Search Results (743)

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

Background/Objectives: End-of-life care is a vital part of nursing education that has been overlooked until recent years. Advance care planning should be incorporated into the prelicensure nursing curriculum to build student nurses’ confidence in aiding patients and families with their preferred future care plans. Advance care planning tools, such as the Advance Health Care Directive (AHCD) and Five Wishes, provide experiential learning opportunities that bridge theoretical knowledge with real-world patient advocacy. In this study, students were asked to complete either the AHCD or Five Wishes document as though planning for their own end-of-life care, encouraging personal reflection and professional insight. Embedding these assignments into nursing education strengthens students’ confidence in facilitating end-of-life discussions. This study applied Kolb’s experiential learning theory, including concrete experience, reflective observation, abstract conceptualization, and active experimentation, to explore student nurses’ perspectives on the Advance Health Care Directive and Five Wishes assignments, as well as their understanding of end-of-life care. Methods: This study used an exploratory–descriptive qualitative design featuring one open-ended question to collect students’ views on the assignments. Results: The final sample comprised 67 prelicensure student nurses from Bachelor of Science and Entry-Level Master’s programs. The Advance Health Care Directive and/or Five Wishes assignment enhanced students’ understanding of end-of-life decision-making. Conclusions: It is essential to complete the assignment and immerse oneself in an end-of-life situation to grasp patients’ perspectives and concerns regarding when to engage in difficult conversations with their patients. Full article

Sexual consent is one of the most important tools used in the prevention of sexual violence, for which adolescents are especially vulnerable. However, it is unclear how sexual consent processes are defined and used by this population. To bridge this gap in knowledge, this scoping review sought to identify and synthesize the existing empirical research findings on sexual consent conceptualizations and processes among adolescents, as well as determine critical gaps in knowledge. Forty-three articles were reviewed following a systematic search of six academic databases. Articles were included if they were original empirical work published in English between January 1990 and March 2020, included adolescents aged 10 to 17 in their sample, and specifically studied sexual consent conceptualization, communication, and/or behavior. Seventeen articles, diverse in study design and geography, met these criteria and were analyzed. The findings suggest a propensity for adolescents to abstractly define sexual consent as verbal and direct in nature while simultaneously espousing indirect and non-verbal behavioral processes when presented with “real life” scenarios (e.g., vignettes, reflections on personal experience). In addition, the results reveal the significance of concepts like gender norms, normative refusals, and silence as key aspects of adolescent sexual consent. This review demonstrates that research on sexual consent among adolescents is highly limited overall, and the findings that are available indicate some concerning perceptions. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

Extended reality (XR)-based rehabilitation is an emerging therapeutic approach that combines real and virtual environments to enhance patient engagement and promote motor and cognitive recovery. Its clinical utility in children with cerebral palsy (CP), particularly regarding gross motor skills, balance, and psychosocial well-being, remains underexplored. This preliminary study aimed to evaluate the potential effects of XR-based rehabilitation on gross motor function, balance, parental stress, and quality of life in children with cerebral palsy. Thirty children with cerebral palsy were randomly assigned to an extended reality training group (XRT, n = 15) or a conventional physical therapy group (CPT, n = 15). Both groups received 30 min sessions, three times per week for 6 weeks. Outcome measures included the Gross Motor Function Measure-88 (GMFM-88), Pediatric Balance Scale (PBS), Functional Independence Measure (FIM), Parenting Stress Index (PSI), and Pediatric Quality of Life Inventory (PedsQL), assessed pre- and post-intervention. A 2 (group) × 2 (time) mixed ANOVA was conducted. The XR group demonstrated improvements in GMFM-88, PBS, and FIM scores, with decreased PSI and increased PedsQL scores. Although most interaction effects were not statistically significant (GMFM-88: η² = 0.035, p = 0.329; PBS: η² = 0.043, p = 0.274), a marginal interaction effect was observed for PSI (p = 0.065, η² = 0.059), suggesting a potential benefit of XR-based rehabilitation in reducing parental stress. This preliminary study indicates that XR-based rehabilitation may provide beneficial trends in motor function and psychosocial health in children with CP, particularly in reducing parental stress. Further studies with larger sample sizes are needed to confirm these findings. Full article

