Multidisciplinary Digital Publishing Institute (Switzerland)
Multidisciplinary Digital Publishing InstituteNot a member yet
1861300 research outputs found
Sort by
Research on Motion Control of Hydraulic Manipulator Based on Prescribed Performance and Reinforcement Learning
No full textAchieving high-precision motion control for hydraulic manipulators presents a challenging task. Addressing the issue of low motion control accuracy caused by the strong electromechanical-hydraulic coupling characteristics of hydraulic manipulator systems, this paper innovatively introduces an RBF neural network and an Actor–Critic reinforcement learning architecture within a performance-based control framework designed using the inverse method. This approach enables dual compensation for both internal uncertainties and external disturbances within the manipulator, thereby enhancing the system’s control performance. First, within the control architecture, the performance function ensures system transient performance while employing an RBF neural network to estimate and compensate for internal unmodeled errors caused by mechanical coupling and hydraulic parameter uncertainties. Stability proofs are used to derive the network weight update rate. Second, a disturbance compensator is designed based on reinforcement learning. Deployed into the controller through offline training and online adaptation, it compensates for external system disturbances, further improving control accuracy. Finally, comparative and ablation experiments conducted on a hydraulic manipulator testbed demonstrate the effectiveness of the disturbance compensator. Compared to PID control, the proposed approach achieves a 60–65% improvement in control accuracy
Clinical Remission and Its Determinants in Adult Severe Asthma Patients Receiving Biologic Therapy: A Retrospective Analysis
No full textBackground/Objectives: In recent years, the concept of clinical remission under treatment in asthma has gained increasing attention. It is defined as the absence of exacerbations, asthma symptoms, and oral corticosteroid use for at least 12 months, together with improved or stable lung function. This study aimed to evaluate the clinical remission rates and associated factors in patients with severe asthma receiving biologic therapy with either omalizumab (anti-IgE) or mepolizumab (anti-IL-5). Methods: Adult patients with severe asthma and type 2 inflammation who started omalizumab or mepolizumab between January 2009 and December 2023 in our allergy clinic were retrospectively analyzed. Sociodemographic and clinical characteristics were reviewed. Clinical remission rates were assessed at the first and most recent years of maintenance therapy. Independent markers were identified using multivariable analyses. Results: A total of 160 patients were included (mean age 53.8 ± 14.6 years; 81.9% female). Of these, 85.6% received omalizumab and 14.4% mepolizumab. Remission rates at one year and at the latest follow-up were 60.0% and 43.7%, respectively. Patients achieving remission had higher total IgE levels. Psychiatric comorbidity negatively affected remission. The one-year remission rates were 91.3% in the mepolizumab group and 54.7% in the omalizumab group. Higher baseline blood eosinophil counts and Asthma Control Test (ACT) scores were positive markers, while psychiatric disease was inversely associated. Conclusions: Omalizumab and mepolizumab achieved meaningful clinical remission rates in severe asthma. Elevated ACT scores and eosinophil counts and absence of psychiatric comorbidities were independent markers, underscoring the need for individualized biologic therapy to achieve sustained remission
Sustainability-Oriented Student Perspectives on University–Government–Kindergarten Collaboration in Early Childhood Teacher Education
No full textThis study examines the effectiveness of the University–Government–Kindergarten Collaboration (UGK) model in training early childhood teacher candidates (TCs), using the Education for Sustainable Development (ESD) framework to assess its role in enhancing TCs’ competencies. Data were collected through a survey of 210 TCs and in-depth interviews with 12 participants. The findings indicate a structural imbalance in UGK: while university–kindergarten collaboration shows some effectiveness, the lack of governmental leadership weakens tripartite synergy. From an ESD perspective, although UGK fosters basic collaborative skills, it does not systematically develop higher-order ESD competencies such as systems thinking, normative awareness, critical thinking, and strategic action. By shifting the focus from institutional to student experience, this study offers a new analytical framework for teacher education models. It concludes that optimizing UGK requires stronger governmental coordination, deeper university–kindergarten cooperation, and explicit integration of ESD core competencies throughout the training system
Modeling and Assessment of Salinity Reduction Strategies in the Jarahi River, Iran
