Search Results (848)

This study addresses the challenge of early-stage recognition of control chart patterns in statistical process control, which is critical for timely detection of process abnormalities in real-time manufacturing environments. Unlike most existing approaches that focus on fully developed patterns, this work targets partially developed patterns within a fixed observation window to enable proactive intervention. A multi-layer perceptron (MLP) classifier was developed using statistical features, and a structured design of experiments (DOE) approach was employed to optimize both the feature set and network parameters. Simulated X-bar chart data representing six pattern types were used, and candidate features were systematically evaluated using fractional factorial design. The results identified an effective feature subset consisting of autocorrelation, mean, mean square value, standard deviation, slope, and cumulative sum. The optimized MLP achieved an offline accuracy of approximately 86%, while online implementation yielded an overall accuracy of 70.6% with acceptable error rates and average run length performance (ARL₀ = 207.3, ARL_I = 10.9). The findings demonstrate that, despite greater difficulty in online recognition, the proposed approach provides a practical and interpretable solution for early detection in quality control systems. Full article

Background/Objectives: Adolescent obesity is increasingly recognized as a multidimensional condition involving psychosocial and behavioral vulnerabilities beyond metabolic risk. Although social withdrawal, dysregulated eating behaviors, and sleep disturbances have each been associated with obesity, integrative studies examining these domains concurrently remain limited. This study aimed to comparatively evaluate social withdrawal characteristics, eating behavior patterns, and daytime sleepiness in adolescents with obesity and normal-weight peers. Methods: This cross-sectional case–control study included 209 adolescents aged 14–18 years (100 with obesity; 109 normal-weight controls). Social withdrawal was assessed using the Hikikomori Risk Inventory (HRI-24), eating behaviors with the Children’s Three-Factor Eating Questionnaire (CTFEQ-17), and daytime sleepiness with the Pediatric Daytime Sleepiness Scale (PDSS). The BMI z-scores were calculated according to the CDC growth charts. Group comparisons and correlation analyses were performed. Results: Adolescents with obesity demonstrated significantly higher total social withdrawal scores and higher anthropophobia, agoraphobia, lethargy, and depressive mood subscale scores compared with the controls (all p < 0.01). Uncontrolled eating and emotional eating were also significantly higher in the obesity group (both p < 0.001), whereas cognitive restraint did not differ between groups (p > 0.05). Daytime sleepiness scores were higher in adolescents with obesity (p < 0.01). The BMI z-scores were positively correlated with social withdrawal dimensions and dysregulated eating behaviors (r = 0.15–0.30, p < 0.05) but not with daytime sleepiness. In contrast, daytime sleepiness was moderately associated with social withdrawal and uncontrolled/emotional eating (all p < 0.001). Conclusions: Adolescent obesity is associated not only with maladaptive eating behaviors but also with broader psychosocial vulnerabilities, including social withdrawal tendencies and sleep-related difficulties. These findings support a biopsychosocial conceptualization of adolescent obesity and underscore the importance of multidimensional intervention approaches targeting emotional regulation, sleep hygiene, and social functioning alongside weight management strategies. Full article

While global nutrient insufficiency remains a critical health challenge, eggs have emerged as a potential solution due to their profile as an accessible and nutrient-dense food source. To quantitatively assess this potential for mitigating nutrient insufficiencies and guide the production of nutrient-enriched eggs, the study proposes the concept of egg nutriomics, establishing a comprehensive evaluation system with 35 indicators across seven nutritional dimensions (fatty acids, amino acids, vitamins, trace elements, pigments, antioxidant capacity, and dietary restriction factors). Methodologically, the system normalizes raw analytical data into standardized scores (0–100) using indicator-specific functional models, with weights rationally allocated based on the essentiality of the nutrients. These quantitative metrics are subsequently translated into intuitive results using visualization tools such as heatmaps and radar charts. This study applied this system to evaluate six commercial egg varieties (pasteurized, lutein-enriched, ω-3 enriched, animal welfare, low-cholesterol, and conventional cage eggs), profiling multidimensional nutrition that allows for the intuitive visualization of performance scores across distinct dimensions. These profiles extend beyond comprehensive evaluation by revealing specific quantitative advantages—such as ω-3 enriched eggs scoring 79 in the fatty acid dimension compared to 49 for conventional eggs—thus providing a reference to guide precision modulation as illustrated by a dietary ω-3 enrichment case study involving 200 laying hens. Building upon this foundation, the strategy empowers a shift from the sole pursuit of high yields to precision nutritional modulation. This multi-dimensional strategy bridges nutritional analysis with production control, facilitating the development of nutrient-dense eggs as a potential application to mitigate human malnutrition. Full article