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

Background: Asparaginase (ASPase) plays an important role in the therapy of acute lymphoblastic leukemia (ALL). Serum ASPase activity (SAA) can be modified and even abolished by host immune responses; therefore, current treatment guidelines recommend to monitor SAA during treatment administration. The SAA monitoring schedule needs to be carefully planned to reduce the number of samples without hampering the possibility of measuring pharmacokinetics (PK) parameters in individual patients. Complex modelling approaches, not easily applicable in common practice, have been applied in previous studies to estimate ASPase PK parameters. This study aimed to estimate PK parameters by using a simplified approach suitable for real-world settings with limited sampling. Methods: Our study was based on 434 patients treated in Italy within the AIEOP-BFM ALL 2009 trial. During the induction phase, patients received two doses of pegylated ASPase and were monitored with blood sampling at five time points, including time 0. PK parameters were estimated by using the individually available SAA measurements with simple modifications of the classical non-compartmental PK analysis. We also took the opportunity to develop and validate a series of limited sampling models to predict ASPase exposure. Results: During the induction phase, average ASPase activity at day 7 was 1380 IU/L after the first dose and 1948 IU/L after the second dose; therapeutic SAA levels (>100 IU/L) were maintained until day 33 in 90.1% of patients. The average AUC and clearance were 46,937 IU/L × day and 0.114 L/day/m², respectively. The database was analyzed for possible associations of PK parameters with biological characteristics of the patients, finding only a limited dependence on sex, age and risk score; however, these differences were not sufficient to allow any dose or schedule adjustments. Thereafter the possibility of further sampling reduction by using simple linear models to estimate the AUC was also explored. The most simple model required only two samplings 7 days after each ASPase dose, with the AUC being proportional to the sum of the two measured activities A(7) and A(21), calculated by the formula AUC = 14.1 × [A(7) + A(21)]. This model predicts the AUC with 6% average error and 35% maximum error compared to the AUC estimated with all available measures. Conclusions: Our study demonstrates the feasibility of a direct estimation of PK parameters in a real-life situation with limited and variable blood sampling schedules and also offers a simplified method and formulae easily applicable in clinical practice while maintaining a reliable pharmacokinetic monitoring. Full article

Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

The engineering of proteins, nucleic acids, and nanomaterials has significantly advanced the development of biosensors for the monitoring of rare diseases. These innovative biosensing technologies facilitate the early detection and management of conditions that often lack adequate diagnostic solutions. By utilizing engineered proteins and functional nucleic acids, such as aptamers and nucleic acid sensors, these biosensors can achieve high specificity in identifying the biomarkers associated with rare diseases. The incorporation of nanomaterials, like nanoparticles and nanosensors, enhances sensitivity and allows for the real-time monitoring of biochemical changes, which is critical for timely intervention. Moreover, integrating these technologies into wearable devices provides patients and healthcare providers with continuous monitoring capabilities, transforming the landscape of healthcare for rare diseases. The ability to detect low-abundance biomarkers in varied sample types, such as blood or saliva, can lead to breakthroughs in understanding disease pathways and personalizing treatment strategies. As the field continues to evolve, the combination of protein, nucleic acid, and nanomaterial engineering will play a crucial role in developing next-generation biosensors that are not only cost-effective but also easy to use, ultimately improving outcomes and the quality of life for individuals affected by rare diseases. Full article

