Optimal Schedule Research Articles

Blazing a trail for the clinical use of rapamycin as a geroprotecTOR.

Treatment with rapamycin, an inhibitor of the mechanistic Target Of Rapamycin Complex One (mTORC1) protein kinase, has been repeatedly demonstrated to extend lifespan and prevent or delay age-related diseases in diverse model systems. Concerns over the risk of potentially serious side effects in humans, including immunosuppression and metabolic disruptions, have cautiously limited the translation of rapamycin and its analogs as a treatment for aging associated conditions. During the last decade, we and others have developed a working model that suggests that while inhibition of mTORC1 promotes healthy aging, many of the negative side effects of rapamycin are associated with "off-target" inhibition of a second mTOR complex, mTORC2. Differences in the kinetics and molecular mechanisms by which rapamycin inhibits mTORC1 and mTORC2 suggest that a therapeutic window for rapamycin could be exploited using intermittent dosing schedules or alternative rapalogs that may enable more selective inhibition of mTORC1. However, the optimal dosing schedules and the long-term efficacy of such interventions in humans are unknown. Here, we highlight ongoing or upcoming clinical trials that will address outstanding questions regarding the safety, pharmacokinetics, pharmacodynamics, and efficacy of rapamycin and rapalogs on several clinically oriented outcomes. Results from these early phase studies will help guide the design of phase 3 clinical trials to determine whether rapamycin can be used safely to inhibit mTORC1 for the treatment and prevention of age-related diseases in humans.

Scheduling independent tasks on multiple cloud-assisted edge servers with energy constraint

In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first paper in the literature which optimizes the makespan of task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers.

Bicriteria fabrication scheduling of two-component jobs on a single machine

This paper studies the bicriteria problem of non-preemptively scheduling n jobs, each of which is associated with a due date and comprises a standard and a specific component, on a single fabrication machine to minimize makespan and maximum lateness simultaneously. The specific components are processed individually and the standard components are grouped into batches for processing. A setup time is required before each batch of standard components is processed. A standard component is available (i.e., ready for delivery to the next production stage) only when the batch it belongs to is totally completed, whereas a specific component is available on completion of its processing. The completion time of a job is defined as the moment when both its two components have been processed and are available. An O(n2logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2\\log n)$$\\end{document}-time algorithm with linear memory requirements is presented which can generate all Pareto optimal points and find a corresponding Pareto optimal schedule for each Pareto optimal point.

Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory

Spaced repetition, namely, learners review items in a given schedule, has been proven powerful for memorization and practice of skills. Most current spaced repetition methods focus on either predicting student recall or designing an optimal review schedule, thus omitting the integrity of the spaced repetition system. In this work, we propose a novel spaced repetition schedule framework by capturing the dynamics of memory, which alternates memory prediction and schedule optimization to improve the efficiency of learners' reviews. Firstly, the framework collects logs from students' reviews and builds memory models with Markov property to capture the dynamics of memory. Then, the spaced repetition optimization is transformed into a stochastic shortest problem and solved via the value iteration method. We also construct a new benchmark dataset for spaced repetition, which is the first to contain time-series information during learners' memorization. Experimental results on the collected data from the real world and the simulated environment demonstrate that the proposed approach reduces 64% error and 17% cost in predicting recall rates and optimizing schedules compared to several baselines. We have publicly released the dataset containing 220 million rows and codes used in this paper at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/maimemo/SSP-MMC-Plus</uri> .

Deep Reinforcement Learning-Based Rescue Resource Distribution Scheduling of Storm Surge Inundation Emergency Logistics

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

Development of a method for clusterization of the dissipative structures

The dissipative structure (DS) may follow irreversible processes, which occur in the open thermodynamic systems passing and transforming a sufficiently intensive flow of energy. DS can be detected as certain patterns of spontaneous organization in space and time. We consider the DS, which transposes during irreversible ground movement due to landslide or ground pressure manifestation. However, DSs may hide under stochastic noise that impedes their detection. We developed a new method for revealing of DSs in such a stochastic environment using a combination of k-mean clustering, Voronyi tessellation, and the optimal schedule of ground movement monitoring. The developed method has been successfully tested in the case of the irreversible ground movement in the vicinity of a longwall face.

