Search Results (1,789)

Introduction: Breakfast skipping has been proposed as a determinant of childhood obesity and disorders of glucose metabolism. The present study aimed to examine the association between breakfast skipping and insulin resistance in children and adolescents with overweight or obesity. Methods: In total, 1291 children aged 2–18 years old (boys = 48.4%, boys with obesity = 69.8%; girls = 51.6%, girls with obesity = 60.8%) participated in the study, providing sociodemographic, anthropometric, lifestyle, biochemical, and clinical data. Breakfast consumption was assessed using a validated questionnaire. The IOTF criteria were used to categorize children’s body mass index (BMI) status, while the homeostasis model assessment (HOMA-IR) was used to assess insulin resistance. Results: According to the findings of this study, 37.3% of the children/adolescents were found to skip daily breakfast consumption. Girls tended to skip breakfast more than boys (40.5% vs. 33.9%, p = 0.016), with the percentage of girls skipping breakfast increasing in the older age groups (2–5 yrs: 27% vs. 6–12 yrs: 39.1% vs. 13–18 yrs: 53.5%, p = 0.001). Linear regression analyses showed that breakfast skipping is associated with HOMA-IR in the total sample and in children and adolescents with obesity, after adjusting for several confounding factors (age, gender, Tanner stage, residency, and sports participation). Conclusions: A large number of children and adolescents with overweight or obesity, especially adolescent girls, skip daily breakfast consumption, which was associated with insulin resistance. These findings underscore the importance of promoting regular breakfast consumption as a preventive strategy against metabolic complications in children and adolescents with overweight or obesity. Full article

Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture. Full article

In the context of cybersecurity digital twin environments, the ability to simulate realistic network traffic is critical for validating and training intrusion detection systems. However, generating synthetic data that accurately reflects the complex, time-dependent nature of network flows remains a significant challenge. This paper presents an AI-based data generation approach designed to generate multivariate temporal network flow data that accurately reflects adversarial scenarios. The proposed method integrates a Long Short-Term Memory (LSTM) architecture trained to capture the temporal dynamics of both normal and attack traffic, ensuring the synthetic data preserves realistic, sequence-aware behavioral patterns. To further enhance data fidelity, a combination of deep learning-based generative models and statistical techniques is employed to synthesize both numerical and categorical features while maintaining the correct proportions and temporal relationships between attack and normal traffic. A key contribution of the framework is its ability to generate high-fidelity synthetic data that supports the simulation of realistic, production-like cybersecurity scenarios. Experimental results demonstrate the effectiveness of the approach in generating data that supports robust machine learning-based detection systems, making it a valuable tool for cybersecurity validation and training in digital twin environments. Full article

Background/Objectives: Failure to rescue (FTR), defined as the occurrence of a major complication plus death within 30 days, is a key measure of surgical safety. Hypoalbuminemia is a known risk factor for poor outcome in metastatic spinal tumor surgery, yet its association with FTR has not been explored. The purpose of this study is to evaluate serum albumin level as predictor of FTR after surgery for spinal metastases. Methods: A total of 1749 patients with disseminated cancer who underwent oncologic surgery for spinal metastases (identified by CPT codes) and met our inclusion criteria were identified in the ACS-NSQIP database (2018–2023). The primary endpoint was FTR, defined as a major complication plus death occurring within 30 days of surgery. Serum albumin was analyzed both as a continuous and categorical variable (hypoalbuminemia < 3.5 g/dL, normal albumin > 3.5 g/dL). Univariable and multivariable logistic regression was performed, adjusting for demographic and operative variables. Results: The mean preoperative serum albumin level was 3.63 g/dL (standard deviation = 0.642) and the FTR rate was 4% (71 of 1749). After adjusting for potential confounders such as modified Frailty Index 5, ASA class, functional status, emergent case, and reoperation, higher preoperative albumin levels (OR 0.39 [95% CI 0.26–0.61]; p < 0.001) were independently associated with decreased odds of FTR. Conclusions: The findings of this study suggest an association between preoperative serum albumin level and FTR in oncologic surgery for spinal metastases. This highlights the importance of albumin assessment for perioperative prognosis, but the findings require further validation. Full article