Chronic total occlusions (CTOs), defined as complete coronary artery blockages persisting for over three months, are frequently encountered in up to 25% of coronary angiograms. Although percutaneous coronary intervention (PCI) for CTO remains technically challenging, advancements in guidewires, microcatheters, re-entry devices, and intravascular imaging, along with the expertise of specialized operators, have significantly improved procedural success rates, now exceeding 90% in expert centers. While recent evidence, such as the SYNTAX II study, emphasizes the importance of complete revascularization, over half of CTO cases continue to be managed conservatively with optimal medical therapy (OMT), partly due to the limited high-quality randomized evidence supporting revascularization. Observational studies have demonstrated that successful CTO-PCI is associated with improved angina relief, quality of life, left ventricular function, and possibly long-term survival. Extended observational follow-up, such as the Korean and Canadian registries, suggests long-term reductions in cardiac and all-cause mortality with CTO revascularization. However, randomized controlled trials (RCTs) have primarily shown symptomatic benefit, with no consistent reduction in major adverse cardiac events (MACE) or mortality, likely due to limited sample sizes, short follow-up, and treatment crossovers. Various strategies, including the hybrid algorithm, guide CTO interventions by balancing antegrade and retrograde techniques based on lesion complexity. Imaging modalities such as coronary CT angiography and intravascular ultrasound play a pivotal role in planning and optimizing these procedures. Future innovations, such as real-time fusion imaging of CCTA with coronary angiography, may enhance lesion visualization and guidewire navigation. While current guidelines recommend CTO-PCI in selected symptomatic patients with demonstrable ischemia or viable myocardium, the decision should be individualized, incorporating anatomical feasibility, comorbidities, patient preferences, and input from a multidisciplinary Heart Team. Looking ahead, adequately powered RCTs with extended follow-up are essential to determine the long-term clinical impact of CTO-PCI on hard outcomes such as mortality and myocardial infarction. Full article

To develop interventions and policies to promote healthy and sustainable diets during times of crisis, it is important to understand how populations respond to such situations based on real-life examples. Using the recent COVID-19 pandemic as a case study to identify risk and protective factors for such situations, we implemented the first longitudinal mixed-methods approach of this topic to date. Data were collected from a sample of UK parents (stratified for gender and socioeconomic status) through two surveys and a set of focus groups during the pandemic. The focus groups identified multifaceted drivers of change linked to capability (e.g., shielding), opportunity (e.g., time, food access and availability), and motivation (e.g., reflective motivation, stress and mental health challenges) barriers. High levels of COVID-19 stress were linked with less healthy and sustainable diets in the survey results, while higher social cohesion, reflective motivation to consume healthy foods, and positive coping scores were protective, and linked to healthier and more sustainable diets. A range of coping strategies were identified, including home cooking and meal planning, which could provide a basis for future intervention development to reduce stress, maintain wellbeing, and improve dietary outcomes in future crisis situations. Full article

Crohn’s disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract that severely impacts patients’ quality of life. Although current therapies have improved symptom management, they often fail to alter disease progression and are associated with immunosuppressive side effects. This study evaluated the immunomodulatory potential of resolvin D2 (RvD2), a pro-resolving lipid mediator, using a murine model of colitis and the ex vivo treatment of intestinal mucosal biopsies from CD patients, comparing its effects to those of conventional anti-TNFα therapy. To determine the optimal concentration of RvD2 for application in human tissue explant cultures, an initial in vitro assay was conducted using intestinal biopsies from mice with experimentally induced colitis. The explants were treated in vitro with varying concentrations of RvD2, and 0.1 μM emerged as an effective dose. This concentration significantly reduced the transcriptional levels of TNF-α (p = 0.004) and IL-6 (p = 0.026). Intestinal mucosal biopsies from fifteen patients with CD and seven control individuals were analyzed to validate RNA-sequencing data, which revealed dysregulation in the RvD2 biosynthetic and signaling pathways. The real-time PCR confirmed an increased expression of PLA2G7 (p = 0.02) and ALOX15 (p = 0.02), while the immunohistochemical analysis demonstrated the reduced expression of the RvD2 receptor GPR18 (p = 0.04) in intestinal tissues from CD patients. Subsequently, samples from eight patients with active Crohn’s disease, eight patients in remission, and six healthy controls were used for the serum analysis of RvD2 by ELISA, in vitro treatment of intestinal biopsies with RvD2 or anti-TNF, followed by transcriptional analysis, and a multiplex assay of the explant culture supernatants. The serum analysis demonstrated elevated RvD2 levels in CD patients both with active disease (p = 0.02) and in remission (p = 0.002) compared to healthy controls. The ex vivo treatment of intestinal biopsies with RvD2 decreased IL1β (p = 0.04) and TNFα (p = 0.02) transcriptional levels, comparable to anti-TNFα therapy. Additionally, multiplex cytokine profiling confirmed a reduction in pro-inflammatory cytokines, including IL-6 (p = 0.01), IL-21 (p = 0.04), and IL-22 (p = 0.009), in the supernatant of samples treated with RvD2. Altogether, these findings suggest that RvD2 promotes the resolution of inflammation in CD and supports its potential as a promising therapeutic strategy. Full article