Objectives: Adolescent idiopathic scoliosis (AIS) may negatively affect both the quality of life of adolescents and the quality of life of their families (FQOL). Therefore, the analysis of objective and subjective determinants of FQOL in families of children and adolescents with AIS undergoing long-term conservative treatment becomes important and will allow for a better understanding of factors that may have a significant impact on the prognosis and clinical treatment outcomes. Methods: The analysis covered a total of 200 families of children and adolescents aged 7–18 from the Podkarpackie region (Poland). The medical history chart and the original physical examination card, as well as the Family Adaptability and Cohesion Scales (FACES III) and the Family Quality of Life Scale (FQOL), were used in this study. Results: Families of adolescents without AIS demonstrated significantly higher levels of family cohesion and adaptability compared with families of adolescents with AIS (p < 0.001). The mean overall FQOL score was significantly lower in the AIS group (75.33 ± 9.18) than in the control group (86.97 ± 7.91; p < 0.001, r_rb = 0.58). Multivariate analysis indicated that family adaptability was an independent predictor of FQOL in the AIS group, with higher adaptability associated with lower overall FQOL and reduced scores in parental functioning and emotional well-being domains. Conclusions: A long process of AIS treatment can cause crisis situations for patients and their families and influences both the physical and mental health of patients by changing their family’s quality of life (FQOL). Early identification of families characterized by diminished cohesion and adaptability enables the integration of psychopedagogical support and family consultations into standardized care. Such a multidimensional approach may enhance therapeutic prognosis and accelerate the rehabilitation process. Full article

Background: Studies on the clinical presentation of eosinophilic esophagitis and its outcome in children in the Middle East and North African region are scarce. The aim of this 10-year retrospective study was to describe the common clinical manifestations, endoscopic and histological findings, and the response to medication and dietary intervention in children and adolescents with eosinophilic esophagitis. Methods: This study was a retrospective chart review of patients aged 6 months to 18 years who attended the Pediatric Gastroenterology clinic at the American University of Beirut Medical Center between 1 January 2013 and 30 June 2023 and who were diagnosed with eosinophilic esophagitis. Results: A total of 15 patients met the inclusion criteria. The median age at diagnosis was 9 years. Male patients accounted for 73% of our cohort. The most frequent presenting symptoms were dysphagia (80%) and choking (47%). The esophagus appeared normal in 33% of subjects despite histologic confirmation of disease, highlighting the importance of routine biopsies. Adherence to therapy was variable, with 73% of subjects reporting symptom improvement following initial therapy, even in cases where histology remained active. This pattern suggests that symptomatic improvement alone may not reliably reflect disease control and underscores the importance of objective monitoring through follow-up biopsy. Conclusions: The recognition of manifestations of eosinophilic esophagitis in children, early diagnosis, and strict adherence to the diet and medication are essential to prevent long-term complications. In a resource-constrained country like Lebanon, the management remains challenging in view of the burden of dietary restrictions and high cost of procedures and biologics. Socioeconomic feasibility and long-term adherence to diet and medication is as critical as pharmacologic efficacy in determining outcomes in pediatric patients. Full article

Background and Objectives: Metacognition-related processes (e.g., confidence calibration, self-evaluation and the use of feedback) have been linked to cognitive insight, self-evaluation, and daily functioning in psychosis. However, clinic-based assessments only provide limited information. Digital methods may capture state-like variations and contextual factors, but it is unclear to what extent they operationalise core metacognitive monitoring constructs versus adjacent self-evaluative/insight-related constructs. We mapped digital approaches used to assess metacognition-related constructs across the psychosis spectrum, summarising the associated feasibility and validity. Materials and Methods: We conducted a scoping review (PRISMA-ScR) of psychosis-spectrum studies that used digital tools to assess metacognition-related targets. These included ecological momentary assessment/experience sampling (EMA/ESM), task-based paradigms with confidence ratings, and hybrid approaches. Searches covered MEDLINE (via PubMed), Scopus, and IEEE Xplore, with the final search run on 15 December 2025. We charted constructs, operationalisations, feasibility/engagement indices and reported links with clinical or functional measures. Results: The empirical evidence map comprised 13 studies directly assessing metacognition-related constructs; eight additional implementation/methodological sources were synthesised separately to contextualise feasibility, reporting, ethics, and governance. EMA studies more often assessed adjacent self-evaluative constructs, including context-linked self-appraisal bias, conviction, and self-report–context mismatch in daily life, whereas task-based studies more directly assessed confidence–accuracy calibration and feedback updating. Across EMA studies, greater momentary symptom severity and more restricted contexts were often associated with inflated self-evaluations and divergence from observer-rated functioning. Task-based studies indicated that confidence calibration and feedback utilisation may diverge from objective performance; in performance-controlled paradigms, some studies reported comparable metacognitive sensitivity/efficiency, but the overall evidence remains uncertain. Passive sensing was common in psychosis research but was rarely explicitly tied to metacognitive constructs. Conclusions: Current digital work spans both core metacognitive monitoring constructs and adjacent self-evaluative/insight-related constructs, rather than a single unitary construct. Clinical translation remains hypothesis-generating: interpretability may be improved by combining clinical anchors, low-burden EMA, and optional contextual streams, but thresholds, workflows, and signal-action rules require prospective validation. Full article

