Search Results (5,503)

Background: Primary implant stability is a critical factor for successful osseointegration and long-term implant success. This study investigates the impact of drilling protocol modifications on primary stability, considering different bone qualities and implant macro-designs, lengths, and diameters. Material and Methods: Three implant designs—two parallel-walled and one tapered—were tested with diameters ranging from 3.4 to 5.2 mm and lengths from 7.5 to 14.5 mm. Implants were placed in polyurethane foam blocks simulating different bone densities (10, 15, 25, and 35 PCF). A standard drilling protocol was used in all groups, with modifications based on bone quality: overpreparation in dense bone and underpreparation in softer bone. Primary stability was evaluated using insertion torque (IT). The optimal IT range was defined as 25–50 Ncm, based on clinical guidelines for immediate loading. The influence of drilling protocol adaptations on stability parameters was assessed. Results: Insertion torque was primarily influenced by bone density and implant diameter, with implant length playing a minor role. In dense bone (D1, D2), underpreparation improved torque values, especially in smaller implants, while overpreparation reduced them. The highest torques occurred with 5.2 mm implants, sometimes exceeding 80 Ncm. Standard protocols did not consistently achieve optimal torque across implant types. In soft bone (D3), underpreparation—particularly with tapered implants—was modestly beneficial. In very soft bone (D4), none of the protocols reliably reached the desired torque range. Conclusions: Adapting drilling protocols to bone density improves insertion torque, especially with wider implants and in denser bone. Underpreparation is generally more effective than overpreparation. However, in very soft bone, neither implant geometry nor drilling adaptations reliably achieve optimal primary stability, highlighting the need for additional strategies. Full article

Hand hygiene is a key measure to prevent healthcare-associated infections (HAIs), yet compliance remains low in Sub-Saharan Africa (SSA), due to limited resources, insufficient training, and behavioral challenges. Simulation-based education offers a promising approach to enhance technical and non-technical skills in safe learning environments, promoting behavioral change and patient safety. This study aimed to develop and pilot a contextually adapted hand hygiene simulation-based learning scenario for nursing students in SSA. Grounded in the Medical Research Council (MRC) Framework and Design-Based Research principles, a multidisciplinary team from European and African higher education institutions (HEIs) co-created this scenario, integrating international and regional hand hygiene guidelines. Two iterative pilot cycles were conducted with expert panels, educators, and students. Data from structured observation and post-simulation questionnaires were analyzed using descriptive statistics. The results confirm the scenario’s feasibility, relevance, and educational value. The participants rated highly the clarity of learning objectives (M = 5.0, SD = 0.0) and preparatory materials (M = 4.6, SD = 0.548), reporting increased knowledge/skills and confidence and emphasizing the importance of clear roles, structured facilitation, and real-time feedback. These findings suggest that integrating simulation in health curricula could strengthen HAI prevention and control in SSA. Further research is needed to evaluate long-term outcomes and the potential for wider implementation. Full article

Background: Anaplastic lymphoma kinase (ALK) is a key receptor tyrosine kinase involved in regulating signaling pathways critical for cell proliferation, differentiation, and survival. Mutations or rearrangements of the ALK gene lead to aberrant kinase activation, driving tumorigenesis in various cancers. Although ALK inhibitors have shown clinical benefits, drug resistance remains a significant barrier to long-term efficacy. Developing novel ALK inhibitors capable of overcoming resistance is therefore essential. Methods: A structure-based pharmacophore model was constructed using the 3D structures of five approved ALK inhibitors. Systematic virtual screening of the Topscience drug-like database was performed incorporating PAINS filtering, ADMET prediction, and molecular docking to identify promising candidates. In vitro antiproliferative assays, molecular docking, molecular dynamics simulations, and MM/GBSA binding free energy calculations were used to evaluate biological activity and elucidate binding mechanisms. Results: Two candidates, F1739-0081 and F2571-0016, were identified. F1739-0081 exhibited moderate antiproliferative activity against the A549 cell line, suggesting potential for further optimization. Computational analyses revealed its probable binding modes and interactions with ALK, supporting the observed activity. Conclusions: This study successfully identified novel ALK inhibitor candidates with promising biological activity. The integrated computational and experimental approach provides valuable insights for the rational design of optimized ALK inhibitors to address drug resistance in cancer therapy. Full article