No full textThis study investigates the spatial and temporal variations in salinity in the Jarahi River and its traditional channels using field measurements and numerical simulations. The primary objective is to assess the effectiveness of different management strategies for salinity reduction under minimum-discharge conditions. Salinity dynamics were analyzed through electrical conductivity (EC) measurements collected over a one-year period and simulated using the MIKE 11 hydrodynamic model. Model performance was evaluated by comparing simulated and observed EC values at key monitoring stations. The results indicate that maximum salinity levels occur during March and April in both the main river and traditional channels, while the highest temporal variability in EC was observed in October. The comparison between observed and simulated data showed a relative error of less than 10%, confirming the reliability of the model simulations. Four management scenarios were evaluated: (1) preventing inflow from the Motbeg drainage, (2) blocking non-centralized drainage inputs, (3) removing all inlet drains, and (4) increasing discharge releases from the Ramshir Dam. The first and third scenarios led to the highest salinity reductions, reaching up to 39% (approximately 1266 µS/cm) in the Gorgor channel, while reductions of up to 53% were observed in traditional streams such as Mansuri and Omal-Sakher under the third scenario. Increasing dam releases resulted in a maximum reduction of 23% (724 µS/cm) at the Gorgor station. Finally, the proposed management strategies significantly reduced salinity levels along the river system, particularly at the entrance of the Jahangiri traditional stream, providing practical insights for salinity control and river basin management
Basil as a Green Alternative to Synthetic Additives in Clean Label Gilthead Sea Bream Patties
No full textThis study investigated the effectiveness of basil (Ocimum basilicum L.) extract obtained by hydrodistillation (EO) and lipid extract (LE) obtained via supercritical fluid extraction in preserving the quality of ground fish patties during refrigerated storage. Gilthead sea bream (Sparus aurata) patties were formulated with varying concentrations of EO and LE and evaluated over three days at 4 °C. The chemical composition of the extracts, analyzed by GC-MS, revealed linalool, eucalyptol, and τ-cadinol as dominant bioactive compounds, with EO richer in monoterpenes and LE in sesquiterpenes. Both extracts significantly reduced lipid oxidation (TBARS) and protein oxidation (thiol content), with the strongest antioxidative effect observed in patties containing 0.150 µL/g of LE. Color parameters (L*, a*, b*, ΔE) were moderately influenced, without adverse effects on product appearance. pH and water activity values remained stable across treatments, while total volatile basic nitrogen (TVB-N) levels confirmed delayed spoilage in extract-treated patties. Results highlight the potential of basil extracts, especially LE obtained by SFE, as effective natural antioxidants in fish-based products. These findings support the development of clean-label, health-promoting products tailored to individual needs, and show that ground fish porridge has promise as a viable material for the production of innovative seafood products
Influence of Teucrium montanum Hydrolate Integration on the Functional Performance of Chitosan-Based Films
No full textNatural biodegradable polymers such as chitosan are gaining increasing importance due to their favorable mechanical properties. Conversely, their limited antimicrobial and antioxidant activity requires enhancement with bioactive components. This study investigated the effect of Teucrium montanum L. hydrolate on the functional properties of chitosan films. The hydrolate was obtained as a by-product of hydrodistillation, and films were prepared with 0.6% (CH-TMh1), 0.8% (CH-TMh2), and 1.2% (CH-TMh3) hydrolate, along with a control film without hydrolate (CH). Hydrolate-enriched films exhibited greater thickness and elongation at break, with the highest values observed in CH-TMh3. The addition of hydrolate reduced moisture content (from 30.09% in CH to 12.25% in CH-TMh3), solubility, and swelling degree. Antioxidant activity increased significantly, with CH-TMh2 showing the highest free radical scavenging activity (92.9%) and total polyphenol content (38.78 mg GAE/g). Films containing hydrolate also displayed pronounced antimicrobial activity, with the largest inhibition zones against S. aureus ATCC 25923 (16.33 mm). Moderate activity was observed against B. subtilis, while there was no activity against C. albicans ATCC 2091. These results confirm that chitosan films enriched with T. montanum L. hydrolate possess improved mechanical, antioxidant, and antimicrobial properties, making them promising for potential application in the packaging of specific food products
Explaining Logistics Performance, Economic Growth, and Carbon Emissions Through Machine Learning and SHAP Interpretability
No full textThis study provides a multi-faceted and detailed perspective on the relationships between logistics performance, environmental degradation, and economic growth in 38 OECD countries, using each as an individual target variable. In the Analysis section, the relationship between logistics and environment is examined within a broader context, taking economic indicators into account. This examination utilizes the machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). For each algorithm, the dataset is split into training and testing sets using three different ratios: 90:10, 80:20, and 70:30. A comprehensive performance evaluation is conducted on each of these splits by applying 5-fold and 10-fold cross-validation (CV). Considering economic indicators, the analysis section examines how the logistics-environment relationship is shaped in a broader context using the machine learning algorithms RF, XGBoost, and LightGBM. MSE, MAE, RMSE, MAPE, and R2 metrics are utilized to evaluate model performance, while MDA and SHAP are employed to assess feature importance. Furthermore, a bee swarm plot is leveraged for visualizing the results. The XGBoost algorithm can successfully predict carbon dioxide (CO2) emissions from transport and economic growth with high accuracy. However, the logistics performance model achieves high performance only with the LightGBM algorithm using a 90% train, 10% test split, and 5-fold CV setup. Based on the variable importance levels of the best-performing algorithm for each of the three target variables separately, the prediction of logistics performance is largely dependent on the economic growth predictor, and secondly, on the trade openness predictor. In predicting CO2 emissions from transport, economic growth is identified as the most effective predictor, while logistics performance and trade openness contribute the least to the prediction. The findings also reveal that transport-related emissions and environmental indicators are prominent in the prediction of economic growth, whereas logistics performance and trade openness play a supportive, yet secondary role