Background: Solute carrier organic anion transporter family member 1B1 (SLCO1B1) mediates statin uptake into hepatocytes, the primary sites of cholesterol production. While the impact of SLCO1B1 variation on statin-associated muscle symptoms (SAMS) is well-documented, its role in LDL-C reduction remains understudied. This single-center, retrospective cohort study evaluated whether SLCO1B1 variation affects statin effectiveness in 213 adults. Methods: The SLCO1B1 variant rs4149056 (NM_006446.5:c.521T>C) was tested to categorize patients by their SLCO1B1 function: normal, decreased, or poor. The primary endpoint was percent change in LDL-C from baseline to follow-up (≥6 weeks post-statin initiation), with secondary endpoints of SAMS occurrence and statin adherence. Results: Overall, the average LDL-C decreased by 32% across all groups. No significant difference in LDL-C reduction was observed between SLCO1B1 phenotypes (p = 0.24). Conclusions: SLCO1B1 variation did not significantly affect LDL-C reduction, SAMS occurrence, or statin adherence. However, the retrospective design and limited adherence data in this study represent important limitations warranting prospective validation studies. Full article

Software-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynamically apply and enforce security and traffic policies. The shift in architecture offers numerous advantages such as increased flexibility, scalability, and improved network management but also introduces new and notable security challenges such as Distributed Denial-of-Service (DDoS) attacks. Such attacks focus on affecting the target with malicious traffic and even short-lived DDoS incidents can drastically impact the entire network’s stability, performance and availability. This comprehensive review paper provides a detailed investigation of SDN principles, the nature of DDoS threats in such environments and the strategies used to detect/mitigate these attacks. It provides novelty by offering an in-depth categorization of state-of-the-art detection techniques, utilizing machine learning, deep learning, and federated learning in domain-specific and general-purpose SDN scenarios. Each method is analyzed for its effectiveness. The paper further evaluates the strengths and weaknesses of these techniques, highlighting their applicability in different SDN contexts. In addition, the paper outlines the key performance metrics used in evaluating these detection mechanisms. Moreover, the novelty of the study is classifying the datasets commonly used for training and validating DDoS detection models into two major categories: legacy-compatible datasets that are adapted from traditional network environments, and SDN-contextual datasets that are specifically generated to reflect the characteristics of modern SDN systems. Finally, the paper suggests a few directions for future research. These include enhancing the robustness of detection models, integrating privacy-preserving techniques in collaborative learning, and developing more comprehensive and realistic SDN-specific datasets to improve the strength of SDN infrastructures against DDoS threats. Full article

In high-density commercial farms, the timely detection and removal of dead bodies are essential to maintain the well-being of animals and ensure farm productivity. This review systematically synthesizes 128 published studies, 52 of which are highly related to the death detecting topic, covering diverse animal species and farming scenarios. The review systematically synthesizes existing research on death detection methods, dead body removal systems, and their integration. The death detection process is divided into three key stages: data acquisition, dataset establishment, and data processing. Inspection systems are categorized into fixed and mobile inspection systems, enabling autonomous imaging for death detection. Regarding death removal systems, current research predominantly focuses on hardware design for poultry and aquaculture, but real-farm validation remains limited. Key focuses for future development include enhancing the robustness and adaptability of detection models with high-quality datasets, brainstorming for more feasible designs of removal systems to enhance adaptability to diverse farm conditions, and improving the integration of inspection systems with removal systems to conduct fully automated detection-removal operations. Ultimately, the successful application of these technologies will reduce labor dependence, enhance biosecurity, and support sustainable, high-density large-scale animal farming while ensuring both satisfying production and the welfare of animals. Full article