The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting. Full article

Emergency diesel is prone to degradation during long-term storage, and experimental evaluations are costly and slow. Three-dimensional computational fluid dynamics (CFD) simulations were employed to model the diesel conditioning process. A physical model based on the actual dimensions of the storage tank was constructed. The volume of fraction (VOF) model tracked the gas–liquid interface, and the species transport model handled mixture transport. A UDF then recorded inlet and outlet flow rates and velocities in each cycle. The study focused on the effects of different baffle structures and numbers on conditioning efficiency. Results showed that increasing the baffle flow area significantly delays the mixing time but reduces the cycle time. Openings at the bottom of baffles effectively mitigate the accumulation of high-concentration conditioning oil caused by density differences. Increasing the number of baffles decreases the effective volume of the tank and amplifies density differences across the baffles, which shortens the mixing time. However, excessive baffle numbers diminish these benefits. These findings provide essential theoretical guidance for optimizing baffle design in practical diesel tanks, facilitating rapid achievement of emergency diesel quality standards while reducing costs and improving efficiency. Full article

In the field of mobile crowdsensing (MCS), a large number of tasks rely on the participation of ordinary mobile device users for data collection and processing. This model has shown great potential for applications in environmental monitoring, traffic management, public safety, and other areas. However, the enthusiasm of participants and the quality of uploaded data directly affect the reliability and practical value of the sensing results. Therefore, the design of incentive mechanisms has become a core issue in driving the healthy operation of MCS. The existing research, when optimizing long-term utility rewards for participants, has often failed to fully consider dynamic changes in trustworthiness. It has typically relied on historical data from a single point in time, overlooking the long-term dependencies in the time series, which results in suboptimal decision-making and limits the overall efficiency and fairness of sensing tasks. To address this issue, a credibility-aware incentive mechanism based on long short-term memory and proximal policy optimization (CIM-LP) is proposed. The mechanism employs a Markov decision process (MDP) model to describe the decision-making process of the participants. Without access to global information, an incentive model combining long short-term memory (LSTM) networks and proximal policy optimization (PPO), collectively referred to as LSTM-PPO, is utilized to formulate the most reasonable and effective sensing duration strategy for each participant, aiming to maximize the utility reward. After task completion, the participants’ credibility is dynamically updated by evaluating the quality of the uploaded data, which then adjusts their utility rewards for the next phase. Simulation results based on real datasets show that compared with several existing incentive algorithms, the CIM-LP mechanism increases the average utility of the participants by 6.56% to 112.76% and the task completion rate by 16.25% to 128.71%, demonstrating its significant advantages in improving data quality and task completion efficiency. Full article

As global temperatures continue to rise, surpassing the +2.5 °C threshold under current emissions scenarios, the urgency for sustainable, effective carbon management strategies has intensified. Geological carbon storage (GCS) has been explored as a potential mitigation tool; however, its large-scale feasibility remains highly uncertain due to concerns over storage permanence, leakage risks, and economic viability. This study proposes three advanced deep learning models—DeepDropNet, GateSeqNet, and RecurChainNet—to predict the Residual Trapping Index (RTI) and Solubility Trapping Index (STI) with enhanced accuracy and computational efficiency. Using a dataset of over 2000 high-fidelity simulation records, the models capture complex nonlinear relationships between key reservoir properties. Results indicate that GateSeqNet achieved the highest predictive accuracy, with an R² of 0.95 for RTI and 0.93 for STI, outperforming both DeepDropNet and RecurChainNet. Ablation tests reveal that excluding post injection and injection rate significantly reduced model performance, decreasing R² by up to 90% in RTI predictions. The proposed models provide a computationally efficient alternative to traditional numerical simulations, which makes them viable for real-time CO₂ sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO₂ storage performance in geological formations and for achieving global sustainability goals. Full article

