Search Results (269)

As wind power penetration continues to increase, the sub-synchronous control interaction (SSCI) problem caused by the interaction between doubly fed induction generators (DFIGs) and series-compensated transmission lines has become increasingly prominent, posing a serious threat to power system stability. To address this problem, this research proposes a centralized sub-synchronous oscillation damping controller (CSODC) for wind farms. First, a DFIG impedance model was constructed based on multi-operating-point impedance scanning and a Taylor series expansion, achieving impedance prediction with an error of less than 2% under various power conditions. Subsequently, a CSODC comprising a sub-synchronous damping calculator (SSDC) and a power electronic converter is designed. By optimizing feedback signals, phase shift angles, gain parameters, and filter parameters, dynamic adjustment of controllable impedance in the sub-synchronous frequency band is achieved. Frequency-domain impedance analysis demonstrates that the CSODC significantly enhances the system’s equivalent resistance, reversing it from negative to positive at the resonance frequency point. Time-domain simulations validated the CSODC’s effectiveness in scenarios involving series capacitor switching and wind speed disturbances, demonstrating rapid sub-synchronous current decay. The results confirm that the proposed strategy effectively suppresses sub-synchronous oscillations across multiple scenarios, offering an economical and efficient solution to stability challenges in high-penetration renewable energy grids. Full article

Background/Objectives: In the age of digitalization, nomophobia has emerged as a relevant issue, especially among university students who utilize smartphones heavily for academic and social purposes. The Stressor–Strain–Outcome (SSO) framework explains the relationship between stressors, strain, and outcomes. Stressors such as nomophobia induce psychological strain. This strain subsequently influences outcomes like mindfulness. Nomophobia has been linked to higher distress, including depression, anxiety, and stress, that can negatively impact students’ focus. However, the mechanisms by which nomophobia impacts mindfulness remain less explored. Hence, this study aims to analyze the mediating effect of distress on the relation between student’s nomophobia and mindfulness. Methods: In this quantitative study, the researcher employed a structured close-ended survey to collect data from 723 students at the University of Ha’il in Saudi Arabia. Nomophobia was measured using the Nomophobia Questionnaire (NMP-Q). The level of distress was measured using the Depression, Anxiety, and Stress scale (DASS-21) Furthermore, the assessment of mindfulness was conducted using the Mindful Attention Awareness Scale (MAAS). Structural equation modeling was utilized to test the hypotheses of this study. Results: The results from PLS-SEM indicate that nomophobia did not directly reduce mindfulness, as its effect was statistically non-significant (β_1 = −0.052, p-value = 0.168). This suggests that nomophobia alone may not weaken focus. However, it significantly increased distress, particularly depression (β_2a = 0.327, p-value < 0.001), anxiety (β_2b = 0.294, p-value < 0.001) and stress (β_2c = 0.259, p-value < 0.001). In plain terms, students with higher nomophobia reported more depression and stress, which in turn reduced mindfulness. Anxiety, however, did not significantly affect mindfulness (β_3b = 0.006, p-value < 0.933), indicating its influence may be negligible or context-specific. Mediation analysis confirmed indirect effects of nomophobia on mindfulness through depression (β_4a = −0.096, p-value < 0.001) and stress (β_4c = −0.045, p-value < 0.020). Together, these mediators explained a substantial portion of the variance in mindfulness. Conclusions: The findings align with the SSO model, indicating that nomophobia acts as a stressor, exacerbating distress, which in turn reduces mindfulness. From a practical perspective, the results highlight the need for comprehensive student support. Universities should integrate digital wellness programs, stress-management resources, and mindfulness training into their services. Limitations and Future Research: The cross-sectional design and convenience sampling restrict causal inference and generalizability. Future studies should employ longitudinal research designs. They should also examine diverse cultural contexts. In addition, researchers should investigate potential mediators such as social support and sleep quality. Full article

