Search Results (21,727)

With the development of inspection robot control technology, wheel-legged robots are increasingly used in complex underground space inspection. To address low stability during obstacle crossing in unstructured terrains, a motion control strategy integrating an improved CPG algorithm and a biological reflex mechanism is proposed. It introduces an adaptive coupling matrix, augmented with the Lyapunov function, and vestibular/stumbling reflex models for real-time motion feedback. Simulink–Adams virtual prototypes and single-wheeled leg experiments (on the left front leg) were used to verify the system. Results show that the robot’s turning oscillation was ≤±0.00593 m, the 10° tilt maintained a stable center of mass at 10.2° with roll angle fluctuations ≤±5°, gully-crossing fluctuations ≤±0.01 m, and pitch recovery ≤2 s. The experiments aligned with the simulations, proving that the strategy effectively suppresses vertical vibrations, ensuring stable and high-precision inspection. Full article

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments. Full article

Background/Objective: Nursing and non-nursing students experience high stress levels, making them susceptible to mental health issues. This study compared stress, anxiety, and depression between these two groups after 2 years of the COVID-19 pandemic. Additionally, it explored the relationship between perceived helplessness, self-efficacy, and symptoms of mental stress and strain resulting from challenging internship conditions for nursing students. Methods: This cross-sectional study included 154 nursing students (mean age = 22.43 years) and 291 non-nursing students (mean age = 27.7 years). Data were collected using the Depression Anxiety Stress Scales (DASS-21), Perceived Stress Scale-10 (PSS-10), and a questionnaire on mental stress and strain. Results: Nursing students reported significantly higher scores in the DASS-21 subscales depression (ηp² = 0.016) and anxiety (ηp² = 0.037), and global stress (PSS-10; ηp² = 0.029) compared to non-nursing students, but no significant difference on the DASS-21 Stress subscale. The observed group differences in the present study may be partially attributed to group differences in demographic factors. Helplessness correlated strongly with nearly all scales of mental stress and strain during internships (all p’s < 0.001), while self-efficacy showed a strong negative correlation with non-occupational difficulties, health impairment, and emotional problems (all p’s < 0.001). Conclusions: Nursing students experience elevated depression, anxiety, and perceived stress levels compared to non-nursing students. Stronger feelings of helplessness and lower confidence in their ability to overcome challenges were strongly correlated with mental stress and strain during clinical training. Targeted interventions such as cognitive behavioral training and stress management should be integrated into nursing curricula to enhance resilience and coping strategies. Full article

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

The proliferation of social media has paradoxically facilitated the widespread dissemination of fake news, impacting individuals, politics, economics, and society as a whole. Despite the increasing scholarly research on this phenomenon, a significant gap exists regarding its dynamics in developing countries, particularly how predictors of fake news sharing interact, rather than merely their net effects. To acquire a more nuanced understanding of fake news sharing behavior, we propose identifying the direct and complex interplay among key variables by utilizing a dual analytical framework, leveraging Structural Equation Modeling (SEM) for linear relationships and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to uncover asymmetric patterns. Specifically, we investigate the influence of news-find-me orientation, social media trust, information-sharing tendencies, and status-seeking motivation on the propensity of fake news sharing behavior. Additionally, we delve into the moderating influence of social media literacy on these observed effects. Based on a cross-sectional survey of 1028 Haitian social media users, the SEM analysis revealed that news-find-me perception had a negative but statistically insignificant influence on fake news sharing behavior. In contrast, information sharing exhibited a significant negative association. Trust in social media was positively and significantly linked to fake news sharing behavior. Meanwhile, status-seeking motivation was positively associated with fake news sharing behavior, although the association did not reach statistical significance. Crucially, social media literacy moderated the effects of trust and information sharing. Interestingly, fsQCA identified three core configurations for fake news sharing: (1) low status seeking, (2) low information-sharing tendencies, and (3) a unique interaction of low “news-find-me” orientation and high social media trust. Furthermore, low social media literacy emerged as a direct core configuration. These findings support the urgent need to prioritize social media literacy as a key intervention in combating the dissemination of fake news. Full article

This study estimates the prevalence of co-use of alcohol and cannabis, assesses the sociodemographic risk factors of co-use, and examines the associations between mental health and heavy episodic drinking (HED) and alcohol–cannabis co-use in Canada during the early years of the COVID-19 pandemic. Nine successive cross-sectional surveys, held from May 2020 to January 2022, of adults (aged ≥18 years) living in Canada were pooled for 9011 participants. The prevalence of co-use was calculated across sociodemographic groups. Logistic regressions were used to assess associations. Alcohol–cannabis co-use was associated with a greater likelihood of engaging in HED and experiencing symptoms of anxiety, depression, and loneliness. The prevalence of co-use of alcohol was different across sociodemographic groups. The highest prevalence was among TGD people (35.5%), followed by individuals aged 18–39 years (14.5%). Additionally, being TGD (aOR = 3.61, 95% CI 2.09–6.25), separated/divorced/widowed (aOR = 1.60, 95% CI 1.23–2.07), living in an urban area (aOR = 1.26, 95% CI 1.07–1.56), and having a high household income (aOR = 1.41, 95% CI 1.09–1.82) increased the likelihood of reporting alcohol–cannabis co-use. These findings underscore the fact that developing public health and clinical interventions for preventing and treating excessive alcohol or cannabis use must consider both alcohol and cannabis use patterns and should be tailored to the highest-risk TGD and young adults. Full article

