Search Results (10,094)

Interim periods between peak and off-peak operations in urban rail transit networks often suffer from mismatched headways across lines, which increases passenger transfer waiting and operating costs. This paper proposes a passenger-oriented timetable synchronization method for network-wide interim period train service. In this study, based on the AFC data, passengers are assigned to the shortest travel time paths, and passenger transfer flows are linked to connecting train pairs by consideration of the maximum acceptable waiting time. As a result, the transfer waiting time is accurately calculated by matching passengers’ platform arrival times with the departures of feasible connecting trains. A mixed integer nonlinear programming model then jointly optimizes departure headways at each line’s first station, arrival and departure times at transfer stations, subject to safety headways and time bounds. The objective minimizes total cost, combining transfer waiting time cost and train operating cost (depreciation and distance-related cost). A simulated-annealing-based genetic algorithm (SA-GA) is designed to solve the NP-hard problem. A case study on the Nanjing rail transit network from 6:30 to 7:30 reduces total cost by 6.88%, including 3.77% lower transfer waiting time cost and 14.49% lower operating cost, and shows stable results under typical transfer demand fluctuations. Full article

The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated scheduling of installation vessels (IVs) and transport vessels (TVs). First, a Mixed-Integer Linear Programming (MILP) model is established to describe the operational constraints, which is then decomposed into two interrelated sub-problems: vessel path planning and scheduling optimization. For path planning, the problem is modeled as a Multiple Traveling Salesman Problem (MTSP) to ensure balanced fleet workloads. This stage is solved via a tailored three-stage heuristic combining balanced sweep clustering and penalized local search. For scheduling optimization, a hybrid Earliest Deadline First (EDF)-Simulated Annealing (SA) strategy is employed, where EDF generates a strictly feasible baseline to warm-start the SA optimization. Furthermore, a stochastic optimization approach integrates historical meteorological data to ensure schedule robustness against weather uncertainty. The validity of the framework is supported by two real-world OWF cases, which demonstrate total cost reductions of 15.44% and 13.20%, respectively, under stochastic weather conditions. These results demonstrate its effectiveness in solving high-constraint offshore engineering problems. Full article

The use of lecithin as an emulsifier in food supplements has increased in recent years. However, successful formation of liposomes or micelles requires an appropriate mixture of phospholipids in lecithin. To evaluate the emulsification properties of lecithin for food supplements, a reliable analytical procedure for characterizing phospholipids is necessary. A liquid chromatography–mass spectrometry method was developed to identify phospholipids in lecithin without standard reference materials. For efficient separation of phospholipids before mass spectrometric analysis, a reverse-phase high-performance liquid chromatography method was optimized using a Waters XBridge Protein BEH C4 column. The optimized chromatographic method demonstrated good linearity and precision. Molecular ions were detected in full scan mode to determine accurate mass-to-charge ratios for individual peaks in the chromatogram. A custom Python program was then used to generate a list of possible phospholipid species for each peak based on the measured mass-to-charge ratios. Tandem mass spectrometry was performed to confirm the identity of specific phospholipids by comparing experimental fragmentation patterns with theoretical predictions. Identification of the phospholipids was also confirmed with four commercially available standard reference compounds, demonstrating the reliability of the proposed approach. The developed method offers a practical and cost-effective strategy for identifying phospholipids in complex matrices, especially when standard reference compounds are unavailable. Additionally, it enables targeted selection of standard compounds for future quantitative analyses, making it a valuable tool for comprehensive lipid profiling. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

This article presents a hybrid optimization model designed to determine the optimal location and operation of capacitor banks in medium-voltage distribution networks, aiming to reduce energy losses and enhance the system’s economic efficiency. The use of reactive power compensation through fixed-step capacitor banks is highlighted as an effective and cost-efficient solution; however, their optimal placement and sizing pose a mixed-integer nonlinear programming optimization challenge of a combinatorial nature. To address this issue, a multi-objective optimization methodology based on the Sine Cosine Algorithm (SCA) is proposed to identify the ideal location and capacity of capacitor banks within distribution networks. This model simultaneously focuses on minimizing technical losses while reducing both investment and operational costs, thereby producing a Pareto front that facilitates the analysis of trade-offs between technical performance and economic viability. The methodology is validated through comprehensive testing on the 33- and 69-bus reference systems. The results demonstrate that the proposed SCA-based approach is computationally efficient, easy to implement, and capable of effectively exploring the search space to identify high-quality Pareto-optimal solutions. These characteristics render the approach a valuable tool for the planning and operation of efficient and resilient distribution networks. Full article

