Search Results (52,877)

Topological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling. Rich point clouds acquired by multi-camera systems in Visual Simultaneous Localization and Mapping (V-SLAM) are typically processed using graph-based methods. In this work, we introduce a topological signal processing (TSP) framework that integrates texture information extracted from V-SLAM; we refer to this framework as TSP-SLAM. We show how TSP-SLAM enables the extension of graph-based point cloud processing to more advanced topological signal processing techniques. We demonstrate, on real stereo data, that TSP-SLAM enables a richer point cloud representation by associating signals not only with vertices but also with edges and faces of the mesh computed from the point cloud. Numerical results show that TSP-SLAM supports the design of topological filtering algorithms by exploiting the mapping between the 3D mesh faces, edges and vertices and their 2D image projections. These findings confirm the potential of TSP-SLAM for topological signal processing of point cloud data acquired in challenging V-SLAM environments. Full article

Endovascular repair of thoracoabdominal aortic aneurysms (TAAAs) requires multidisciplinary expertise to minimize mortality and disabling complications. Despite satisfactory outcomes with this approach being common knowledge, extensive aortic coverage occurring in fenestrated/branched endovascular aortic repair (F/B-EVAR) carries a non-negligible risk of spinal cord ischemia (SCI). Recently, many authors have proposed different endovascular strategies to mitigate the risk of SCI; however, the real effectiveness of these maneuvers is not universally recognized due to a lack of standardized protocols among individual centers. Several adjuncts have been proposed to obtain staged occlusion of segmental aortic branches to promote spinal cord preconditioning. These strategies include proximal thoracic aortic repair (PTAR), temporary aneurysm sac perfusion (TASP), and minimally invasive staged segmental artery coil embolization (MIS²ACE). The present paper aims to provide an overview of the most advanced staging strategies used in high-volume aortic centers, pointing out that it takes meticulous preoperative planning to face every clinical scenario. Full article

The demand for higher data rates and energy efficiency in wireless communication systems drives power amplifiers (PAs) into nonlinear operation, causing signal distortions that hinder performance. Digital Pre-Distortion (DPD) addresses these distortions, but existing systems face challenges with complexity, adaptability, and resource limitations. This paper introduces DRL-DPD, a Deep Reinforcement Learning-based solution for DPD that aims to reduce computational burden, improve adaptation to dynamic environments, and minimize resource consumption. To ensure safety and regulatory compliance, we integrate an ad-hoc Safe Reinforcement Learning algorithm, CRE-DDPG (Cautious-Recoverable-Exploration Deep Deterministic Policy Gradient), which prevents ACLR measurements from falling below safety thresholds. Simulations and hardware experiments demonstrate the potential of DRL-DPD with CRE-DDPG to surpass current DPD limitations in both local and remote configurations, paving the way for more efficient communication systems, especially in the context of 5G and beyond. Full article

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

Introduction: Tuberculosis (TB) remains a public health concern, and people at risk for TB are hesitant to seek care. The first South African National TB prevalence survey, conducted in 2017–2019, found that most participants with TB symptoms did not seek care for TB. In 2022, an estimated 23% of people with TB in South Africa were undiagnosed, contributing to the country’s burden of “missing” TB cases. This study explores health-seeking behaviour among people with TB (PwTB) in South Africa, focussing on barriers and facilitators to care-seeking and the quantification of TB-related stigma from a patient and community perspective. Methods: We conducted a mixed-method study in the City of Johannesburg (COJ) Metropolitan Municipality from February to March 2022. PwTB aged 18 and older initiating TB treatment for microbiologically confirmed pulmonary TB were recruited from three primary healthcare facilities in the COJ. After providing written informed consent, they participated in a one-time, in-depth, face-to-face interview. The interviews were digitally recorded and conducted by trained facilitators. We used thematic analysis with deductive approaches to develop themes. We used the Van Rie TB stigma assessment scale to quantify perceived stigma. Results: We interviewed 23 PwTB with an overall median age of 39 years and 14 (61%) males. Patient-level barriers to accessing TB care included visiting traditional healers and pharmacists before their TB diagnosis; wrong or missed diagnosis by private doctors; work commitments; scarcity of resources to attend the clinic or walk long distances; perceived and experienced stigma; and a lack of TB knowledge. Facility-level barriers included long clinic queues and uncertainty about where to receive TB care in the clinic. Facilitators for TB care-seeking included being in contact with someone who had TB, receiving encouragement from family, or having knowledge about TB transmission and early diagnosis. The overall median total stigma score among 21 PwTB was 53 (IQR: 46–63), with median community and patient stigma scores of 25 (IQR: 22–30) and 31 (IQR: 21–36), respectively. Conclusions: We found important considerations for the TB programme to improve the uptake of services. Since PwTB consult elsewhere before visiting a facility for TB care, TB programmes could establish private–public partnerships. TB programmes could also increase TB awareness in the community, especially among males, and mobile clinics could be considered to assist with TB case detection and treatment provision. Applying behavioural design techniques and co-designing interventions with patients and providers could improve TB health-seeking behaviours. Full article