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

This paper introduces CodeDive, a web-based programming environment with real-time behavioral tracking designed to enhance student progress assessment and provide timely support for learners, while also addressing the academic integrity challenges posed by Large Language Models (LLMs). Visibility into the student’s learning process has become essential for effective pedagogical analysis and personalized feedback, especially in the era where LLMs can generate complete solutions, making it difficult to truly assess student learning and ensure academic integrity based solely on the final outcome. CodeDive provides this process-level transparency by capturing fine-grained events, such as code edits, executions, and pauses, enabling instructors to gain actionable insights for timely student support, analyze learning trajectories, and effectively uphold academic integrity. It operates on a scalable Kubernetes-based cloud architecture, ensuring security and user isolation via containerization and SSO authentication. As a browser-accessible platform, it requires no local installation, simplifying deployment. The system produces a rich data stream of all interaction events for pedagogical analysis. In a Spring 2025 deployment in an Operating Systems course with approximately 100 students, CodeDive captured nearly 25,000 code snapshots and over 4000 execution events with a low overhead. The collected data powered an interactive dashboard visualizing each learner’s coding timeline, offering actionable insights for timely student support and a deeper understanding of their problem-solving strategies. By shifting evaluation from the final artifact to the developmental process, CodeDive offers a practical solution for comprehensively assessing student progress and verifying authentic learning in the LLM era. The successful deployment confirms that CodeDive is a stable and valuable tool for maintaining pedagogical transparency and integrity in modern classrooms. Full article

The global demand for sustainable energy has accelerated the development of biofuels, aiming to reduce fossil fuel reliance and environmental impact. Second-generation ethanol (2G), produced from lignocellulosic biomass such as sugarcane bagasse and straw, is a promising alternative aligned with the circular economy. Its production relies on pretreatments to improve the enzymatic access to polysaccharides. Among the available methods, the organosolv (O) and hydrothermal (H) pretreatments are effective in separating the biomass into cellulose-rich pulps and hemicellulosic liquors. In this study, these pretreatments were applied to sugarcane bagasse (SCB) and straw (SS), aiming to obtain hemicellulosic fractions for bioconversion. The characterization of pretreated biomasses showed increased cellulose content, indicating successful delignification. After the lignin precipitation, the hemicellulosic liquors were submitted to enzymatic hydrolysis, with increases in the total reducing sugar (TRS) concentrations, from 11.144 to 13.440 g·L⁻¹ (SBO), 16.507 to 22.492 g·L⁻¹ (SBH), 8.560 to 9.478 g·L⁻¹ (SSO), and 14.164 to 22.830 g·L⁻¹ (SSH), with highlights for the hydrothermal pretreated hydrolysates in the improvement of sugar release. HPLC confirmed these gains, notably in the xylose content. The results indicated the potential of hemicellulosic liquors for the fermentation of pentoses, supporting integrated bioethanol production. This approach promotes the efficient use of agro-residues and strengthens the role of biofuels in low-carbon and sustainable energy systems. Full article

Background/Objectives: To develop and validate deep learning-based models for detecting significant steno-occlusion (SSO)—defined as luminal narrowing greater than 50%—of the femoropopliteal arteries using maximum intensity projection (MIP) images from lower extremity CT angiography (CTA). Methods: This retrospective study utilized MIP images of lower extremity CTA performed between January 2021 and December 2023 for internal model development. Deep learning-based models were developed sequentially to diagnose SSO: screening with single anteroposterior image, followed by four-segment rotational analysis that divided each femoropopliteal artery into four segments and incorporated multi-angle images. Given the cropped images and the shape of stenosis, models were trained to classify the presence of SSO. A temporal validation dataset comprised MIP images from lower extremity CTA performed between January and June 2024. Results: In total, 56,496 segment images from 642 patients (mean age: 68.2 ± 13.5 years; 472 men) were included in the internal dataset. In the single-image analysis, RDNet achieved the highest mean AUC of 0.886 for SSO detection. In the four-segment rotational analysis, RDNet also demonstrated the highest mean AUC, reaching 0.964 in both half-set and full-set approaches. While RDNet recorded the highest mean AUC, all other models showed improved AUCs as the number of input images increased (p < 0.05). In the temporal validation dataset, RDNet again achieved the highest mean AUC (0.959) in the half-set analysis. Conclusions: The deep learning-based model, particularly RDNet, demonstrated excellent performance in detecting SSO of peripheral arteries on MIP images from lower extremity CTA. Full article