The article explores the current challenges and prospects for the electrification of the bus fleet in urban passenger transport, with a particular focus on the municipal operator Municipal Transport Ruse EAD. The study is motivated by the growing importance of sustainable mobility and the European Union’s policy framework aimed at decarbonization of urban transport systems. A mixed-integer linear programming (MILP) model is developed to optimize the investment and operational strategies for the gradual replacement of diesel buses with electric ones, taking into account capital expenditures, operational costs, charging infrastructure, and environmental benefits. Scenario analysis is employed to compare six different pathways of fleet electrification, ranging from partial to full transition within a defined planning horizon. The results highlight significant trade-offs between financial feasibility and ecological impact, illustrating that an accelerated electrification strategy yields the largest emission reductions but requires substantial upfront investment. Conversely, gradual transition scenarios demonstrate better budget alignment but achieve lower environmental benefits. The discussion emphasizes the practical applicability of the model for municipal decision-makers, offering a tool for strategic planning under economic and ecological constraints. The paper concludes that sustainable electrification of municipal bus fleets requires a balanced approach that aligns environmental objectives with financial and operational capacities. Full article

This pilot study examined the feasibility, usability, and physiological effects of a high-intensity exercise program delivered through immersive virtual reality (IVR) in adults with Down syndrome (DS). Twenty participants (mean age: 29.85 ± 9.37 years) completed a 12-week intervention using the FitXR exergame on Meta Quest 3, with two sessions per week. Usability, safety, and personal experiences were assessed via the System Usability Scale (SUS), Simulator Sickness Questionnaire (SSQ), and Game Experience Questionnaire (GEQ), while body composition and strength were measured using bioelectrical impedance analysis and standardized tests (handgrip dynamometry, Five Sit-to-Stand Test). Results indicated excellent usability (SUS: 92.88–95.03/100), minimal cybersickness (SSQ: 2.12 → 1.98/48), and high adherence (90%). Positive experiences increased significantly, with no negative experiences reported. Lower-limb strength has been considered as a primary outcome, which has shown to improve significantly (p = 0.018; Cohen’s d = 0.89), whereas upper-limb strength and body composition changes were minimal. These findings suggest that IVR-based exercise is a safe, engaging, and feasible strategy for promoting physical activity and enhancing functional strength in adults with DS. Further controlled trials with longer duration and nutritional strategies are warranted to optimize body composition outcomes. Full article

Background: Multiple sclerosis (MS) involves complex physical, cognitive and behavioral challenges that collectively affect quality of life. Integrating lifestyle components such as sleep optimization, dietary behaviors, stress management, and self-management strategies into rehabilitation may enhance outcomes beyond traditional approaches. This scoping review aimed to map rehabilitation interventions that combine psychomotor, cognitive, lifestyle-focused, or multimodal elements and assess quality of life in adults with MS. Methods: This scoping review was conducted in accordance with the PRISMA-ScR guidelines, which guided the identification, screening, and selection of studies. Screening and data extraction were conducted independently by two reviewers. From 135 records, nine primary studies and four secondary evidence sources were included. Results: Most studies involved adults with mild-to-moderate disability and predominantly relapsing–remitting multiple sclerosis. Physical or motor rehabilitation interventions were examined in five studies, while two studies evaluated multimodal rehabilitation programs, one study focused on cognitive rehabilitation, and one study investigated lifestyle-oriented or self-management-integrated approaches. Quality of life was assessed in all included studies, with improvements reported across multiple domains. Fatigue-related outcomes were reported in four studies, sleep-related outcomes in three studies, and digitally delivered or hybrid rehabilitation interventions were implemented in three studies, indicating an emerging trend toward technology-supported rehabilitation approaches. Conclusions: Contemporary MS rehabilitation is moving toward multidimensional, holistic models that integrate lifestyle components. Standardized outcomes, inclusion of more diverse MS phenotypes, and rigorous evaluation of integrated frameworks are required to strengthen the evidence base and inform clinical practice. Full article

