Optimal Time Schedule Research Articles

Optimal time plan for accelerated degradation testing based on the inverse Gaussian process

This paper addresses the optimal design problem for constant-stress accelerated degradation tests using inverse Gaussian (IG) process models. Unlike previous studies that primarily focussed on optimizing stress levels and sample sizes, this research uniquely emphasizes the critical role of measurement timing as a design variable. We investigate D-, A-, and V -optimal designs for IG processes, both with and without random effects. Our study derives analytical solutions for optimal measurement time schedules in IG processes characterized by linear degradation paths and provides numerical solutions for those with nonlinear degradation paths. To streamline the search for optimal time schedules, we employ a lower-dimensional parameterization method that mitigates the substantial computational burden. This approach facilitates the generation of multiple measurement times using only a limited number of design variables. The numerical results highlight the significant impact of optimal time scheduling on enhancing the efficiency and effectiveness of accelerated degradation test designs.

Quantitative brain mapping using magnetic resonance fingerprinting on a 50-mT portable MRI scanner.

Ultralow-field magnetic resonance imaging (ULF-MRI) has broad application prospects because of its portable hardware system and low cost. However, the low B0 magnitude of ULF-MRI results in a reduced signal-to-noise ratio in qualitative images compared with that of commercial high-field MRI, which can affect the visibility and delineation of tissues and lesions. In this work, a magnetic resonance fingerprinting (MRF) approach is applied to a homemade 50-mT ULF-MRI scanner to achieve efficient quantitative brain imaging, which is an original and promising disease-diagnosis approach for portable MRI systems. An inversion recovery fast imaging with steady-state precession-based sequence is utilized for MRF through Cartesian acquisition. A microdictionary analysis method is proposed to select the optimal repetition time and flip angle variation schedule and ensure the best possible tissue discriminative ability of MRF. The T1 and T2 relaxation properties and the B1 + distribution are considered for estimation, and the results are compared with those of gold standard (GS) quantitative imaging or qualitative imaging methods. The phantom experiment indicates that the quantitative values obtained by schedule-optimized MRF show good agreement, and the bias from the GS results is acceptable. The in vivo experiment shows that the relaxation times of white and gray matter estimated by MRF are slightly lower than the reference data, and the relaxation times of lipid are within the range of the reference data. Compared with qualitative MRI under ULF, MRF can intuitively reflect various items of brain tissue information in a single scan, so it is a valuable addition to point-of-care imaging approaches.

Intelligent Reflecting Surface-Assisted Wireless Powered Heterogeneous Networks

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

Multi-IRS Assisted Wireless-Powered Mobile Edge Computing for Internet of Things

This paper proposes a multiple intelligent reflecting surfaces (IRSs) assisted wireless-powered mobile edge computing (MEC) system, where the IRSs are deployed to assist both the downlink wireless power transfer (WPT) from the multi-antenna hybrid access point (HAP) to the wireless devices (WDs) and the uplink computation offloading from the WDs to the MEC server. To further improve the system performance, the energy beamforming and multiple-user detection (MUD) technologies are exploited. We consider both partial and binary offloading schemes and formulate the sum computation rate (SCR) maximization problems for them, respectively. To tackle the non-convexity of each problem, we propose an efficient alternating optimization (AO) method. Specifically, the Lagrange duality method is used to optimize the energy beamforming vector and the MUD matrix at the HAP, and the CPU frequencies and transmit power of the WDs. Then, we optimize the discrete phase shifts via the successive convex appropriation (SCA) method, the rank-one equivalents, and rounding method. Finally, the optimal time scheduling can be obtained via the one-dimensional search method. In addition, we propose a greedy algorithm with low complexity to optimize the computing modes for the binary offloading scheme. Numerical results show that our proposed schemes outperform the benchmarks.

How long should we follow patients managed for muscle-invasive bladder cancer? Lesson learned from a recent clinical practice.

To the Editor, The exact time to stop bladder cancer patient's follow-up is not well known and there is not a clear recommendation on if and when stop to follow a patient managed for muscle invasive bladder cancer (MIBC). Major urological guidelines do not provide a precise indication on the timing of follow-up, and there is currently no real consensus on optimal time schedule [...].

Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity.