Backpropagation neural network based adaptive load scheduling method in an isolated power system

This work introduces an efficient load scheduling method for handling the day-to-day power supply needs. At peak load times, due to its instabilitythe power generation system fails and as a measure, the load shedding process is followed. The presented method overcomes this problem by scheduling the load based on necessity. For this load scheduling is handled with an artificial neural network (ANN). For the training purpose the backpropagation (BP) algorithm is used. The whole load essential is the input of the neural network (NN). The power generation of all resources and power losses at the instant of transmission is the NN output. The optimum scheduling of different power sources is important when considering all the available sources. Load scheduling shares the feasibility of entire load and losses. It is well-known as optimal scheduling if the constraints such as availability of power, load requirement, cost and power losses are considered. Training the system using a large number of parameters would be a difficult task. So, finest number of communally independent inputs is selected. The presented method aims to lower the power generation expenditure and formulate the power available on demand without alteration. The network is designed using MATLAB.

Proliferation Saturation Index to Simulate Adaptive Radiation Fractionation in HPV-Associated Oropharyngeal Cancer

In current clinical practice for fractionated radiotherapy (RT) patients receive a fixed fractionation protocol throughout radiation course. Radiation oncology has not yet moved toward adaptive fractionation to change RT fractionation based on individual patients' response dynamics. We have previously introduced a novel mathematical and imaging biomarker, proliferation saturation index (PSI), to simulate and evaluate tumor volume dynamics during therapy. Robust tumor volume reduction was associated with lower PSI, and PSI has been utilized to select fractionation schedules for the first mathematical model informed clinical trial. Here we simulate the possibility of adapting the selection of RT fractionation on a week-by-week basis by utilizing a dynamic weekly estimation of PSI. We generated a cohort of in silico patients by sampling from distributions of PSI, radiosensitivity, and growth rate parameters. These distributions were by pre-treatment and on-treatment tumor volume dynamics from a prospective clinical trial (NCT03656133), where 49 HPV+ OPC patients were treated with either 2 Gy daily fractions up to a total of 70 Gy if PS<0.75, or hyperfractionation at 1.2 Gy b.i.d. up to a total of 66 Gy for patients with PSI ≥ 0.75. We then simulated standard (2 Gy q.d.) and hyperfractionated (1.2 Gy b.i.d) for 1-8 weeks of RT to build a library of response dynamics for the different parameter combinations and treatment durations. This library was then used to estimate optimal adaptive fractionation schedules for the in silico patients that maximized tumor volume reduction and minimized total RT dose. We found that PSI evolved towards lower values (i.e., higher proliferative fractions) over the fractionation RT treatment course. For patients that began treatment with standard fractionation, no adaption was found to be superior to uniform fractionation. For selected patients who began treatment with hyperfractionation, however, the optimal fractionation schedule included a period of upfront hyperfractionated RT followed by a switch to standard fractionation at different times during their treatment course. The results of this study suggest that in addition to selecting the best initial fractionation schedule, a novel approach to personalize RT for OPC patients may include adapting the fractionation based on early treatment response dynamics. These results motivate validation in prospective clinical trials.

Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis

The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.

Optimizing aircrew recovery considering long connections: A column generation based approach

Severe weather and air traffic flow control can often cause flight delays and disrupt the crew schedules, resulting in a too-short or a too-long connection between flights. Short connections need to be adjusted to ensure the airline’s smooth operations, while the long connection whose duration is only a bit lower than the threshold enabling the crew to leave the flying zone for a rest at airport terminals can be quite inefficient, as the crew’s ground standby is viewed as working all the time without executing any flight which adds to the shortage of crew resources. Thus, we propose a method of delaying flights to the point enabling crews to leave for a rest, which helps save the available working hours. The difficulty lies in the trade-off between the additional swap or delay cost in creating long connections and the possible global cost decrease due to savings in available working hours brought by long connections, which calls for optimization. In this paper, we introduce the crew recovery problem for one day of operation taking long connections into account. The problem is mathematically formulated as an arc-based integer programming model and a set partitioning model in order to determine the optimal flight recovery schedule and the crew reassignment. A column generation based solution approach is developed and the sub-problems consisting of the resource-constrained shortest path problem are solved by a labeling algorithm. Computational experiments based on real-world data from a major Chinese airline confirm the effectiveness and efficiency of the solution approaches. It is also demonstrated the save of available working hours resulting from adaptively creating long connections would decrease the recovery cost by about 18.87%–74.88%.

