Search Results (192)

During the transition to adulthood, college students undergo profound personal growth and identity exploration. Spirituality, which is defined as the individual pursuit of meaning, purpose, and connection with others, oneself, and the sacred or transcendent, plays a significant role in shaping well-being, relationships, and academic engagement, independent of organized religion. This qualitative study explores the spiritual aspirations of college students, offering insights into their diverse experiences and values. Participants included 113 ethnically and religiously diverse students from a southern United States urban university who completed an anonymous, open-ended questionnaire focused on spirituality in interpersonal relationships, education, and broader life domains. A thematic analysis identified recurring themes, including “peaceful or less stressed,” “sharing spiritual experiences,” and “being focused.” The findings suggest that the majority of students view spirituality as central to their lives, highlighting its role in their search for meaning, personal development, and a sense of connectedness. These results underscore spirituality as a pervasive influence on student well-being and identity, with implications for their academic and relational experiences. Full article

To address the problems of random noise interference, inadequate disturbance estimation and compensation, and the difficulty in controller parameter tuning in speed tracking control of high-speed trains, an improved Active Disturbance Rejection Control (ADRC) strategy combined with a Sobol-based Black Widow Optimization (SBWO) algorithm is proposed. An improved Tracking Differentiator (TD) is adopted by integrating a novel optimal control synthesis function with a phase compensator to suppress input noise and ensure a smooth transition process. A novel Extended State Observer (ESO) using a nonlinear saturation function is designed to improve the observation accuracy and decrease chattering. An enhanced Nonlinear State Error Feedback (NLSEF) law that incorporates an error integral and adaptive parameter update laws is developed to reduce steady-state error and achieve self-tuned proportional and derivative gains. A feedforward compensation term is added to provide real-time dynamic compensation for ESO estimation errors. Finally, an enhanced Black Widow Optimization (BWO) algorithm, which initializes its population with Sobol sequences to improve its global search capability, is employed for parameter optimization. The simulation results demonstrate that compared with the control methods based on Proportional–Integral–Derivative (PID) control and conventional ADRC, the proposed strategy achieves higher steady-state tracking accuracy, better adaptability to dynamic operating conditions, stronger anti-disturbance ability, and more precise stopping precision. Full article

This literature review examines the relationship between the number and type of deficiencies identified during Port State Control (PSC) inspections and a ship’s overall risk. The main objective is to synthesise the current academic evidence, detailing the analytical methodologies employed and highlighting key research contributions. The selection of literature has focused on peer-reviewed articles and relevant doctoral theses addressing detention risk prediction, accident risk and ship risk profiling. The findings indicate a consistent correlation between PSC deficiencies and ship risk, although the nature and strength of this correlation may vary depending on the type of risk considered and the specific deficiencies. A methodological evolution is observed in the field, from descriptive statistical analyses and regressions towards more complex predictive models, such as Machine Learning (ML) and Bayesian Networks (BNs). This transition reflects a search for greater accuracy in risk assessment, going beyond simple numerical correlation to improve the selection of ships for inspection. Multivariate statistical techniques, on the other hand, focus on the identification of risk patterns and the evaluation of the PSC system. The conclusions underline the importance of deficiencies as indicators of risk, the need for differentiated inspection approaches and the persistent challenges related to data quality and model interpretability. Full article

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. Full article

Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging and migration. The optimization algorithm (SWSO) makes use of these behaviors to boost the ability of exploration and exploitation in the optimization process. Unlike other birds, swallows are known to be so precise when performing fast directional alterations and making intricate aerial acrobatics during foraging. Moreover, the flight patterns of swallows are very efficient; they have extensive capabilities to transition between flapping and gliding with ease to save energy over long distances during migration. This allows instantaneous changes of wing shape variations to optimize performance in any number of flying conditions. The model used by the SWSO algorithm combines these biologically inspired flight dynamics into a new computational model that is aimed at enhancing search performance in rugged terrain. The design of the algorithm simulates the swallow’s social behavior and energy-saving behavior, converting it into exploration, exploitation, control mechanisms, and convergence control. In order to verify its effectiveness, (SWSO) is applied to many benchmark problems, such as unimodal, multimodal, fixed-dimension functions, and a benchmark CEC2019, which consists of some of the most widely used benchmark functions. Comparative tests are conducted against more than 30 metaheuristic algorithms that are regarded as state-of-the-art, developed so far, including PSO, MFO, WOA, GWO, and GA, among others. The measures of performance included best fitness, rate of convergence, robustness, and statistical significance. Moreover, the use of (SWSO) in solving real-life engineering design problems is used to prove (SWSO)’s practicality and generality. The results confirm that the proposed algorithm offers a competitive and reliable solution methodology, making it a valuable addition to the field of swarm-based optimization. Full article