Type 2 diabetes (T2D) is a chronic metabolic disorder requiring effective management to avoid complications. Metformin is a first-line drug agent and is routinely prescribed for the control of glycemia, but its underlying dynamics are complicated and not fully quantified. This paper formulates a control-oriented and interpretable mathematical model that integrates metformin dynamics into a classic beta-cell–insulin–glucose (BIG) regulation system. The paper’s applicability to theoretical and clinical settings is enhanced by rigorous mathematical analysis, which guarantees the model is globally bounded, well-posed, and biologically meaningful. One of the key features of the study is its global stability analysis using Lyapunov functions, which demonstrates the asymptotic stability of critical equilibrium points under realistic physiological constraints. These findings support the predictive reliability of the model in explaining long-term glycemic regulation. Bifurcation analysis also clarifies the dynamic interplay between glucose production and utilization by identifying parameter thresholds that signify transitions between homeostasis and pathological states. Residual analysis, which detects Gaussian-distributed errors, underlines the robustness of the fitting process and suggests possible refinements by including temporal effects. Sensitivity analysis highlights the predominant effect of the initial dose of metformin on long-term glucose regulation and provides practical guidance for optimizing individual treatment. Furthermore, changing the two considered metformin parameters from their optimal values—altering the dose by ±50% and the decay rate by ±20%—demonstrates the flexibility of the model in simulating glycemic responses, confirming its adaptability and its potential for optimizing personalized treatment strategies. Full article

Displacement back-analysis is a crucial approach to enhance the effectiveness of landslide monitoring data. To improve the computational efficiency and reliability of large-scale three-dimensional landslide displacement inversion, this study develops a novel Landslide Displacement Intelligent Dynamic Inversion Framework (LDIDIF), which integrates the Bayesian displacement back-analysis (BBA) approach, a Long Short-Term Memory (LSTM) surrogate model, and the RANdom SAmple Consensus (RANSAC) algorithm. Specifically, BBA is employed to dynamically calibrate geotechnical parameters with uncertainty, the LSTM model replaces traditional numerical simulations to reduce computational cost, and RANSAC filters inlier observations to enhance the robustness of the inversion model. A case study of the Dawanzi GNSS landslide is conducted. Results show that the LSTM surrogate model achieves prediction errors below 2 mm and enhances computational efficiency by approximately 50,000 times. The RANSAC algorithm effectively identifies and removes GNSS outliers. Notably, LDIDIF significantly reduces the uncertainty of shear strength parameters within the slip zone, yielding a calibrated displacement precision better than 10 mm. The calibrated model reveals that the landslide is buoyancy-driven and that frontal failure may trigger progressive deformation in the rear slope. These findings offer valuable insights for landslide early warning and reservoir operation planning in the Dawanzi area. Full article

As the importance of both indoor air quality (IAQ) and energy efficiency grows in residential buildings, the application of air filters in energy recovery ventilators has become essential. However, high-efficiency filters such as MERV 12 inevitably increase the pressure drop, adversely affecting the airflow, fan energy use, and heat exchange balance. This study quantitatively investigates how different levels of filter resistance—from clean conditions to 200% dust loading—affect system airflow, static pressure, exhaust air transfer, and power consumption. A standardized dust loading procedure was adopted to simulate long-term use conditions. The results show a 37% reduction in net supply airflow under heavily clogged filters, while the unit exhaust air transfer ratio increased from 7.2% to 17.7%, exceeding compliance limits. Surprisingly, electrical energy consumption decreased as the fan load dropped with the airflow. Despite an increase in the apparent heat exchange efficiency, this gain was driven by return air recirculation rather than true thermal effectiveness. These findings highlight the need for filter performance-based ERV certification and operational strategies that balance IAQ, energy use, and system compliance. Full article

Objectives: To study the effects of internal and marginal polymerization shrinkage stress and distribution in different resin composite class III dental restorations in relation to the restorative technique using numerical finite element analysis (FEA). Methods: A 3D model of a human hemi-maxilla with a sound maxillary central incisor were created. Four class III distal cavities were shaped and differently restored. Four groups of resin composite combinations were analyzed: group C (three increments of conventional composite); group B (two increments of bulk-fill composite); group FC (flowable base + three increments of conventional composite); and group FB (flowable bulk-fill base + two increments of conventional composite). The resulting four models were exported to FEA software for static structural analysis. Polymerization shrinkage was simulated using thermal analogy, and stress distribution was analyzed using the Maximum Principal Stress criterion at the marginal and internal cavity interfaces. Results: Group FC showed the highest stress at the level in the proximal region (9.05 MPa), while group FB showed the lowest (4.48 MPa). FB also exhibited the highest internal dentin stress, indicating potential risks for long-term bond degradation. In the cavo-surface incisal angle, the average peak stress across all groups was 3.76 MPa. At the cervical cavo-surface angle, stress values were 3.3 MPa (C), ~3.36 MPa (B), 3.41 MPa (FC), and 3.27 MPa (FB). Conclusions: Restorative technique did not significantly influence marginal stress distribution in class III composite restorations. However, the bevel area at the cervical margin showed the highest concentration of shrinkage stress. Full article