Mathematical analysis identifies the optimal treatment strategy for epidermal growth factor receptor-mutated non-small cell lung cancer.

In Asians, more than half of non-small cell lung cancers (NSCLC) are induced by epidermal growth factor receptor (EGFR) mutations. Although patients carrying EGFR driver mutations display a good initial response to EGFR-Tyrosine Kinase Inhibitors (EGFR-TKIs), additional mutations provoke drug resistance. Hence, predicting tumor dynamics before treatment initiation and formulating a reasonable treatment schedule is an urgent challenge. To overcome this problem, we constructed a mathematical model based on clinical observations and investigated the optimal schedules for EGFR-TKI therapy. Based on published data on cell growth rates under different drugs, we found that using osimertinib that are efficient for secondary resistant cells as the first-line drug is beneficial in monotherapy, which is consistent with published clinical statistical data. Moreover, we identified the existence of a suitable drug-switching time; that is, changing drugs too early or too late was not helpful. Furthermore, we demonstrate that osimertinib combined with erlotinib or gefitinib as first-line treatment, has the potential for clinical application. Finally, we examined the relationship between the initial ratio of resistant cells and final cell number under different treatment conditions, and summarized it into a therapy suggestion map. By performing parameter sensitivity analysis, we identified the condition where osimertinib-first therapy was recommended as the optimal treatment option. This study for the first time theoretically showed the optimal treatment strategies based on the known information in NSCLC. Our framework can be applied to other types of cancer in the future.

Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles

The charging of electric vehicles (EVs) at residential premises is orchestrated through either centralized or decentralized control mechanisms. The former emphasizes adherence to power grid constraints, employing demand management techniques to restrict EV charging when the aggregated demand exceeds a predetermined threshold, which may result in user discontentment. Conversely, the latter endows EV users with the authority to self-regulate their charging behavior to optimize cost, allowing a multitude of interconnected EVs to charge during the same off-peak window. However, this decentralized approach gives rise to the herding problem, wherein a simultaneous surge in EV charging during off-peak periods burdens the power grid, leading to potential system overloads. This paper presents a hybrid coordinating scheme that integrates a fuzzy inference mechanism to synergistically blend the merits of centralized and decentralized coordinations. The proposed hybrid coordination scheme aims to minimize peak load, alleviate herding, and optimize charging costs while ensuring adherence to EV users’ charging obligations at the lowest feasible expense. The problem is formulated with the introduction of a novel fuzzy objective function and subsequently resolved through the fuzzy inference mechanism. The fuzzy inference encapsulates independent and uncertain price profiles, consumption load patterns, and state-of-charge data collected from the power grid, households, and EV domains, which are effectively integrated into weighted variables for the requesting EVs. The proposed hybrid coordinating scheme leverages weighted variables to optimize the objective function, enabling the determination of an optimal charging schedule that satisfies the charging requirements of the requesting EVs, while adhering to stringent power grid operational constraints and minimizing charging costs. To assess the efficacy of the hybrid coordination scheme, we conducted two meticulous case studies employing the IEEE 34 bus system as a testbed, thoroughly evaluating performance metrics encompassing charging cost, load profile impact, and peak-to-average ratio. The results demonstrate the superior performance of the proposed hybrid coordination scheme compared to alternative charging strategies, including uncoordinated charging, standard-rate charging, time-of-use charging, and two-layer decentralized approaches.

The Impact of Dose Reduction of Bacillus Calmette-Guerin on Oncological Outcomes and Toxicity in Non-Muscle Invasive Bladder Cancer: A Systematic Review and Meta-Analysis.