This study presents an advanced signal analysis model for internal defect identification in bridge decks using impact echo field testing data designed to mitigate signal noise and the variability encountered during real-world inspections. Field tests were conducted on a concrete bridge deck utilizing an automated inspection system, systematically capturing impact-echo signals across multiple scanning paths. The large volume of field-acquired data poses significant challenges, particularly in identifying defects and isolating clean signals and suppressing noise under variable environmental conditions. To enhance the accuracy of defect detection, a deep learning framework was designed to refine critical signal parameters, such as signal duration and the starting point in relation to the zero-crossing. A convolutional neural network (CNN)-based classification model was developed to categorize signals into delamination, non-delamination, and insignificant classes. Through systematic parameter tuning, optimal values of 1 ms signal duration and 0.1 ms starting time were identified, resulting in a classification accuracy of 88.8%. Laboratory test results were used to validate the signal behavior trends observed during the parameter optimization process. Comparison of defect maps generated before and after applying the CNN-optimized signal parameters revealed significant enhancements in detection accuracy. The findings highlight the effectiveness of integrating advanced signal analysis and deep learning techniques with impact-echo testing, offering a robust non-destructive evaluation approach for large-scaled infrastructures such as bridge deck condition assessment. Full article

Background: Oral health is increasingly recognized as a determinant of overall well-being, but its role in mental health remains underexplored. Chewing difficulty and tooth loss can impair nutrition, social interaction, and quality of life, thereby contributing to psychological distress. Objectives: This study examined the association between oral health indicators and anxiety among Korean adults. Methods: Data were obtained from 4746 adults aged ≥19 years who participated in the 2023 Korea National Health and Nutrition Examination Survey (KNHANES). Anxiety was assessed using the Generalized Anxiety Disorder-7 (GAD-7), a validated 7-item self-report questionnaire with responses on a 4-point Likert scale (0 = not at all to 3 = nearly every day). Anxiety severity was categorized into four levels. Severity was categorized into four levels using the GAD-7. Oral health predictors included the number of remaining teeth and self-reported chewing difficulty, along with toothache experience, toothbrushing frequency, and unmet dental care needs. Complex survey-weighted ordinal logistic regression models were applied, adjusting for sociodemographic, behavioral, and clinical covariates. Results: Overall, 15.3% of adults reported mild, 3.1% moderate, and 1.6% severe anxiety. Chewing difficulty, fewer than 20 remaining teeth, overweight status, high stress, depressive symptoms, and unmet dental care needs were significantly associated with greater anxiety severity. Conclusions: The number of remaining teeth retention and chewing function were closely related to anxiety. Preserving functional dentition and ensuring timely access to dental care may be effective public health measures to reduce the psychological burden in the general population. Full article

Low-permeability reservoirs play a crucial role in global energy supply, yet their efficient development is hindered by complex seepage mechanisms and strong nonlinear flow behavior. This study systematically investigates the characteristics of oil–water relative permeability curves and the associated non-Darcy flow phenomena in low-permeability sandstone reservoirs. Through unsteady-state water flooding experiments on native cores with permeabilities ranging from 2.99 to 34.40 mD, we analyzed the influence of permeability on relative permeability curves and categorized the water-phase curves into concave-downward and linear types. A dynamic quasi-threshold pressure gradient model was established, incorporating the corrected permeability and water saturation. Furthermore, a novel relative permeability calculation model was derived by integrating the threshold pressure gradient into the non-Darcy flow framework. Validation against the traditional Johnson–Bossler–Naumann (JBN) method demonstrated that the proposed model more accurately captures the flow behavior in low-permeability media, showing lower oil-phase permeability and higher water-phase permeability. The findings provide a reliable theoretical basis for optimizing water flooding strategies and enhancing recovery in low-permeability reservoirs. Full article

