental risks, while underweight linked to severe SGA and motor deficits, highlighting paternal weight optimization’s modifiable role. Full article

We analyze two different scenarios for the late universe dynamics, resulting in Hubble parameters deviating from the $\Lambda$CDM, mainly for the presence of an additional free parameter, which is the dark energy parameter. The first model consists of a pure evolutionary dark energy paradigm as a result of its creation by the gravitational field of the expanding universe. The second model also considers an interaction of the evolutionary dark energy with the matter component, postulated via the conservation of the sum of their ideal energy–momentum tensors. These two models are then compared via the diagnostic tool of the effective running Hubble constant, with the binned data of the so-called “Master sample” for the Type Ia Supernovae. The comparison procedures, based on a standard MCMC analysis, lead to a clear preference of data for the dark energy–matter interaction model, which is associated with a phantom matter equation of state parameter (very close to −1) when, being left free by data (it has a flat posterior), it is fixed in order to reproduce the decreasing power-law behavior of the effective running Hubble constant, already discussed in the literature. Full article

Against the backdrop of growing concerns over university students’ mental health worldwide, campus environments play a crucial role not only in shaping spatial experiences but also in influencing psychological well-being. However, the psychosocial mechanisms through which campus landscapes affect well-being remain insufficiently theorized. Drawing on survey data from 500 students across two Chinese universities, this study employs structural equation modeling (SEM) and interpretable machine learning techniques (XGBoost-SHAP) to systematically examine the interrelations among landscape perception, place attachment, perceived social acceptance, school belonging, and psychological well-being. The results reveal the following: (1) campus landscapes serve as the primary catalyst for fostering emotional identification (place attachment) and social connectedness (perceived social acceptance and school belonging), thereby indirectly influencing psychological well-being through these psychosocial pathways; (2) landscape perception emerges as the strongest predictor of well-being, followed by school belonging. Although behavioral variables such as the green space maintenance quality, visit frequency, and duration of stay contribute consistently, their predictive power remains comparatively limited; (3) significant nonlinear associations are observed between core variables and well-being. While the positive effects of landscape perception, place attachment, and school belonging exhibit diminishing returns beyond certain thresholds, high levels of perceived social acceptance continue to generate sustained improvements in well-being. This study advances environmental psychology by highlighting the central role of campus landscapes in promoting mental health and provides actionable strategies for campus planning. It advocates for the design of balanced, diverse, and socially engaging landscape environments to maximize psychological benefits. Full article

Corporate carbon emissions reporting is central to climate accountability, yet significant gaps remain in transparency, completeness, and methodological rigor. This study introduces the Carbon Integrity Index (CIX), a structured framework for assessing disclosure quality through ten indicators covering Scopes 1, 2, and 3. Unlike existing standards focused on reporting requirements, the CIX evaluates how well emissions are reported, addressing methodological transparency, scope coverage, and treatment of uncertainty. Applied to 2022 sustainability reports from companies listed in Spain’s IBEX 35 index, the framework reveals an average score of 5.7/10, with 69% of firms achieving passing results. While Scope 2 reporting was generally robust (mean: 0.82), Scope 3 disclosures—often representing the majority of emissions—and uncertainty assessments were systematically weak (mean: 0.08). Findings provide empirical support for legitimacy and institutional theory, showing how formal compliance can mask performative compliance that limits meaningful accountability. Sectoral differences suggest that institutional pressures and operational complexity shape divergent transparency pathways, raising concerns that universal standards may entrench reporting disparities. The CIX offers regulators, investors, and companies a practical tool for distinguishing symbolic from substantive disclosure, enabling more informed decision-making and strengthening the role of reporting in driving the transition to net-zero business models. Full article

Background/Objectives: Neurological disorders (ND) are a global health challenge, affecting millions and greatly reducing quality of life. Disorders such as Alzheimer’s disease, mild cognitive impairment (MCI), schizophrenia, and depression often share overlapping symptoms, complicating diagnosis and treatment. Early detection is crucial for timely intervention; however, traditional diagnostic methods rely on subjective assessments and costly imaging, which are not universally accessible. Addressing these challenges, this study investigates the classification of multiple ND using electroencephalography (EEG) signals. Methods: Various feature extraction methods were employed, and the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm was utilized for effective feature selection. Two-class (disease–disease and healthy control–disease), three-class (healthy control and two ND, as well as three ND), and four-class (healthy control and three ND) classifications were conducted using different machine learning algorithms with the selected features. An EEG dataset comprising 40 Alzheimer’s patients, 43 healthy controls, 42 schizophrenia patients, 28 MCI patients, and 28 depression patients served as the experimental benchmark. Results: The Linear Discriminant Analysis (LDA) classifier achieved the highest accuracy, distinguishing between healthy controls and Alzheimer’s with 100% accuracy and demonstrating strong performance in other comparisons. Multi-class classification reached 84.67% accuracy for distinguishing depression, MCI, and schizophrenia, while four-class classification achieved 57.89%, highlighting the complexity of differentiating among multiple ND. The frequent selection of frontal lobe channels across ND indicates their critical role in classification. Conclusions: This study contributes to the literature by emphasizing disease-to-disease classification over the traditional control-versus-patient framework, highlighting the potential for more effective diagnostic tools in clinical settings. Full article