Bacillus Calmette-Guerin (BCG) is the standard adjuvant treatment for intermediate and high-risk non-muscle invasive bladder cancer (NMIBC) following transurethral resection of the bladder (TURB). However, the optimal dose, strain, and schedule of BCG remain unclear. To evaluate the impact of BCG dose reduction on oncological outcomes and toxicity in patients with non-muscle invasive bladder cancer. We performed a systematic review of the literature in PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases. Selected studies were analyzed for Meta Analysis using PRISMA criteria. The study focused on disease recurrence, progression, and toxicity. We also compared the oncological outcomes of the different BCG strains. A total of 2963 patients in 13 randomized controlled trials were included. In recurrence analysis, we found a non-significant difference between the full dose and any dose reduction of BCG (RR = 1.17, [1.06-1.28], I2 = 0%, p = 0.7). In terms of progression, the difference was also non-statistically significant (RR: 1.12 [0.89 - 1.41], I2 = 0%, p = 0.93). In the toxicity analysis, there were more local (RR: 0.81 [0.67-0.99] I2 = 76%; p < 0.01) and systemic (RR: 0.53 [0.34-0.82] I2 = 83%; p < 0.01) side effects in the full dose group than in the dose reduction group. There were no statistically significant differences in oncological outcomes between the analyzed BCG strains. Dose reduction did not affect the oncological outcomes of patients with NMIBC who received adjuvant therapy with BCG. On the other hand, dose reduction showed a significant trend towards fewer systemic and local side effects. Further studies comparing oncological and toxicity outcomes using different strains are needed.

Optimal Lot-Sizing Decisions for a Remanufacturing Production System under Spare Parts Supply Disruption

Remanufacturing is one of the ways forward for product recovery initiatives and for maintaining sufficient production flow to satisfy customer demand by providing high-quality goods with a combination of new and return parts through a circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chains vulnerable to disruptions. One of the main disruptions that occurs in remanufacturing systems is the shortage of spare parts supply, which results in unexpected delays in the remanufacturing process and could eventually result in a possible loss of sales. In the event of such potential disruptions, remanufacturing facilities must manage their supply chains in an effective and optimal manner such that the negative impact of disruptions to their business can be minimised. In this study, a two-stage production–inventory system was analysed by developing a cost-minimisation model that focuses on the recovery schedule after the occurrence of a disruption in sourcing spare parts for a remanufacturer’s production cycle. The developed model was solved using the branch-and-bound algorithm, where the experimental results demonstrated that the model provides effective solutions. Through numerical experiments, results indicated that the optimal recovery schedule and the number of recovery cycles are considerably dependent on the disruption time, lost sales and backorder costs. A sensitivity analysis showed that the lost sales option seems to be more effective than the backorder sales option in optimising the system’s overall cost due to unmet demand, which becomes lost sales when serviceable items are reduced, thereby shortening recovery time. Furthermore, a case study revealed that a manufacturer’s response to disruption is highly influenced by the spare part costs and overall recovery costs as well as the supplier’s readiness level. The proposed model could assist managers in deciding the optimal production strategy whilst providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

Live attenuated vaccines and layered defense strategies to combat infections caused by nonencapsulated Yersinia pestis

IntroductionPlague is an ancient disease caused by Yersinia pestis, a widely disseminated Tier 1 pathogen that poses significant public health and biothreat risks. The rapid course and high mortality of pneumonic plague limit the efficacy of antibiotic treatment and mandate the need for an effective, licensed, and readily available vaccine. New candidate vaccines are being developed; however, their efficacy in nonhuman primates, optimal vaccination schedule and immune response, duration of protection, and breadth of coverage against various virulent strains are inadequately understood. In the current work, we explored homologous and heterologous vaccination schemes using the sensitive BALB/c mouse models of bubonic and pneumonic plague challenged with Y. pestis strain C12. This strain, a derivative of the wild-type strain CO92, lacks the anti-phagocytic F1 capsule yet remains highly virulent. Protection against such nonencapsulated strains has been particularly elusive.MethodsWe tested the efficacy of live attenuated vaccine (LAV) derivatives of Y. pestis CO92 or C12 with a deletion of a type 3 secretion-associated gene (ΔyscN) or the pgm pigmentation locus, and they were cured of the pPst (PCP1) plasmid (CO92 pgm− pPst−). The LAVs were evaluated alone or accompanied by a dose of a protein subunit vaccine (rF1V or rV).ResultsThe most protective and immunogenic vaccination scheme, as tested under a variety of conditions in bubonic and pneumonic plague models, was heterologous vaccination with a LAV and the recombinant rF1V or rV protein subunit vaccine. Furthermore, in the heterologous scheme, different LAVs and subunit vaccines could be substituted, affording flexibility in vaccine component selection. We also evaluated a novel intervention strategy consisting of vaccination and post-exposure antibiotic treatment. The layering of vaccination with the LAVs and post-exposure treatment with streptomycin was synergistic, extending the time after the Y. pestis C12 challenge when treatment remained effective and affording a sparing of antibiotics.ConclusionThe current work defined effective and flexible vaccination and treatment interventions that successfully prevented lethal infection with virulent, nonencapsulated Y. pestis.