Background/Objectives: Systemic sclerosis (SSc) is a heterogeneous connective tissue disease characterized by immune dysregulation, vasculopathy, and fibrosis. Objectives: To evaluate the genetic architecture and autoantibody profile in a Kazakh cohort of patients with SSc. Methods: A total of 26 Kazakh patients with diffuse SSc were examined for disease activity and organ impairment using EScSG and the modified Rodnan skin score (mRSS). Eighteen healthy volunteers were enrolled in the control group. Antinuclear factor (ANF) was estimated on HEp-2 cells, while antibodies to Scl-70, CENP-B, U1-snRNP, SS-A/Ro52, SS-A/Ro60, Sm/RNP, Sm, SS-B, Rib-P0, and nucleosomes were determined by immunoblotting. The level of IL-6 cytokine was detected using ELISA. To investigate the genetic basis of SSc in Kazakh patients, a custom AmpliSeq panel including targeting immune/fibrosis pathways and 120 genes was used on the Ion Proton sequencer. The statistical analysis of categorical variables was conducted using Fisher’s exact test and Chi-square (χ²) test. Results: The examination of SSc patients (mRSS 16 ± 7.2; EScSG 3.54 ± 2.18) revealed a broad range of antibodies to Scl-70, CENP-B, SS-A/Ro60, SS-A/Ro52, U1-snRNP, and RNP/Sm, which were undetectable in the control group. Genetic analysis identified multiple variants across immune regulatory genes, including likely pathogenic changes in SAMD9L, REL, IL6ST, TNFAIP3, ITGA2, ABCC2, AIRE, IL6R, AFF3, and TREX1. Variants of uncertain clinical significance were detected in LY96, IRAK1, RBPJ, IL6ST, ITGA2, AIRE, IL6R, JAZF1, IKZF3, IL18, IL12B, PRKCQ, PXK, and DNASE1L3. Novel variants at the following genomic coordinates were identified and have not been previously reported in association with SSc: LY96 (chr8:74922341 CT/C), PTPN22 (chr1:114381166 CT/C), IRAK1 (indels at chrX:153278833), and SAMD9L (chr7:92761606 GT/G; chr7:92764981 T/TT). Conclusions: The first immunogenetic investigation of SSc in Kazakhstan revealed a polygenic architecture involving immune signalling pathways that partially overlap with international cohorts while exhibiting region-specific variation. Although the limited sample size and lack of functional validation constrain the interpretability of the findings, the results provide a framework for larger research to confirm the pathogenic mechanisms and establish clinical relevance. Full article

The state of Bahia in Brazil possesses significant, yet underexploited, iron ore reserves. To support the initial stages of mineral exploration in this vast region, cost-effective and rapid large-scale mapping methods are essential. This paper presents a workflow based on publicly available remote sensing data for a state mineral prospectivity mapping (MPM) for iron. The methodology employs a Random Forest (RF) classification model on Sentinel-2 multispectral images, trained with a randomly selected dataset in the image at varying distances defined from the location of known iron mines in the state. The Spectral Angle Mapper (SAM) algorithm was used to categorize the samples according to spectral similarity features with laboratory-confirmed ore signatures from samples collected in the mine pit area. The resulting MPM successfully delineated known iron districts and highlighted new, unexplored areas with potential. A quantitative evaluation of the model yielded an overall accuracy of 69.8%, a macro-average F1-score of 0.697, and a Cohen’s Kappa coefficient of 0.623, indicating a reasonable agreement beyond random chance. This work demonstrates a validated, low-cost, and simple approach for regional-scale MPM, offering a valuable reconnaissance tool for preliminary exploration, particularly in extensive and data-scarce regions. Full article