Every company conducts evaluations to ensure the quality of its products and services, often utilizing multivariate simultaneous control charts to monitor the process mean and variability concurrently. The objective of this study is to overcome a significant limitation in the Maximum Half-Normal Multivariate Control Chart (Max-Half-Mchart): its vulnerability to outliers, which can trigger masking and swamping effects and lead to inaccurate process monitoring. The primary scientific contribution is the development of two robust versions of the Max-Half-Mchart by integrating the fast minimum covariance determinant (Fast-MCD) and deterministic minimum covariance determinant (Det-MCD) estimators into the chart’s statistical framework. The evaluation criteria for these methods include the average run length (ARL) to assess process shift detection speed and classification accuracy, false positive (FP) rate, false negative (FN) rate, and area under the curve (AUC) to measure outlier detection performance. Simulation results indicate that, while both robust charts effectively detect process shifts, the Det-MCD-based robust Max-Half-Mchart is particularly superior for lower contamination levels (5–20%), whereas the Fast-MCD-based chart performs best at higher contamination levels (30%). An illustrative application to ordinary Portland cement (OPC) quality data confirms the practical superiority of the Det-MCD approach, which detected six out-of-control signals compared with only two identified by conventional methods. These results suggest that the proposed robust charts are highly sensitive tools for maintaining quality in the presence of contaminated data. Full article

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

Addressing the severe risk of artificial fractures causing vertical pressure channeling and subsequent water flooding during shale oil development in the Ordos Basin, this study investigates the overlapping development zone in Block Shun 269. Through laboratory rock mechanics experiments, the mechanical anisotropy of the overlapping layers was characterized. Utilizing actual production data, a 4D dynamic geomechanical model incorporating 21 years of injection-production history was established to reconstruct the pre-fracturing 3D in situ stress field. Based on this stress field model, a quantitative analysis was conducted on the evolution of injection-production stresses, the vertical superposition distance, the distribution of natural fractures, and the propagation patterns of hydraulic fractures across layers under various fracturing engineering parameters (including pumping rate, fluid viscosity, and perforation cluster, etc.). Research indicates that long-term injection-production disturbances caused the average minimum horizontal principal stress in the Chang 6 layer to decrease by 1.6 MPa, with partial “stress deficit zones” experiencing reductions as high as 3.5 MPa. This significantly weakened the stress shading capability between layers, resulting in the probability of fracturing cracks through the Chang 7 layer in the lower section increasing from 12% to 49%. The propagation of fracture height is jointly governed by geological and engineering factors, the weighting order is as follows: superposition distance > pumping rate > interlayer stress difference. A fracturing cross-layer risk assessment chart based on the coupling of geological and engineering factors has been established, proposing different anti-leakage and fracture control technical models for fracturing sections with different risk levels. Using this model to simulate fracturing in B horizontal wells, the simulation results were consistent with microseismic measurement data. Full article

One of the main tools in Statistical Process Control (SPC) for monitoring quality is the control chart. The Max-Half-Mchart is a Shewhart-type simultaneous multivariate control chart designed to detect shifts in both process mean and variability. However, outliers can distort the estimation of process parameters used to set control limits, leading to masking and swamping effects. Recent studies have highlighted the importance of cellwise contamination, which can reduce the effectiveness of casewise robust estimators. To overcome this limitation, this study develops a robust Max-Half-Mchart using the cellwise Minimum Covariance Determinant (cellMCD) estimator for location and covariance estimation. The proposed chart was evaluated through simulation studies, average run length analysis, and applications to synthetic and real OPC cement quality data. Simulation results under different correlation levels and contamination proportions show that the proposed chart provides more stable outlier detection performance than the conventional Max-Half-Mchart and the Fast-MCD-based Max-Half-Mchart, with better discrimination between normal and contaminated observations. The ARL analysis also indicates faster detection of small to moderate shifts. In the synthetic-data application, it achieved an Accuracy of 0.9899 and an AUC of 0.9939 under 20% contamination, and in the real-data application it detected seven out-of-control signals. Overall, the findings demonstrate that incorporating cellMCD into the Max-Half-Mchart provides a more robust and effective approach for multivariate process monitoring under cellwise contamination. Full article