Background/Objectives: Neoadjuvant radiochemotherapy and perioperative chemotherapy are both well-established treatment strategies for locally advanced adenocarcinoma of the esophagus (EAC) and the esophagogastric junction (AEGJ). However, recent knowledge controversially discusses whether neoadjuvant radiotherapy or perioperative chemotherapy represents superior therapeutic options to prolong survival or cause less toxicity. Methods: We retrospectively analyzed 76 patients with locally advanced EAC or AEGJ treated at our tertiary cancer center between January 2015 and March 2023. Patients received either perioperative FLOT chemotherapy (n = 36) or neoadjuvant radiochemotherapy following the CROSS protocol (n = 40), followed by surgical resection and standardized follow-up. We compared survival outcomes, toxicity profiles, treatment compliance, and surgical results between the two groups. Results: There were no statistically significant differences between FLOT and CROSS treatments in five-year loco-regional controls (LRC: 61.5% vs. 68.6%; p = 0.81), progression-free survival (PFS: 33.9% vs. 42.8%; p = 0.82), overall survival (OS: 60.2% vs. 63.4%; p = 0.91), or distant controls (DC: 42.1% vs. 56.5%; p = 0.39). High-grade hematologic toxicities did not significantly differ between groups (p > 0.05). Treatment compliance was lower in the FLOT group, with 50% (18/36) not completing all the planned chemotherapy cycles, compared to 17.5% (7/40) in the CROSS group. All the patients in the CROSS group received the full radiotherapy dose. Surgical outcomes and post-surgical tumor status were comparable between the groups. Conclusions: Although perioperative chemotherapy with FLOT has recently become a standard of care for locally advanced EAC and AEGJ, neoadjuvant radiochemotherapy per the CROSS protocol remains a well-tolerated alternative. In appropriately selected patients, both approaches yield comparable oncological outcomes. Full article

Pediatric palliative care (PPC) aims to enhance the quality of life of children with life-limiting conditions and their families through individualized, interdisciplinary support. Among this population, children with neurological diseases represent a substantial and growing group, often facing prolonged disease courses, cognitive impairment, and high prognostic uncertainty. Effective communication is central to PPC; however, it remains deeply influenced by cultural, religious, and spiritual frameworks that shape family perceptions of illness, suffering, and decision-making. This narrative review explores communication strategies in PPC, with a specific focus on children with neurological conditions, highlighting conceptual foundations, cross-cultural variations, and emerging best practices. Key findings highlight the importance of culturally humble approaches, family-centered communication models, and structured tools, such as co-designed advance care planning and dignity therapy, to enhance communication. Additionally, the review highlights the presence of ethical and interdisciplinary challenges, particularly in neonatal and neurology settings, where misaligned team messaging and institutional hesitancy may compromise trust and timely referral to palliative care. Future research, policy, and clinical education priorities should advocate for models that are inclusive, ethically grounded, and tailored to the unique trajectories of neurologically ill children. Integrating cultural competence, team alignment, and family voices is essential for delivering equitable and compassionate PPC across diverse care settings. Full article

The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F₁ generations. In the following years, the F₁ hybrids were advanced to the BC₂F₁ and BC₂F₂ generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC₂F₂ lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC₂F₂ plants were subsequently advanced to homozygous BC₂F₃ lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security. Full article

The classification of cancer based on gene expression profiles is a central challenge in precision oncology due to the high dimensionality and low sample size inherent in microarray datasets. Effective gene selection is crucial for improving classification accuracy while minimizing computational overhead and model complexity. This study introduces Genetic-Embedded Nuclear Reaction Optimization (GNR), a novel hybrid metaheuristic that enhances the conventional Nuclear Reaction Optimization (NRO) algorithm by embedding a genetic uniform crossover mechanism into its fusion phase. The proposed algorithm leverages a two-stage process: an initial F-score filtering step to reduce dimensionality, followed by GNR-driven optimization to identify compact, informative gene subsets. Evaluations were conducted on six widely used microarray cancer datasets, with Support Vector Machines (SVM) employed as classifiers and performance assessed via Leave-One-Out Cross-Validation (LOOCV). Results show that GNR consistently outperforms the original NRO and several benchmark hybrid algorithms, achieving 100% classification accuracy with significantly smaller gene subsets across all datasets. These findings confirm the efficacy of the genetic-embedded fusion strategy in enhancing local exploitation while preserving the global search capabilities of NRO, thereby offering a robust and interpretable approach for gene selection in cancer classification. Full article