To improve the dry-machining performance of a traditional-coated cemented carbide tool when cutting the laser-clad Cr-Ni-based steel, TiN/DLC multilayer coatings were fabricated using physical vapor deposition (PVD). The coated tools were tested for their surface and cross-sectional morphology, roughness, and microhardness. Dry-cutting experiments were conducted to compare the performance of a TiN monolayer-coated tool and a TiN/DLC multilayer-coated tool. The results indicated that the TiN/DLC multilayer coatings significantly improved the machining performance, lowered the cutting force and cutting temperature, decreased the average friction coefficient at the rake face, and reduced surface roughness compared to the TiN-coated tool. This improvement is mainly attributed to the low shear strength of the DLC layer, which effectively reduces surface friction and wear of the tool. The main failure modes were abrasive wear and adhesive wear. The results suggest that the composite coating offers a promising approach to improving traditional-coated tool life and enhancing machining efficiency in the dry cutting of laser-clad alloy components. Full article

Background: Intensive Care Unit (ICU) readmission and in-hospital mortality are critical indicators of patient outcomes following ICU discharge. Patients readmitted to the ICU often face worse prognosis, higher healthcare costs, and prolonged hospital stays. Identifying high-risk patients is essential for optimizing post-ICU care and resource allocation. Methods: This two-phase study included the following: (1) a retrospective analysis of ICU survivors in a mixed medical–surgical ICU to identify risk factors associated with ICU readmission and in-hospital mortality, and (2) a prospective validation of a newly developed predictive model: the Worse Outcome Score (WOScore). Data collected included demographics, ICU admission characteristics, severity scores (SAPS II, SAPS III, APACHE II, SOFA), interventions, complications and discharge parameters. Results: Among 1.190 ICU survivors, 126 (10.6%) were readmitted to the ICU, and 192 (16.1%) died in hospital after ICU discharge. Key risk factors for ICU readmission included Diabetes Mellitus, SAPS III on admission, and ICU-acquired infections (Ventilator-Associated Pneumonia (VAP) and Catheter-Related Bloodstream Infection, (CRBSI)). Predictors of in-hospital mortality were identified: medical admission, high SAPS III score, high lactate level on ICU admission, tracheostomy, reduced GCS at discharge, blood transfusion, CRBSI, and Acute Kidney Injury (AKI) during ICU stay. The WOScore, developed based on the results above, demonstrated strong predictive ability (AUC: 0.845 derivation, 0.886 validation). A cut-off of 20 distinguished high-risk patients (sensitivity: 88.1%, specificity: 73.0%). Conclusions: ICU readmission and in-hospital mortality are influenced by patient severity, underlying comorbidities, and ICU-related complications. The WOScore provides an effective, easy-to-use risk stratification tool that can guide clinicians in identifying high-risk patients at ICU discharge and guide post-ICU interventions, potentially improving patients’ outcomes and optimizing resource allocation. Further multi-center studies are necessary to validate the model in diverse healthcare settings. Full article