Optimal camera placement (OCP) is a crucial task for ensuring adequate surveillance of both indoor and outdoor environments. While several solutions to this problem have been documented in the literature, there are still research gaps related to the maximization of surveillance coverage, particularly in terms of optimal placement of omnidirectional camera (OC) sensors in indoor and partially occluded environments via metaheuristic optimization algorithms (MOAs). In this paper, we present a study centered on several popular MOAs and their application to OCP for OC sensors in indoor environments. For our experiments we considered two experimental layouts consisting of both a deployment area, and visual obstructions, as well as two different omnidirectional camera models. The tested MOAs include popular algorithms such as PSO, GWO, SSO, GSA, SMS, SA, DE, GA, and CMA-ES. Experimental results suggest that the success in MOA-based OCP is strongly tied with the specific search strategy applied by the metaheuristic method, thus making certain approaches preferred over others for this kind of problem. Full article

Background: It has been shown that human leucocyte antigen (HLA) alleles are related to certain diseases. Some alleles were associated with alloimmunization in individuals with thalassemia. In this study, we studied the distribution of HLA alleles among beta thalassemia (BT) patients compared to healthy controls. Material and Methods: The HLA results of 100 patients with BT and 100 healthy controls were obtained for the study. The HLA-A, -B and -DRB1 tissue typing were performed at the laboratory. The low-resolution sequence-specific primer (SSP)–polymerase chain reaction (PCR-SSP) (Olerup HLA-A,B,DR typing kit, USA) and sequence-specific oligonucleotide (SSO)–PCR (LABType HLA-A,B,DR kit, ABD) methods were performed using the Luminex genotyping kits. All related data were retrospectively analyzed. Results: One in five patients (21%) underwent hematopoietic stem cell transplantation (HSCT). Patients with HSCT had significantly lower frequency of HLA-B *14, HLA-DRB1 *11 and HLA-DRB1 *16 alleles and had a higher frequency of HLA-A *66, HLA-B *41, HLA-B *55, HLA-DRB1 *3 alleles compared to patients without HSCT (p < 0.05). The HLA-A *3, HLA-B *41 and HLA-B *55 alleles were more commonly seen in HSCT patients compared to the healthy group (p = 0.04). Female patients showed a higher frequency of HLA-B *58 and HLA-DRB1 *4 alleles (p = 0.04). Conclusions: This study demonstrated that HLA-B *41 and -B *55 alleles were closely related to HSCT among BT patients. It might be considered that the variance in certain HLA-B alleles in BT patients might cause difficulty in finding a matched donor in this limited population. Full article

As the principal dietary source of lipids, edible oils (notably vegetable oils) exist in crude form predominantly as triacylglycerols (about 95%), with the remainder comprising impurities and diverse minor components. Therefore, the refining processes of vegetable oil are particularly important. The application potential of safflower seed oil (SSO) in both nutraceutical and pharmaceutical domains is attributed to its exceptionally high linoleic acid concentration and abundant polyphenolic constituents. However, a systematic analysis of SSO during physical refining has yet to be conducted. This study aims to investigate the effects of refining processes on the fatty acids of SSO compared with flaxseed oil (FSO). In this study, chemical analysis, gas chromatography and ultra-high-performance liquid chromatography were used to analyze and compare the physicochemical indexes, fatty acid composition, and the lipidomics of SSO and FSO. Results indicated that optimized refining significantly enhances quality parameters in both SSO and FSO. A total of 40 and 43 fatty acids were identified in SSO and FSO, respectively. Deacidification significantly altered their fatty acid profiles, particularly polyunsaturated fatty acids, with C18:2 and C18:3 being the most affected. A total of 20 significantly different lipids were screened (variable importance in projection > 1.5, p < 0.05) and were mainly classified as glycerophospholipids and glycerolipids, of which two lipids (C18:2 and C18:3 (9, 12, 15)) demonstrated particularly marked differences, suggesting that these lipid species represent significant discriminators between SSO and FSO groups; these two lipids exhibited significant alterations during the refining processes of SSO and FSO, respectively. Full article