Unmasking the Apex: Multimodality Imaging for the Evaluation of Left Ventricular Apical Obliteration
No full textLeft ventricular (LV) apical obliteration represents a convergent imaging phenotype arising from diverse cardiac conditions, including thrombotic, hypertrophic, infiltrative, congenital, and neoplastic diseases. These conditions, despite sharing overlapping morphological features, require profoundly different management strategies. In this context, an accurate characterization of the LV apex is a cornerstone point, and can be performed through various techniques. Advances in multimodality imaging have substantially improved diagnostic precision, allowing clinicians to differentiate true obliteration from mimicking conditions such as hypertrabeculation, apical hypertrophy, or subendocardial fibrosis. This review provides a comprehensive overview of the anatomical variability of the LV apex and its implications for imaging interpretation. We appraise the role of echocardiography, including contrast-enhanced and speckle-tracking studies—alongside cardiac magnetic resonance (CMR), computed tomography (CT), and selective nuclear imaging in the evaluation of apical pathology. For each principal cause of apical obliteration—LV thrombus, apical hypertrophic cardiomyopathy, left ventricular non-compaction, endomyocardial fibrosis, cardiac amyloidosis, and intracardiac tumors—we outline key diagnostic clues, imaging red flags, and distinguishing tissue characteristics. Special emphasis is given to the incremental value of CMR for tissue characterization, thrombus detection, and fibrosis mapping, as well as to the interpretative challenges posed by apical foreshortening, near-field artefacts, and suboptimal acoustic windows. A practical, stepwise imaging framework is proposed to guide clinicians through the differential diagnosis of apical obliteration using an integrated multimodality approach. Future directions include the incorporation of 4D flow, advanced mapping techniques, and artificial intelligence-powered analysis to refine apical phenotyping and identify early disease signatures. Recognizing the full spectrum of apical pathology and its imaging manifestations is essential to prevent misdiagnosis, enable timely therapeutic decisions, and improve risk stratification
Pedagogical Tact Insights in Online Learning Communities
No full textThe growing reliance on AI-powered EdTech solutions has prompted educators at all levels to rethink teaching and learning methodologies. This shift has fostered a renewed partnership among teachers, students, and society, repositioning AI from a passive support tool into a proactive agent in the classroom. This transformation calls for teachers to exercise leadership and judgement in guiding students’ use of AI, emphasising both responsible practices and ethical considerations within their broader socio-cultural contexts. To harness this potential, we leveraged AI-based solutions within the AECT academic association to reinterpret UNESCO’s four foundational pillars of learning, thereby impacting the broader educational community. This initiative underscores literacy in educational communities emerging from intra-national and international inequity. Hence, it is imperative to examine the exigency of fundamental rights in relation to ethics and norms to uphold the innovative opportunities of AI in education globally. In this regard, this study connects the Pedagogical AI-Tact concept to bridge the gap between theory and practice, fostering both interest and ethical engagement across diverse educational communities. This study valuably upholds Margaret Mead’s proposal that every child deserves universal educational rights, a principle in harmony with justice and freedom
Assessment of Propulsion Patterns for Hybrid Wing Configuration Aircraft with Embedded Propellers
No full textThis study employs computational fluid dynamics (CFD) to investigate the aerodynamic performance and static stability of hybrid wing aircraft, considering the interference of counter-rotating embedded propellers. Extensive numerical verification has been carried out, including comparisons with NASA’s high-lift propeller (HLP) data. Three configurations—no propeller, counter-rotating inboard-upwash (CNIU) and counter-rotating outboard-upwash (CNOU) are defined to analyze the aerodynamic force/moment characteristics and flow field structures over a range of angles of attack from −6° to 26°, in conjunction with crosswind velocities of 0, 5, 10, and 15 m/s. The propeller-induced slipstream alters the aircraft’s fundamental performance by modifying wing pressure distributions and vortex systems. Specifically, the CNIU configuration increases the low-pressure areas on both the fuselage and outer wing upper surfaces, enhancing the lift-to-drag ratio by 28.4% at low angles of attack. In contrast, the CNOU configuration improves longitudinal steady-static margin by 27.4% under typical conditions and demonstrates superior lateral static stability under 10 m/s leftward crosswind conditions. For engineering applications in the aerodynamic design of such aircraft, the CNIU configuration is recommended for high cruise efficiency, whereas the CNOU configuration is preferred for flight stability