In the evaluation of vehicle noise, vibration and harshness (NVH) performance, interior noise control is the core consideration. In the early stage of automobile research and development, accurate prediction of interior noise caused by road surface is very important for optimizing NVH performance and shortening the development cycle. Although the data-driven machine learning method has been widely used in automobile noise research due to its advantages of no need for accurate physical modeling, data learning and generalization ability, it still faces the challenge of insufficient accuracy in capturing key local features, such as peaks, in practical NVH engineering. Aiming at this challenge, this paper introduces a forecast approach that utilizes an empirical-informed neural network, which aims to integrate a physical mechanism and a data-driven method. By deeply analyzing the transmission path of interior noise, this method embeds the acoustic mechanism features such as local peak and noise correlation into the deep neural network as physical constraints; therefore, this approach significantly enhances the model’s predictive performance. Experimental findings indicate that, in contrast to conventional deep learning techniques, this method is able to develop better generalization capabilities with limited samples, while still maintaining prediction accuracy. In the verification of specific models, this method shows obvious advantages in prediction accuracy and computational efficiency, which verifies its application value in practical engineering. The main contributions of this study are the proposal of an empirical-informed neural network that embeds vibro-acoustic mechanisms into the loss function and the introduction of an adaptive weight strategy to enhance model robustness. Full article

Armed conflicts continue to severely impact human populations and essential infrastructure, particularly water supply systems. This study examines the Yechilla area, a high-intensity battle corridor during the Tigray (between 12°15′26″ 14°57′49″ N latitude; and 36°20′57″–39°58′54″ E longitude) war (2020–2022). Using Cochran’s formula, a representative sample of 89 water schemes was selected for onsite assessment. Additional data on damages to water offices, personnel, equipment, and related infrastructure were gathered through face-to-face interviews with local officials and water professionals, onsite visits, and reviews of governmental and non-governmental archives, and previous studies. The findings reveal that 48.3% of water schemes in the study area are non-functional (does not deliver water), which is a significant increase from pre-war non-functionality rates of approximately 7.1% regionally and 21.1% nationally. Despite the Pretoria peace agreement, non-functionality levels remain critically high two years after conflict. Damage includes partial impairments, lack of technical and spare part support, complete destruction, and looting of water scheme components. The widespread destruction of civilian water infrastructure during the Tigray conflict underscores the insufficiency of existing international legal frameworks, such as the International Humanitarian Law and International Water Law, which are inadequately protecting civilians and their property. Understanding the broader consequences of armed conflicts requires examining the indirect effects and the complex interactions within and between social, economic, and environmental systems. These interconnected impacts are essential to fully grasp how conflict affects livelihoods and human security on a wider scale. Full article

Background: Children in underdeveloped rural areas of China often face socioeconomic disadvantages, which are associated with low confidence and self-esteem. While SEL programs have shown benefits internationally, evidence from Mainland China is limited. This study examined whether a school-based SEL intervention could improve confidence and self-esteem among children in economically disadvantaged rural areas. Methods: The intervention was a quasi-experimental study conducted in a rural, underdeveloped region of central China. It involved 16 weekly sessions, each lasting 90 min. A total of 230 children aged 8–12 years participated in the intervention school, while 325 children from another school served as the control group. The study used a mixed-methods design, including a quantitative survey administered at baseline, post-intervention, and a 5-month follow-up, as well as qualitative interviews with 83 children, nine caregivers, and eight teachers following the intervention. A linear mixed-effects model was employed to evaluate the effectiveness of the intervention, while interview data were analyzed using an inductive thematic approach. Results: The findings suggested (1) children from lower socioeconomic backgrounds consistently reported lower levels of self-esteem and self-efficacy across all three assessment points. (2) There was a short-term intervention effect on self-esteem, with greater improvements among children from poorer families. (3) The increase in children’s confidence meant they were more able to express themselves and mix with others. (4) Children’s improvements were not sustained up to a 5-month follow-up. Conclusions: The program may be effective in improving children’s confidence and self-esteem in underdeveloped rural areas of China. Such a program may contribute not only to educational outcomes but also to broader efforts aimed at social mobility and poverty reduction. Full article