Background: Freezing of gait (FoG) is one of the most debilitating motor symptoms in Parkinson’s disease (PD). It often leads to falls and reduces quality of life due to the risk of injury and loss of independence. Several types of wearable sensors have emerged as promising tools for the detection of FoG in clinical and real-life settings. Objective: The main objective of this systematic review was to critically evaluate the current usability of wearable sensor technologies for FoG detection in PD patients. The focus of the study is on sensor types, sensor combinations, placement on the body and the applications of such detection systems in a naturalistic environment. Methods: PubMed, IEEE Explore and ACM digital library were searched using a search string of Boolean operators that yielded 328 results, which were screened by title and abstract. After the screening process, 43 articles were included in the review. In addition to the year of publication, authorship and demographic data, sensor types and combinations, sensor locations, ON/OFF medication states of patients, gait tasks, performance metrics and algorithms used to process the data were extracted and analyzed. Results: The number of patients in the reviewed studies ranged from a single PD patient to 205 PD patients, and just over 65% of studies have solely focused on FoG + PD patients. The accelerometer was identified as the most frequently utilized wearable sensor, appearing in more than 90% of studies, often in combination with gyroscopes (25.5%) or gyroscopes and magnetometers (20.9%). The best overall sensor configuration reported was the accelerometer and gyroscope setup, achieving nearly 100% sensitivity and specificity for FoG detection. The most common sensor placement sites on the body were the waist, ankles, shanks and feet, but the current literature lacks the overall standardization of optimum sensor locations. Real-life context for FoG detection was the focus of only nine studies that reported promising results but much less consistent performance due to increased signal noise and unexpected patient activity. Conclusions: Current accelerometer-based FoG detection systems along with adaptive machine learning algorithms can reliably and consistently detect FoG in PD patients in controlled laboratory environments. The transition of detection systems towards a natural environment, however, remains a challenge to be explored. The development of standardized sensor placement guidelines along with robust and adaptive FoG detection systems that can maintain accuracy in a real-life environment would significantly improve the usefulness of these systems. Full article

The industrial sector is a major contributor to energy-related CO₂ emissions in Europe, making the transition to renewable energy solutions essential. Decarbonization strategies integrate renewable energy sources, power-to-heat technologies, and energy storage systems into existing production sites to enhance sustainability and flexibility. However, a key challenge lies in designing energy systems that remain robust under long-term operational uncertainties. Usually the design of each energy system component is discrete, as it is manufactured in a predetermined size. Classical state-of-the-art coupled design and operational optimization methods are based on continuous design variables, which might give sub-optimal solutions. This study overcomes this limitation by employing novel, computationally efficient robust quantum-classical discrete-design methods. Traditional approaches often optimize operations for a single year due to the computational limitations of operational optimization algorithms, leading to designs that lack robustness. By incorporating long-term operational uncertainties, this approach ensures that selected energy-system configurations minimize both CO₂ emissions and costs while maintaining resilience to variations in weather conditions and demand fluctuations. Robust discrete designs which consider operational uncertainties show 12% less global warming impact (GWI) with 27% higher total annualized cost (TAC) compared to designs based on operational optimization without uncertainty. A novel quantum-assisted non-dominated sorting genetic algorithm (QANSGA-II) shows accuracy up to 90%, which leads to 27% less computational effort than the NSGA-II algorithm. This novel method can help industries to search larger and more optimal robust discrete-design spaces for making decarbonization decisions. Full article

The global energy transition and increasingly rigorous legal regulations aimed at climate protection are driving the search for alternative energy sources, including renewable energy sources (RESs) and waste heat. However, the mismatch between supply and demand presents a significant challenge. Thermal energy storage (TES) technologies, particularly mobile thermal energy storage (M-TES), offer a potential solution to address this gap. M-TES can not only balance supply and demand but also facilitate the transportation of heat from the source to the recipient. This paper reviews the current state of M-TES technologies, focusing on their technology readiness level, key operating parameters, and advantages and disadvantages. It is found that M-TES can be based on sensible heat, latent heat, or thermochemical reactions, with the majority of research and projects centered around latent heat storage. Regarding the type of research, significant progress has been made at the laboratory and simulation levels, while real-world implementation remains limited, with few pilot projects and commercially available systems. Despite the limited number of real-world M-TES implementations, currently existing M-TES systems can store up to 5.4 MWh in temperatures ranging from 58 °C to as high as 1300 °C. These findings highlight the potential of the M-TES and offer data for technology selection, simultaneously indicating the research gaps and future research directions. Full article