Conventional cooling tower operations often rely on the operator’s experience for fan-switching control, lacking precise decision support and real-time monitoring capabilities. This makes it challenging to maintain water temperature within an optimal range, thereby affecting industrial process efficiency. Using a case study approach, we integrate a Long Short-Term Memory (LSTM) model for temperature prediction with a Reinforcement Learning (RL) model to develop a web-based decision support system for cooling tower operations. The system uses an LSTM model to predict the trend of return water temperature for the next 15 min. This prediction, along with environmental conditions and historical data, is then fed into the RL model. Through a reward mechanism, the model is designed to receive a higher score when the predicted temperature is close to the benchmark of 30.5 °C and a lower score otherwise, enabling it to learn the optimal fan control strategy. Based on the evaluation results, the system automatically determines the optimal action—turning the fan on, off, or maintaining its current state—and provides specific fan operation suggestions and a decision-making basis to the operator via a web interface. This system is designed with a layered architecture, comprising functional modules such as a real-time monitoring dashboard, historical data query, and AI model management. Through visual elements like temperature trend line charts, fan status indicators, and a decision suggestion interface, it provides operators with real-time water temperature status, predicted temperature trends, and specific operational recommendations. The system has been deployed and is running in an actual manufacturing factory, where the AI model generates predictions and decision outputs every 15 min, assisting operators in adjusting fan control. This has successfully stabilized the outlet water temperature within the target range of 30–31 °C, thereby enhancing the efficiency of cooling water temperature regulation. The model presents the practical application of AI technology in a manufacturing control scenario and establishes a web-based decision support system, providing a concrete example for smart manufacturing transformation within an Industrial IoT environment. Full article

Background/Objectives: Alcohol and tobacco use in adolescence are major public health concerns that shape long-term health trajectories and undermine healthy behaviour development. Schools are key settings for health promotion, offering structured environments to foster self-regulation, social skills, and protective behaviours. This scoping review mapped recent school-based educational strategies designed to prevent alcohol and tobacco use among adolescents and to examine whether the included studies reported any involvement of school nurses. Methods: Review followed Arksey and O’Malley’s framework and adhered to JBI guidance and PRISMA-ScR. Searches were conducted in PubMed and Web of Science (2019–2024) to identify school-based educational interventions targeting alcohol and/or tobacco use among primary or secondary school children. The primary search targeted prevention strategies, complemented by nursing-related terms to identify nurse involvement. A standardised charting form captured study characteristics, intervention formats, theoretical foundations, implementation factors, and any reported participation of health professionals. Data extraction was performed independently by two reviewers. Results: Eleven studies met the inclusion criteria. Most were randomised controlled trials (81.8%). Educational strategies included online (45.5%), hybrid (27.3%), and face-to-face (27.3%) formats. Programs focused on social skills, self-regulation, harm reduction, or resilience. Digital formats were cost-effective but showed challenges in engagement and sustained participation, while face-to-face or hybrid approaches offered relational support but were vulnerable to implementation drift. No study reported nurse involvement. Conclusions: School-based prevention strategies can contribute to healthier behaviours related to substance use by reinforcing socioemotional competencies and reducing early exposure to substances. However, persistent barriers such as low engagement, inconsistent delivery, and the absence of health professionals limit their impact. The role of school nurses could be considered in future school-based prevention programmes. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article

In the field of oil reservoir engineering, the development of large-dip-angle reservoirs poses significant challenges due to their strong heterogeneity, pronounced gravity effects, and inefficient water flooding sweep, all contributing to suboptimal oil recovery rates. This study aims to address these challenges by focusing on the core issue of optimizing water injection development strategies for such reservoirs. A numerical simulation mechanism model is constructed based on actual large-dip-angle reservoir A, and the impact of key parameters—including reservoir dip angle, permeability, injection–production well spacing, water injection intensity, and crude oil viscosity—on oil recovery is systematically analyzed under the “water injection at structural lows and oil production at structural highs” high-pressure water injection development mode. The simulation results reveal that the oil recovery rate increases with higher dip angles, permeability, injection–production well spacing, and water injection intensity; however, excessive water injection intensity or crude oil viscosity can lead to premature water breakthrough, reducing efficiency. Using the analytic hierarchy process, the primary controlling factors are ranked as permeability > crude oil viscosity > reservoir dip angle > water injection intensity > injection–production well spacing. Furthermore, development theory charts are established to guide the selection of appropriate water injection intensities for different injection–production well distances and permeabilities. This study offers valuable theoretical insights for optimizing water injection development in large-dip-angle reservoirs, thereby enhancing oil recovery and economic benefits and laying a foundation for future research and practical applications in similar reservoir settings. Full article