Construction management in space: explore solution space of optimal schedule and cost estimate

Multi-planetary life is one of humanity's audacious dreams. A key challenge towards achieving such a space mission is the design and construction of space habitats, for instance, on Mars. This paper presents a virtual space construction decision framework (SCDF) prototype developed and tested to address the question: "How can space construction project partners make informed decisions and leverage new construction methods and cutting-edge technologies that are developed and transform the AEC terrestrial industry?" We consider six practical and theoretical points of departure reflecting knowledge and technology and their application towards developing SCDF: General Contractor Workflow; BIM; Generative Scheduling and Construction Schedule Optimization; Construction Robotics; 3D Printing; Virtual Reality (VR) and Visualization. SCDF development applied virtual design and construction (VDC) to model - simulate - optimize - visualize - validate a space construction project by exploring the solution space in the context of extra-terrestrial construction environments from concept design to construction completion in the virtual environment before any mission is launched. Results confirm that insights from terrestrial construction apply to extra-terrestrial construction and vice versa. These insights contribute to the six points of departure at three levels: 1. The SCDF; 2. Extensions to existing technology platforms; 3. New approaches and methods.

Combined Optimization of Maintenance Works and Crews in Railway Networks

This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.

An Adaptive Shift Schedule Design Method for Multi-Gear AMT Electric Vehicles Based on Dynamic Programming and Fuzzy Logical Control

This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

Tb3+ and/or Sm3+ doped NaGd(MoO4)2 nanocrystalline transparent glass-ceramics: Preparation, tunable luminescence, and energy transfer

Tb3+ and/or Sm3+ doped SiO2–B2O3–Na2O–ZnO–MoO3-Gd2O3 transparent glass-ceramics containing NaGd(MoO4)2 nanocrystals were successfully prepared by the melt-quenching method and subsequent heat treatment process. The optimal schedule to achieve heat treatment was crystallized at 650 °C for 5 h. The strongest green luminescence (545 nm: 5D4→7F5) was obtained under 376 nm excitation when the Tb2O3-doped amount was 0.3 mol%. A thorough investigation was conducted to explore the mechanism of energy transfer from Tb3+ ions to Sm3+ ions, which is caused by quadrupole-quadrupole interactions. Tb3+-Sm3+ co-doped SiO2–B2O3–Na2O–ZnO–MoO3-Gd2O3 glass-ceramics can achieve green, yellow, and red multi-color tunable luminescence as the amount of Sm2O3 doping changes, which indicates that it holds promising potential in applications such as ultraviolet-transformed white light-emitting diodes and various optical apparatus.

Uncertainty risk assessment of overloading violation based on security region and risk scheduling of active distribution networks

With increased uncertainties and depressed security margins of distribution networks, the risk of violating load or voltage constraints has increased. The usual risk-prevention method is to set an appropriate optimal schedule of active distribution networks to reserve enough margin without incurring extra operating costs. In this study, an optimal scheduling method for an active distribution network is proposed that allows for a low level of violation risk to minimize operating costs while maintaining good system security. Overload violation risk cost is a financial cost due to the cost of countermeasures needed to avoid overload violation. In this study, the N-0 security regions of overload are set as the constraint that provides the definition and calculation of the bidirectional violation distance of overloading for active distribution networks based on the N-0 security region. The relationship between the violation distance of overloading and the countermeasures against overload violation have been established based on the power flow equations, enabling the calculation of the countermeasures cost. The assessment model for the overload violation risk cost is derived from the cost of countermeasures and probability distributions of nodal power. The point estimation algorithm is introduced to simplify the calculation. The calculation of risk cost is fixed through the conditional value-at-risk. On this basis, a novel optimization scheduling model of active distribution networks that considers risk cost is constructed. Examples of the optimal schedule are designed and the validity of the proposed method is verified.