The search for artificial blood substitutes that are suitable for safe transfusion in clinical conditions and in extreme situations has gained increasing interest during recent years. Most of the problems related to donor blood could be overcome with hemoglobin sub-micron particles (HbMPs) that are able to bind and deliver oxygen. On the other hand, the length of the circulation time of HbMPs in the bloodstream strongly depends on their surface properties and can be improved with biopolymer coatings. The redox potential of HbMPs and HbMPs coated with biopolymers using the layer-by-layer technique (LbL-HbMPs) is related to the energy required for electron transfer upon transition from an oxidized to a reduced state. It can be used as a measure of the stability of Hb against oxidation, which is directly connected with its function as an oxygen carrier. The redox potential of Hb, HbMPs, and LbL-HbMPs was determined by a spectroelectrochemical method utilizing the shift of the Soret peak of Hb upon oxidation/reduction of the iron in the heme. The obtained results showed a slight shift in the redox potential of both particle types of about 17 mV towards more negative values compared to the free Hb in the solution. It was demonstrated that the free Hb and the cross-linked Hb in HbMPs and LbL-HbMPs undergo transitions from an oxidized to a reduced state and vice versa several times without Hb destruction. The LbL coating does not affect the redox properties of HbMPs. This ability, as well as the proximity of the obtained redox potentials of Hb, HbMPs, and LbL-HbMPs, indicates that the eventual oxidation of HbMPs in the bloodstream is reversible; thus, HbMPs can be active as artificial oxygen carriers for a longer period of time. Full article

Liver cirrhosis is a chronic progressive disease marked by the transition from a compensated to a decompensated stage, associated with severe complications. Central to this progression is portal hypertension, which results from increased intrahepatic vascular resistance and endothelial dysfunction, as well as splanchnic vasodilation and an augmented circulatory state. Non-selective beta-blockers (NSBBs) remain the standard of care for portal hypertension, reducing portal pressure by lowering cardiac output via beta-1 receptor blockade and decreasing splanchnic blood flow through beta-2 receptor antagonism. However, clinical application of NSBBs is often hindered by adverse effects such as bradycardia, hypotension, and fatigue, alongside inconsistent efficacy in certain patient populations. Such limitations have driven the search for alternative therapeutic strategies and effective biomarkers for identifying non-responders. Beta-3 adrenergic receptor agonists have emerged as promising candidates, acting through distinct mechanisms, different from NSBBs. By stimulating nitric oxide release from endothelial cells, beta-3 agonists induce selective vasodilation without negatively impacting cardiac function, potentially overcoming the limitations of traditional therapies. This review discusses the molecular pathways of NSBBs, their clinical role and limitations, introduces potential novel biomarkers, and highlights the growing evidence supporting beta-3 receptor agonists as novel and targeted treatments for portal hypertension. Full article

Vanillin photoinduced deprotonation was evaluated and analyzed. Vibronic states and transitions were computationally investigated. Optimizations and vertical electron transitions in the gas phase and with the continuum solvation model were computed using the time-dependent density functional theory. Static absorption and emission (photostatic optical) spectra were statistically averaged over the excited instantaneous molecular conformers fluctuating on quantum–classical molecular dynamic trajectories. Photostatic optical spectra were generated using the hybrid quantum–classical molecular dynamics for explicit solvent models. Conical intersection searching and nonadiabatic molecular dynamics simulations defined potential energy surface propagations, intersections, dissipations, and dissociations. The procedure included mixed-reference spin–flip excitations for both procedures and trajectory surface hopping for photodynamics. Insignificant structural deformations vs. hydroxyl bond cleavage followed by deprotonation were demonstrated starting from different initial structural conditions, which included optimized, transition state, and several other important fluctuating configurations in various environments. Vanillin electronic structure changes were illustrated and analyzed at the key points on conical intersection and nonadiabatic molecular dynamics trajectories by investigating molecular orbital symmetry and electron density difference. The hydroxyl group decomposed on transition to a σ-molecular orbital localized on the elongated O–H bond. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Graphene quantum dots (GQDs) have strong potential in optoelectronics, particularly in LEDs, photodetectors, solar cells, and nanophotonics. While challenges remain in efficiency and scalability, advances in functionalization and hybrid material integration could soon make them commercially viable for next-generation optoelectronic devices. In this work, we assess the stability of various epoxy positions and their impact on the electronic and optical properties of GQDs. The oxygen binding energies and the potential barrier heights at different positions of epoxy groups at the edges and in the core of the GQD were estimated. The effect of possible transformations of epoxy groups into other edge configurations on the structural and optical properties of GQDs was evaluated. The results demonstrate that the functionalization of the GQD surface and edges with an epoxy groups at varying binding sites can result in substantial modification of the electronic structure and absorption properties of the GQDs. The prospects of low temperature annealing for controlling optical properties of epoxidized GQDs were discussed. The present computational work offers atomistic insights that can facilitate the rational design of optoelectronic systems based on GQD materials. Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article