Autonomous surface vehicles (ASVs) have been widely applied in ocean engineering due to their small size, low cost, and high mobility. However, more relevant encirclement control methods with many-to-one are simple and do not consider the system dynamics. This article proposes a cooperative encirclement control method for ASVs against multiple targets based on multi-agent reinforcement learning. Firstly, a dynamic target allocation algorithm is designed based on location information of both vehicles and targets, enabling vehicles to select encirclement targets in real-time according to relative distances. Subsequently, the whole encirclement process is divided into multiple stages, and a multi-stage reward function is developed based on curriculum learning to guide ASVs in completing encirclement tasks progressively, from simpler to more complex scenarios. Then, the actor and critic networks incorporating long short-term memory are constructed, respectively, and a multi-agent soft actor-critic reinforcement learning algorithm is employed to train ASVs, enhancing cooperative target encirclement maneuvers. Finally, the effectiveness and superiority of the proposed method is validated through a six-on-two encirclement simulation. Full article

South Africa’s transition to a renewable-powered grid faces critical challenges due to the inherent variability of wind and solar generation as well as the need for economically viable and reliable dispatch strategies. This study proposes a scenario-based stochastic optimization framework that integrates machine learning forecasting and uncertainty modeling to enhance operational decision making. A hybrid Long Short-Term Memory–XGBoost model is employed to forecast wind, photovoltaic (PV) power, concentrated solar power (CSP), and electricity demand, with Monte Carlo dropout and quantile regression used for uncertainty quantification. Scenarios are generated using appropriate probability distributions and are reduced via Temporal-Aware K-Means Scenario Reduction for tractability. A two-stage stochastic program then optimizes power dispatch under uncertainty, benchmarked against Deterministic, Rule-Based, and Perfect Information models. Simulation results over 7 days using five years of real-world South African energy data show that the stochastic model strikes a favorable balance between cost and reliability. It incurs a total system cost of ZAR 1.748 billion, with 1625 MWh of load shedding and 1283 MWh of curtailment, significantly outperforming the deterministic model (ZAR 1.763 billion; 3538 MWh load shedding; 59 MWh curtailment) and the rule-based model (ZAR 1.760 billion, 1.809 MWh load shedding; 1475 MWh curtailment). The proposed stochastic framework demonstrates strong potential for improving renewable integration, reducing system penalties, and enhancing grid resilience in the face of forecast uncertainty. Full article

The integration of renewable energy resources (RES) into microgrids (MGs) poses significant challenges due to the intermittent nature of generation and the increasing complexity of multi-energy scheduling. To enhance operational flexibility and reliability, this paper proposes an intelligent energy management system (EMS) for MGs incorporating a hybrid hydrogen-battery energy storage system (HHB-ESS). The system model jointly considers the complementary characteristics of short-term and long-term storage technologies. Three conflicting objectives are defined: economic cost (EC), system response stability, and battery life loss (BLO). To address the challenges of multi-objective trade-offs and heterogeneous storage coordination, a novel deep-reinforcement-learning (DRL) algorithm, termed MOATD3, is developed based on a dynamic reward adjustment mechanism (DRAM). Simulation results under various operational scenarios demonstrate that the proposed method significantly outperforms baseline methods, achieving a maximum improvement of 31.4% in SRS and a reduction of 46.7% in BLO. Full article

This paper proposes a Hybrid Adaptive Learning-Based Control (HALC) algorithm for voltage regulation in grid-forming inverters (GFIs), addressing the challenges posed by voltage sags and swells. The HALC algorithm integrates two key control strategies: Model Predictive Control (MPC) for short-term optimization, and reinforcement learning (RL) for long-term self-improvement for immediate response to grid disturbances. MPC is modeled to predict and adjust control actions based on short-term voltage fluctuations while RL continuously refines the inverter’s response by learning from historical grid conditions, enhancing overall system stability and resilience. The proposed multi-stage control framework is modeled based on a mathematical representation using a control feedback model with dynamic optimal control. To enhance voltage stability, Lyapunov is used to operate across different time scales: milliseconds for immediate response, seconds for short-term optimization, and minutes to hours for long-term learning. The HALC framework offers a scalable solution for dynamically improving voltage regulation, reducing power losses, and optimizing grid resilience over time. Simulation is conducted and the results are compared with other existing methods. Full article