Besides the purification process in the wastewater treatment plant that includes mechanical, biological, and chemical approaches, analysis of hydraulic behavior is also fundamental. This means developing a digital replica of the physical process by simulating the hydraulic parameters. Studying fluid behavior in the plant enables process optimization, improves plant behavior, prevents equipment malfunctions, and more. This paper focuses on defining the concept of a wastewater treatment plant prototype, simulating it, and identifying the available and most suitable software that enables efficient process simulation and validation. The hydraulic parameters, as per the literature review and the proposed concept, that will be simulated are pressure, flow, pressure drop, and water level. MATLAB/Simulink and Python programming languages are considered the most suitable software/programming languages for hydraulic parameters simulation. Full article

Background/Objectives: People suffering from mental illnesses are more likely to experience adverse social and health outcomes. Various interventions have been shown to help people with mental illness achieve better results in terms of symptom reduction, functional status, and quality of life. Psychiatric rehabilitation interventions integrate evidence-based practices, promising approaches, and emerging methods that can be effectively implemented to enhance health outcomes in this population. This study aims to examine whether the rehabilitative treatment provided to a group of patients with mental illness leads to improvements in health outcomes and psychiatric symptomatology. Methods: This study employed a retrospective quasi-experimental design. Data were collected between 2023 and 2025 within the Partial Hospitalization Program of the Psychiatry and Mental Health Service of Soria (Spain). The sample consisted of 58 participants who received rehabilitative treatment in this setting. Data were collected at the time of patients’ admission and at discharge. Gender, age, psychiatric diagnosis according to ICD-10, and the average length of stay in the rehabilitation program were assessed. The questionnaires administered were psychometrically validated scales related to heteroaggressiveness, perceived quality of life, global functioning, attitudes toward medication, and the risk of suicide. Results: A significant improvement was observed in the Global Assessment of Functioning (GAF) Scale (t = −7.1, p < 0.001), with mean scores increasing from 42.17 at admission to 69.13 at discharge. Additionally, reductions in suicidal risk and hetero-aggressive behavior were noted, alongside improvements in quality of life and treatment adherence. Conclusions: The findings highlight the effectiveness of implementing activities and programs focused on psychiatric rehabilitation processes to promote positive health outcomes. Future research directions and practical implications are discussed to support the continued development and optimization of psychiatric rehabilitation programs. Full article

Engineering laboratory courses are essential for developing conceptual understanding and practical skills; however, the time students spend assembling prototypes and troubleshooting wiring issues often reduces opportunities for analysis, programming, and reflective learning. To address this limitation, this study designed and evaluated an integrated STM32-based educational laboratory that consolidates the main peripherals required in a microcontroller course into a single Printed Circuit Board (PCB) platform. A quasi-experimental intervention was implemented with 40 engineering students divided into a control group using traditional STM32 Blue Pill and breadboard connections and an experimental group using the integrated platform. Throughout ten laboratory sessions, data were collected through pre- and post-tests, laboratory logs, and the Motivated Strategies for Learning Questionnaire Short Form (MSLQ-SF). Results showed that the experimental group achieved a Hake normalized learning gain of 40.09% compared with 16.22% in the control group, also showing that it completed the sessions an average of 27 min faster and facilitated a substantial reduction in hardware- and connection-related errors. Significant improvements were also observed in metacognitive and improved motivational and self-regulated learning scores. Overall, the findings indicate that reducing operational barriers in laboratory work enhances both cognitive and motivational learning processes, supporting the adoption of integrated educational hardware to optimize learning outcomes in engineering laboratory courses. Full article

In this work, we study the multiresource flexible job-shop scheduling problem (MRFJSSP), which relaxes the standard “simultaneous occupation” policy described in the literature. This policy implies that a job operation starts only when all its assigned necessary resources are available and releases them simultaneously. In contrast, our approach assumes that a job operation begins simultaneously across all assigned resources, although these resources may not be occupied for the same duration. This variant (which we will call “early resource release”) was first formally proposed in the scheduling literature more than twenty years ago, but to the best of our knowledge, it has not been empirically tested. Thus, to tackle this problem, we formulate a constraint programming (CP) model adopting a multi-mode resource-constrained project scheduling problem (MMRCPSP) representation. We tested our approach on 65 instances of the MRFJSSP where the precedence relationships between operations of a given job follow a linear order. We prove optimality in 44 instances with an average optimality gap of 9.37%. Additionally, we contributed eight new lower bounds for the same set of instances in the literature when considering the simultaneous occupation policy. Full article