Cryptorchidism is a common congenital disorder in male horses characterized by the failure of one or both testes to descend into the scrotum. This developmental anomaly has important clinical, surgical, and economic implications, particularly in breeding animals. This retrospective study investigates the prevalence and laterality of cryptorchidism in a large equine population presented to two veterinary referral hospitals—Colorado State University Veterinary Teaching Hospital (CSU VTH, 1984–2014) and Oakridge Equine Hospital (OEH, 2008–2023). Medical records were reviewed to identify affected horses, and data on breed, age, and laterality of retained testes were collected. Anatomical location of retention (inguinal vs. abdominal) was excluded due to inconsistent documentation across the study period. A total of 777 horses met the inclusion criteria, with Quarter Horses comprising the majority of clinical cases. Quarter Horses exhibited a strong predisposition for left-sided testicular retention, whereas Thoroughbreds and Arabians more commonly presented with right-sided retention. These breed-specific trends in laterality suggest possible developmental factors influencing testicular descent. Understanding these patterns can aid in clinical diagnosis, improve surgical planning, and contribute to evidence-based breeding recommendations aimed at reducing the incidence of cryptorchidism in equine populations. Full article

Fault diagnosis under variable operating conditions remains challenging due to the limited adaptability of traditional methods. This paper proposes a transfer learning-based approach for bearing fault diagnosis across different rotational speeds, addressing the critical need for reliable detection in changing industrial environments. The method trains a diagnostic model on labeled source-domain data and transfers them to unlabeled target domains through a two-stage adaptation strategy. First, only the source-domain data are labeled to reflect real-world scenarios where target-domain labels are unavailable. The model architecture combines a convolutional neural network (CNN) for feature extraction with a self-attention mechanism for classification. During source-domain training, the feature extractor parameters are frozen to focus on classifier optimization. When transferring to target domains, the classifier parameters are frozen instead, allowing the feature extractor to adapt to new speed conditions. Experimental validation on the Case Western Reserve University bearing dataset (CWRU), Jiangnan University bearing dataset (JNU), and Southeast University gear and bearing dataset (SEU) demonstrates the method’s effectiveness, achieving accuracies of 99.95%, 99.99%, and 100%, respectively. The proposed method achieves significant model size reduction compared to conventional TL approaches (e.g., DANN and CDAN), with reductions of up to 91.97% and 64%, respectively. Furthermore, we observed a maximum reduction of 61.86% in FLOPs consumption. The results show significant improvement over conventional approaches in maintaining diagnostic performance across varying operational conditions. This study provides a practical solution for industrial applications where equipment operates under non-stationary speeds, offering both computational efficiency and reliable fault detection capabilities. Full article

Background: Inborn errors of immunity (IEI) are mainly genetically driven disorders that affect immune function and present with highly heterogeneous clinical manifestations, ranging from severe combined immunodeficiency (SCID) to adult-onset immune dysregulatory diseases. This clinical heterogeneity, coupled with limited awareness and the absence of a universal diagnostic test, makes early and accurate diagnosis challenging. Although genetic testing methods such as whole-exome and genome sequencing have improved detection, they are often expensive, complex, and require functional validation. Recently, artificial intelligence (AI) tools have emerged as promising for enhancing diagnostic accuracy and clinical decision-making for IEI. Methods: We conducted a systematic review of four major databases (PubMed, Scopus, Web of Science, and Embase) to identify peer-reviewed English-published studies focusing on the application of AI techniques in the diagnosis and treatment of IEI across pediatric and adult populations. Twenty-three retrospective/prospective studies and clinical trials were included. Results: AI methodologies demonstrated high diagnostic accuracy, improved detection of pathogenic mutations, and enhanced prediction of clinical outcomes. AI tools effectively integrated and analyzed electronic health records (EHRs), clinical, immunological, and genetic data, thereby accelerating the diagnostic process and supporting personalized treatment strategies. Conclusions: AI technologies show significant promise in the early detection and management of IEI by reducing diagnostic delays and healthcare costs. While offering substantial benefits, limitations such as data bias and methodological inconsistencies among studies must be addressed to ensure broader clinical applicability. Full article