The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water areas, while images of bottom objects were obtained using a UAV platform. The work consisted in preparing SSOs, thanks to which composite images were created. On such training data, 120 models based on the YOLO (You Only Look Once) network were obtained. The study confirmed the effectiveness of models created using YOLOv3, YOLOv5, YOLOv8, YOLOv9, and YOLOv10. A comparison was made between versions of YOLO. The influence of the amount of training data, SSO type, and augmentation parameters used in the training process was analyzed. The main parameter of model performance was the F1-score. The calculated statistics of individual models indicate that the most effective networks use partial augmentation, trained on sets consisting of 2000 SSOs. On the other hand, the increased transparency of SSOs resulted in increasing the diversity of training data and improving the performance of models. This research is developmental, and further research should improve the processes of obtaining detection models using deep networks. Full article

This paper proposes a pseudo full-channel signal integrity (SI) simulation method tailored for high-bandwidth memory (HBM) interconnects. In this approach, real interconnect models are applied to selected portions of the channel, while the remaining sections are replaced with synchronized current loads that emulate the electrical behavior of actual signal transmission. This technique enables accurate modeling of the HBM interface under full-channel parallel data transfer conditions. In addition to the simulation methodology itself, this study focuses on three specific implementation schemes for the synchronized current loads and explores their practical applications. Comparative analysis demonstrates the necessity and effectiveness of using synchronized current loads as substitutes for real transmission loads, offering a viable and efficient solution for SI analysis in HBM interconnect systems. Full article

The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration and resource allocation that allows trade-off among economic, environmental, social, and resilience objectives. This paper addresses the SSCNDP under hybrid uncertainty, which combines objective randomness got from historical data, and subjective beliefs induced by expert judgment. Building on chance theory, we formulate a dependent-chance goal programming model that specifies target probability levels for achieving sustainability objectives and minimizes deviations from these targets using a lexicographic approach. To solve this complex optimization problem, we develop a hybrid intelligent algorithm that combines uncertain random simulation with Reinforcement Learning-enhanced Salp Swarm Optimization (RL-SSO). The proposed RL-SSO algorithm is benchmarked against standard metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and standard SSO, across diverse problem instances. Results show that our method consistently outperforms these techniques in both solution quality and computational efficiency. The paper concludes with managerial insights and discusses limitations and future research directions. Full article

Background/Objectives: Patients with syndromic craniosynostosis (SC) pose a significant challenge for post-operational outcomes due to the variability in craniofacial deformities and gain-of-function characteristics. This study aims to develop validated predictive tools using stable cranial base variables to predict changes in the midfacial region and explore the craniofacial morphology among patients with SC. Methods: This study involved 17 SC patients under 12 years old, 17 age-matched controls for morphological analysis, and 21 normal children for developing craniofacial predictive models. A stable cranial base and changeable midfacial variables were analyzed using the Mann–Whitney U test. Pearson correlation identified linear relationships between the midface and cranial base variables. Multicollinearity was checked before fitting the data with multiple linear regression for growth prediction. Model adequacy was confirmed and the 3-fold cross-validation ensured results reliability. Results: Patients with SC exhibited a shortened cranial base, particularly in the middle cranial fossa (S-SO), and a sharper N-S-SO and N-SO-BA angle, indicating a downward rotation and kyphosis. The midface length (ANS-PNS) and zygomatic length (ZMs-ZTi) were significantly reduced, while the midface width (ZFL-ZFR) was increased. Regression models for the midface length, width, and zygomatic length were given as follows: ANS-PNS = 23.976 + 0.139 S-N + 0.545 SO-BA − 0.120 N-S-BA + 0.078 S-SO-BA + 0.051 age (R² = 0.978, RMSE = 1.058); ZFL-ZFR = −15.618 + 0.666 S-N + 0.241 N-S-BA + 0.155 S-SO-BA + 0.121 age (R² = 0.903, RMSE = 3.158); and ZMs-ZTi = −14.403 + 0.765 SO-BA + 0.266 N-S-BA + 0.111 age (R² = 0.878, RMSE = 3.720), respectively. Conclusions: The proposed models have potential applications for midfacial growth estimation in children with SC. Full article