Background and Objectives: Falls are a leading cause of morbidity and mortality among older adults in hospitals and long-term care facilities (LTCFs). Digital healthcare approaches are increasingly being applied to fall detection and prevention; however, their effectiveness remains uncertain. This review evaluated the effectiveness, usability, and clinical applicability of detection- and prediction-based systems in institutional settings. Materials and Methods: We systematically searched major international and Korean databases—PubMed, Embase, Ovid-MEDLINE, CINAHL, the Cochrane Library, IEEE, KMbase, KISS, KoreaMed, and RISS—for studies published up to December 2024. The eligible studies included randomized controlled trials, quasi-experimental, and observational studies involving older adults in hospitals or LTCFs. Two reviewers independently screened the studies, extracted data, and assessed their quality using standardized tools. Results: Thirty-three studies comprising 20 fall detection systems and 13 fall prediction models were included. Detection systems using inertial, pressure, radar, or multimodal sensors have improved monitoring and achieved high usability (>80% acceptance); however, they did not consistently reduce fall incidence or the occurrence of injurious falls. For instance, one trial reported a nonsignificant reduction in injurious falls (aRR 0.56, 95% CI 0.17–1.79), whereas another trial observed a nonsignificant increase (aIRR 1.60, 95% CI 0.83–3.08). Frequent false alarms contribute to alarm fatigue. The prediction models showed moderate-to-strong discrimination. Gradient boosting and neural networks performed best for continuous gait features, while regression and boosting approaches were effective for categorical EHR data. Most models lacked external validation and were not linked to clinical care pathways. Conclusions: Digital approaches show potential for fall prevention in hospitals and LTCFs; however, current evidence remains inconsistent and limited. Detection systems improve surveillance but offer limited preventive effects, whereas prediction models demonstrate technical promise without establishing clinical benefits. Future research should refine the technology, validate models externally, and integrate them into patient-centered workflows. Full article

Background/Objectives: Depressive disorders are among the most prevalent mental illnesses worldwide. Digital health interventions offer potential to improve access, efficiency, and outcomes in depression care. However, their sustainable integration into routine clinical practice remains limited. This study explored individual, organizational, external and contextual factors influencing digital health interventions implementation from the perspective of health professionals. Methods: Semi-structured interviews with health professionals (n = 9) were analyzed using a hybrid qualitative approach. First, structuring content analysis following Kuckartz was applied to systematically code and categorize the transcripts. Second, the resulting codes were mapped onto four domains of the Consolidated Framework for Implementation Research (Outer Setting, Inner Setting, Process, and Characteristics of Individuals) to identify implementation-relevant barriers and facilitators. This combined approach ensured a transparent, theory-informed, and reproducible analysis of factors influencing digital health intervention implementation in depression care. Results: Key individual-level enablers included openness to innovation, motivation, and prior experience with digital tools. Organizational factors such as leadership support, designated facilitators, time, training, and IT infrastructure were critical. External factors included data protection, clear regulatory frameworks, reimbursement mechanisms, and scientific validation. Barriers involved limited digital skills, ambiguous responsibilities, and concerns about misuse or risks. Conclusions: The successful implementation of digital health interventions in depression care requires alignment with organizational structures, provider capabilities, and patient needs. Supportive leadership, tailored training, and clear external frameworks can enhance acceptance and sustainability. As complementary tools, digital health interventions can help optimize mental health services and improve patient outcomes. Full article

The existing research on open-set recognition (OSR) and novel class discovery (NCD) has largely treated these tasks as independent fields. OSR aims to identify samples that do not belong to the training set classes, while NCD seeks to further classify such unseen, unlabeled samples into novel classes. However, there is a lack of a unified framework to automate both tasks systematically. In this paper, we propose a unified training framework to identify and categorize unseen samples. Specifically, we conduct a comprehensive evaluation of existing post hoc OSR methods and observe that their performance is highly sensitive to the temperature scaling factor. To address this, we introduce a distance-based evaluation method for OSR, which not only outperforms existing post hoc approaches but also integrates seamlessly with them to deliver enhanced performance. Furthermore, we developed a prototype-based classification head leveraging this distance metric, which facilitates compact feature representations for known classes and guides the clustering of unknown classes, thereby significantly enhancing the classification accuracy for novel classes. On the CUB-200-2011 dataset, our unified framework achieves a 0.95–6.12% improvement in AUROC scores on OSR benchmarks and a 3.19% increase in classification accuracy for novel classes. Extensive experiments and visualizations validate the effectiveness of the proposed approach. We believe that this unified framework will pave the way for automating the integration of OSR and NCD, offering a more efficient and systematic approach to addressing these tasks. Full article