Background/Objectives: Despite the increasing recognition of person-centered care (PCC), existing evaluation tools often have profession-specific limitations, lacking broad applicability across interdisciplinary contexts. This study aimed to develop and validate the Person-Centered Care Instrument (PCCI), designed to assess the competence of healthcare providers from diverse professions. Methods: Using a two-round modified Delphi technique, ten experts validated an initial pool of 63 items. The process assessed both face validity (overall appropriateness) and content validity using a 9-point Likert scale and the Item-level Content Validity Index (I-CVI). Items with a median rating of 6 or higher and an I-CVI of ≥0.70 were retained. Results: The final PCCI consists of 37 items, with a scale-level content validity index of 0.65. Three items achieved universal agreement among the experts (I-CVI = 1.0). For the final 37-item PCCI, the Scale-level Content Validity Index (S-CVI) was 0.65, and the index based on universal agreement was 0.22. Conclusions: The developed PCCI demonstrated good face and content validity, making it a valid and broadly applicable tool for assessing competence in delivering PCC. This instrument can support quality improvement initiatives and help promote a culture of empathy and respect in healthcare. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Modern societies are increasingly experiencing limited contact with nature, a phenomenon referred to as the “nature deficit.” The article presents a systematic review of the literature on this issue, with particular emphasis on the role of forests in mitigating its effects. The analysis, based on the Scopus and Web of Science databases, synthesizes the current state of knowledge on the consequences of nature deficit for physical, mental, and social health, while also highlighting the potential of forests as spaces supporting human well-being. The review process followed a systematic methodology, using precisely defined keyword combinations and multi-stage screening. From an initial pool of 88 publications, a critical selection process led to 11 articles that met the inclusion criteria and were analyzed in depth. The findings show that regular contact with nature reduces stress, anxiety, and ADHD symptoms, supports cognitive development, and im-proves concentration, creativity, and social skills. At the same time, there is a lack of consistent tools for clearly diagnosing nature deficit, and existing studies face significant methodological limitations (small samples, subjective measurements, lack of laboratory control). The article also identifies research gaps, particularly in the context of sustainable forest management, cultural differences, and the long-term health effects of exposure to nature. Full article

Electric submersible pumps (ESPs) are among the most widely used artificial lifting systems, and their operational stability is crucial to the production capacity and lifespan of oil wells. However, during the operation of ESP systems, they often face complex flow issues such as gas lock and insufficient liquid carry. Traditional control strategies relying on liquid level monitoring and electrical parameter alarms exhibit obvious latency, making it difficult to effectively guide the adjustments of key operating parameters such as pump frequency, valve opening, and on/off strategies. To monitor the flow state of ESP systems and optimize it in a timely manner, this paper proposes an innovative profile recognition method based on multiphase flow fitting in the wellbore, aimed at reconstructing the flow state at the pump’s intake. This method identifies flow abnormalities and, in conjunction with flow characteristics, designs targeted operating parameter optimization logic to enhance the stability and efficiency of ESP systems. Research shows that this optimization method can significantly improve the pump’s operational performance, reduce failure rates, and extend equipment lifespan, thus providing an effective solution for optimizing production in electric pump wells. Additionally, this method holds significant importance for enhancing oil well production efficiency and economic benefits, providing a scientific theoretical foundation and practical guidance for future oil and gas exploration and management. Full article

Background/Objectives: Leisure participation is vital for children’s development and family inclusion, yet families of children with developmental delays and disabilities face significant barriers. Guided by a health literacy framework, this study examined how personal and organizational health literacy shape access to inclusive leisure opportunities. Methods: Semi-structured interviews were conducted with 14 caregivers of young children (aged 2 to 6 years) with developmental delays and disabilities. A qualitative content analysis was applied to identify family and environmental factors shaping leisure participation. Results: Families with stronger personal health literacy engaged in diverse leisure activities, prioritizing children’s development through park visits and structured home routines. In contrast, weak organizational health literacy—reflected in limited inclusive facilities and support systems—restricted opportunities, increased caregiver stress, and forced adaptations such as traveling farther or rescheduling activities. These barriers underscored families’ vulnerability to exclusion while also highlighting their resilience in navigating daily life. Conclusions: This study demonstrates that health literacy functions at both personal and organizational levels to shape leisure participation. Beyond identifying barriers, it shows that leisure is intertwined with developmental needs and school readiness. By applying a health literacy lens, the study contributes to understanding family dynamics in inclusive leisure and underscores the need for responsive community services and inclusive policies. Full article