Sailing Speed Optimization (SSO) is a crucial problem in shipping operations management, aiming to reduce both operating costs and carbon dioxide emissions. The ship’s sailing speed directly impacts fuel consumption, where fuel consumption is generally assumed to follow a power function with respect to sailing speed. Previous studies have used transformation-based fitting methods, such as logarithmic transformations, to investigate the relationship between sailing speed and fuel consumption using historical data. However, these methods introduce estimation bias and heteroskedasticity, violating the core assumptions of Ordinary Least Squares (OLS) used for general linear regression. To address these issues, we propose two novel fitting methods that directly optimize the original nonlinear model without relying on transformations. By analyzing the characteristics of the objective function, we derive parameter constraints and integrate them into a discrete optimization framework, resulting in improved fitting accuracy. Our methods are validated through extensive case studies, demonstrating their effectiveness in enhancing the reliability of SSO decisions. These methods offer a practical approach to optimizing fuel consumption in real-world maritime operations. Full article

Introduction: Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system (CNS) that leads to inflammation and demyelination, manifesting in either a relapsing–remitting or progressive form. As a multifactorial disease, MS involves both genetic and environmental factors, with a known significant contribution from human leukocyte antigen (HLA) genes, mainly represented by the HLA-DRB1 and HLA-DQB1 loci, which have been linked to either susceptibility or protection, but variably across populations and ethnic groups. We aimed to study the distribution and polymorphism of HLA-DRB1 and HLA-DQB1 alleles in a population with MS from the southern Moroccan region, in comparison with healthy controls. Materials and Methods: A cross-sectional study was conducted over a period of 2 years (2022–2024) in a MS cohort including 40 patients and 100 healthy controls. DRB1 and DQB1 HLA genotyping was performed using a high-resolution reverse sequence-specific oligonucleotide (SSO) method, based on the Luminex system (xMAP technology, One lambda^®). Data were analyzed using SPSS 26; differences in allele frequencies were evaluated by the Chi-square test and Fisher’s exact test. OR (95% CI) was calculated, and FDR corrections were applied for multiple testing. Results: Among the various HLA-DRB1 and DQB1 alleles studied, including those considered as predisposing to MS, the DQB1*02:01 and DRB1*15:01 alleles were more prevalent in MS patients, with 40% and 8.8% vs. 16% and 4.08% in controls respectively, although these differences were not statistically significant (p = 0.06 and p = 0.12). Likewise, the DRB1*15:01-DQB1*06:02 association was significantly more prevalent in the MS group (9%, p = 0.004). In contrast, the DRB1*07:01 allele, linked to protection against MS in many populations, was significantly predominant in controls (17%, p = 0.004). Similarly, the DRB1*07:01–DQB*02:01 combination was rather more frequent in controls (12%, p = 0.01). Confronted to MS clinical forms, we remarkably noted that the DRB1*13:03 allele was found only among relapsing–remitting MS (RRMS) patients (6%, p = 0.003), while DQB1*02:01 was significantly associated with RRMS (42.1%) and primary progressive MS (41%, p = 0.001), with an intermediate Expanded Disability Status Scale (EDSS) score, which may indicate a possible link with disease progression and severity. Conclusions: The results of our study highlighted particular HLA alleles, DRB1 and DQB1, alone or in combination, as potential immunogenic factors of susceptibility to MS in a population from southern Morocco, while other alleles seem rather to protect against the disease. This HLA polymorphism is also reflected in the clinical forms of the disease, showing a tendency toward severity for certain alleles. However, such preliminary results need to be consolidated and confirmed by studies carried out on a larger population sample, and compared with others on a national scale. Full article