Cancer stem cells (CSCs) represent a small but highly resilient tumor subpopulation responsible for sustained growth, metastasis, therapeutic resistance, and recurrence. Their survival is supported by aberrant activation of developmental and inflammatory pathways, including Wnt/β-catenin, Notch, Hedgehog, PI3K/Akt/mTOR, STAT3, and NF-κB, as well as epithelial–mesenchymal transition (EMT) programs and niche-driven cues. Increasing evidence shows that phytochemicals, naturally occurring bioactive compounds from medicinal plants, can disrupt these networks through multi-targeted mechanisms. This review synthesizes current findings on prominent phytochemicals such as curcumin, sulforaphane, resveratrol, EGCG, genistein, quercetin, parthenolide, berberine, and withaferin A. Collectively, these compounds suppress CSC self-renewal, reduce sphere-forming capacity, diminish ALDH⁺ and CD44⁺/CD24⁻ fractions, reverse EMT features, and interfere with key transcriptional regulators that maintain stemness. Many phytochemicals also sensitize CSCs to chemotherapeutic agents by downregulating drug-efflux transporters (e.g., ABCB1, ABCG2) and lowering survival thresholds, resulting in enhanced apoptosis and reduced tumor-initiating potential. This review further highlights the translational challenges associated with poor solubility, rapid metabolism, and limited bioavailability of free phytochemicals. Emerging nanotechnology-based delivery systems, including polymeric nanoparticles, lipid carriers, hybrid nanocapsules, and ligand-targeted formulations, show promise in improving stability, tumor accumulation, and CSC-specific targeting. These nanoformulations consistently enhance intracellular uptake and amplify anti-CSC effects in preclinical models. Overall, the consolidated evidence supports phytochemicals as potent modulators of CSC biology and underscores the need for optimized delivery strategies and evidence-based combination regimens to achieve meaningful clinical benefit. Full article

Uncertainty in optimization models often causes awkward properties in their deterministic equivalent formulations (DEFs), even for simple linear models. Chance-constrained programming is a reasonable tool for handling optimization problems with random parameters in objective functions and constraints, but it assumes that the distribution of these random parameters is known, and its DEF is often associated with the complicated computation of multiple integrals, hence impeding its extensive applications. In this paper, for optimization models with chance constraints, the historical data of random model parameters are first exploited to construct an adaptive approximate density function by incorporating piecewise linear interpolation into the well-known histogram method, so as to remove the assumption of a known distribution. Then, in view of this estimation, a novel confidence set only involving finitely many variables is constructed to depict all the potential distributions for the random parameters, and a computable reformulation of data-driven distributionally robust chance constraints is proposed. By virtue of such a confidence set, it is proven that the deterministic equivalent constraints are reformulated as several ordinary constraints in line with the principles of the distributionally robust optimization approach, without the need to solve complicated semi-definite programming problems, compute multiple integrals, or solve additional auxiliary optimization problems, as done in existing works. The proposed method is further validated by the solution of the stochastic multiperiod capacitated lot-sizing problem, and the numerical results demonstrate that: (1) The proposed method can significantly reduce the computational time needed to find a robust optimal production strategy compared with similar ones in the literature; (2) The optimal production strategy provided by our method can maintain moderate conservatism, i.e., it has the ability to achieve a better trade-off between cost-effectiveness and robustness than existing methods. Full article

This study evaluates the potential of 3D-printed acrylonitrile butadiene styrene (ABS) and carbon fiber (CF) as sustainable alternatives to steel reinforcement in cement-based materials. The experimental program analyzed the compressive strength of cement pastes and concrete cylinders incorporating 3D-printed ABS and CF elements. Unreinforced cement pastes exhibited higher compressive strength than reinforced pastes, indicating limited reinforcement–matrix interaction. In concrete cylinders, ABS reinforcement increased compressive strength by approximately 3 to 7 MPa compared to steel, whereas CF reinforcement showed variable performance and did not consistently surpass the control specimens. ANOVA and Tukey tests confirmed the statistical significance of the results. The anisotropic response of ABS and CF, inherent to layer-by-layer deposition, was identified as a major factor influencing structural performance, particularly with respect to reinforcement orientation. The results indicate that ABS presents potential as an environmentally favourable alternative to steel in selected applications, while CF requires further optimization for compression-oriented use. Continued research is recommended to evaluate long-term durability, environmental resistance, and reinforcement–matrix compatibility in order to advance the implementation of polymer-based, additively manufactured reinforcements in construction materials. Full article