The life expectancy of patients with psychotic disorders is significantly shorter than that of the general population; antipsychotic-induced metabolic disorders play a significant role in reducing life expectancy. Both metabolic syndrome (MetS) and schizophrenia are multifactorial conditions. One area where the two conditions overlap is oxidative stress, which is present in both diseases. The glutathione-S-transferase (GST) system is a major line of defense against exogenous toxicants and oxidative damage to cells. The aim of our study was to perform an association analysis of gene polymorphisms with metabolic disorders in patients with schizophrenia treated with antipsychotic therapy. Methods: A total of 639 white patients with schizophrenia (ICD-10) from Siberia (Russia) were included in the study. Genotyping was carried out using real-time polymerase chain reaction for two single-nucleotide polymorphisms (SNPs) in the GSTP1 (rs614080 and rs1695) and one SNP in the GSTO1 (rs49252). Results: We found that rs1695*GG genotype of GSTP1 is a risk factor for the development of overweight (OR 2.36; 95% CI: 1.3–4.29; p = 0.0054). In the subgroup of patients receiving first-generation antipsychotics as basic therapy, the risk of overweight was associated with carriage of the rs1695*GG (OR 5.43; 95% CI: 2.24–13.16; p < 0.001) genotype of GSTP1 in a recessive model of inheritance. In contrast, an association of rs1695*G GSTP1 with obesity (OR: 0.42; 95% CI: 0.20–0.87; p = 0.018) was shown in the dominant model of inheritance in patients receiving second-generation antipsychotics. Conclusions: The pilot results obtained confirm the hypothesis of a violation of the antioxidant status, in particular the involvement of GSTP1, in the development of antipsychotic-induced metabolic disorders in schizophrenia. Further studies with larger samples and different ethnic groups are needed to confirm the obtained results. Full article

The mechanical reliability of metallic alloys under cyclic loading is crucial for optimizing their microstructure–property relationships. Understanding the statistical behavior of fatigue failure data is essential for designing alloys that endure extreme environmental conditions. This study introduces a generalization of the Student’s t-Birnbaum–Saunders distribution to improve the modeling of fatigue life data, which often exhibit heavy tails and are common in advanced alloy systems. Seven different estimation methods are employed to estimate and compare the parameters of the proposed distribution, providing a comprehensive statistical framework for fatigue failure analysis. The goodness-of-fit of the proposed model and its sub-models, along with the joint relative efficiency of parameter estimates, is assessed using real fatigue data within the maximum likelihood framework. Additionally, the robustness of estimation methods is examined through Monte Carlo simulations across various sample sizes and parameter configurations. The results highlight the effectiveness of the generalized Student’s t-Birnbaum–Saunders distribution in capturing the stochastic nature of fatigue failure in metallic alloys, offering valuable insights for materials design and predictive reliability modeling. These findings align with advancements in computational modeling and simulation, contributing to developing alloys with tailored mechanical properties. Full article