This study explores fine-tuning methods and dataset structures for multilingual neural machine translation using the No Language Left Behind model, with a case study on Kazakh, English, and Russian. We compare single-stage and two-stage fine-tuning approaches, as well as triplet versus non-triplet dataset configurations, to improve translation quality. A high-quality, 50,000-triplet dataset in information technology domain, manually translated and expert-validated, serves as the in-domain benchmark, complemented by out-of-domain corpora like KazParC. Evaluations using BLEU, chrF, METEOR, and TER metrics reveal that single-stage fine-tuning excels for low-resource pairs (e.g., 0.48 BLEU, 0.77 chrF for Kazakh → Russian), while two-stage fine-tuning benefits high-resource pairs (Russian → English). Triplet datasets improve cross-linguistic consistency compared with non-triplet structures. Our reproducible framework offers practical guidance for adapting neural machine translation to technical domains and low-resource languages. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

Non-invasive ultrasound diagnosis, combined with deep learning, is frequently used for detecting thyroid diseases. However, real-time detection on portable devices faces limitations due to constrained computational resources, and existing models often lack sufficient capability for small object detection of thyroid nodules. To address this, this paper proposes an improved lightweight small object detection network framework called LISA-YOLO, which enhances the lightweight multi-scale collaborative fusion algorithm. The proposed framework exploits the inherent symmetrical characteristics of ultrasound images and the symmetrical architecture of the detection network to better capture and represent features of thyroid nodules. Specifically, an improved depthwise separable convolution algorithm replaces traditional convolution to construct a lightweight network (DG-FNet). Through symmetrical cross-scale fusion operations via FPN, detection accuracy is maintained while reducing computational overhead. Additionally, an improved bidirectional feature network (IMS F-NET) fully integrates the semantic and detailed information of high- and low-level features symmetrically, enhancing the representation capability for multi-scale features and improving the accuracy of small object detection. Finally, a collaborative attention mechanism (SAF-NET) uses a dual-channel and spatial attention mechanism to adaptively calibrate channel and spatial weights in a symmetric manner, effectively suppressing background noise and enabling the model to focus on small target areas in thyroid ultrasound images. Extensive experiments on two image datasets demonstrate that the proposed method achieves improvements of 2.3% in F1 score, 4.5% in mAP, and 9.0% in FPS, while maintaining only 2.6 M parameters and reducing GFLOPs from 6.1 to 5.8. The proposed framework provides significant advancements in lightweight real-time detection and demonstrates the important role of symmetry in enhancing the performance of ultrasound-based thyroid diagnosis. Full article

Laser cleaning technology has been increasingly applied in the removal of damaged protective coatings from aircraft components due to its environmental friendliness and high efficiency. Appropriate laser cleaning process parameters improve cleaning efficiency while preventing substrate damage. In this study, a Gaussian continuous-wave laser was used to remove the 120 μm coating on the surface of Ti6Al4V alloy. The influence of laser power (100 W to 200 W) and scanning speed (520 mm/min to 610 mm/min) on the paint removal effect was explored based on paint removal rate, surface roughness, microstructural evolution, and the hardness’ change in the direction of heat transfer. The results reveal that optimal paint removal parameters are achieved at a laser power of 100 W with a scanning speed of 550 mm/min. The surface roughness of the sample after paint removal (55 nm) is similar to that of the original substrate (56 nm). Through EBSD analysis, the influence of laser thermal accumulation on the microstructure of the substrate is relatively small. The average hardness of the cross-section after cleaning was 347 HV, which was only 3.41% higher than that of the original substrate. This confirms that parameter-controlled laser cleaning can effectively remove ~120 μm thick paint layers without inflicting damage on the substrate. Full article

Improving construction site safety remains a critical challenge in Saudi Arabia’s rapidly growing construction sector, where high accident rates and diverse labor forces demand evidence-based managerial interventions. This study investigated the influence of Managers’ Safety Perceptions and Practices (MSP) on Workers’ Safety Behaviors (WSB) in the Saudi construction industry, emphasizing the mediating roles of Workers’ Safety Awareness (WSA), Safety Competency (WSC), and Safety Actions (SA). The conceptual framework integrates these three mediators to explain how managerial attitudes and practices translate into frontline safety outcomes. A quantitative, cross-sectional design was adopted using a structured questionnaire distributed among construction workers, supervisors, and project managers. A total of 352 from 384 valid responses were collected, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The findings revealed that MSP does not directly influence WSB but has significant indirect effects through WSA, WSC, and SA. Among these, WSC emerged as the most powerful mediator, followed by WSA and SA, indicating that competency is the most critical driver of safe worker behavior. These results provide robust empirical support for a multidimensional mediation model, highlighting the need for managers to enhance safety behaviors not merely through supervision but through fostering awareness and competency, providing technical training, and implementing proactive safety measures. Theoretically, this study contributes a novel and integrative framework to the occupational safety literature, particularly within underexplored Middle Eastern construction contexts. Practically, it offers actionable insights for safety managers, industry practitioners, and policymakers seeking to improve construction safety performance in alignment with Saudi Vision 2030. Full article