The next generation 6G wireless systems are envisioned to have higher reliability and capacity than the existing cellular systems. The reconfigurable intelligent surfaces (RISs)-assisted wireless networks are one of the promising solutions to control the wireless channel by altering the electromagnetic properties of the signal. The dual connectivity (DC) increases the per-user throughput by utilizing radio resources from two different base stations. In this work, we propose the RIS-assisted DC system to improve the per-user throughput of the users by utilizing resources from two base stations (BSs) in proximity via different RISs. Given an -fair utility function, the joint resource allocation and the user scheduling of a RIS-assisted DC system is formulated as an optimization problem and the optimal user scheduling time fraction is derived. A heuristic is proposed to solve the formulated optimization problem with the derived optimal user scheduling time fractions. Exhaustive simulation results for coverage and throughput of the RIS-assisted DC system are presented with varying user, BS, blockage, and RIS densities for different fairness values. Further, we show that the proposed RIS-assisted DC system provides significant throughput gain of 52% and 48% in certain scenarios when compared to the existing benchmark and DC systems.

RIS Assisted Wireless Powered IoT Networks With Phase Shift Error and Transceiver Hardware Impairment

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

This paper is concerned with a cognitive cloud radio access network (CRAN) with a special attention to efficient and reliable downlink transmission of big data for secondary users (SUs). Existing approaches either try to maximize the number of accepted SUs or the sum data rate of accepted SUs. The first approach unfairly favors users with small data requests, whereas the second approach allocates most resources to users with better channel conditions. In contrast, this paper develops a novel approach that favors big data requests while simultaneously maintaining a certain degree of fairness among SUs. To this end, we first introduce a novel objective function that allows us to jointly optimize deadline-aware time scheduling, spectrum allocation, SU selection, and remote radio head (RRH) allocation for SUs. Second, we demonstrate that finding the global optimum solution entails the enumeration of all colorful independent sets on a generalized interval graph, which is known to be NP-hard. Third, we propose a dynamic programming (DP) approach, which yields the global optimum solution at a reduced computational cost. Fourth, we analyze the complexity of the proposed DP approach and assess its performance against existing baseline algorithms. Simulation results reveal that our solution favors big data users while incurring only a small degradation in the fairness index. Our proposed solution is practical for small-to-medium size networks. Furthermore, it offers an optimum benchmark for any new sub-optimal low-complexity algorithm.

Aquila and Salp Swarm Algorithms to an Optimal Time and Energy-Aware Workflow Scheduling in Iot-Fog-Cloud Network

Aquila and Salp Swarm Algorithms to an Optimal Time and Energy-Aware Workflow Scheduling in Iot-Fog-Cloud Network

Minimizing Rental Costs in Flowshop Scheduling with the NEH (Nawaz, Enscor, and Ham) Method

This study discusses how to minimize rental costs in flowshop scheduling. Optimal scheduling time was obtained using the Nawaz, Enscor, and Ham (NEH) method. The right time to rent a machine is obtained by comparing the utility of the machine with the processing time of each machine with the processing time of each job on the machine. We apply this research in CV. SAMHARI which is a company that manufactures sandals. After applying the rental cost minimization method at the research site, we found that CV. SAMHARI can minimize the rental costs incurred by Rp. 273700.

Platinum and Taxane Based Adjuvant and Neoadjuvant Chemotherapy in Early Triple-Negative Breast Cancer: A Narrative Review.

Platinum (Pt) derivatives such as cisplatin and carboplatin are the class of drugs with proven activity against triple-negative breast cancer (TNBC). This is due to the ability of Pt compounds to interfere with the DNA repair mechanisms of the neoplastic cells. Taxanes have been efficacious against estrogen receptor-negative tumors and act by disruption of microtubule function. Due to their distinct mechanisms of action and routes of metabolism, the combination of the Pt agents and taxanes results in reduced systemic toxicity, which is ideal for treating TNBC. Also, the sensitivity of BRCA1-mutated cells to taxanes remains unsolved as in vitro evidence indicates resistance against taxanes due to BRCA1 mutations. Recent evidence suggests that the combination of carboplatin and paclitaxel resulted in better pathological complete response (pCR) in patients with TNBC, both in neoadjuvant and adjuvant settings. In vitro studies showed sequential dependency and optimal time scheduling of Pt- and taxane-based chemotherapy. Also, combining carboplatin with docetaxel in the NAC regimen yields an excellent pCR in patients with BRCA-associated and wild-type TNBC. TNBC is a therapeutic challenge that can be tackled by identifying new therapeutic sub-targets and specific cross-sections that can be benefitted from the addition of Pt- and taxane-based chemotherapy. This review summarizes the merits as well as the mechanism of Pt- and taxane-based adjuvant and neoadjuvant chemotherapies in early TNBC from the available and ongoing clinical studies.

Digital twins for managing railway maintenance and resilience.

Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM's applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.

Sex-Dependent Signatures, Time Frames and Longitudinal Fine-Tuning of the Marble Burying Test in Normal and AD-Pathological Aging Mice.

The marble burying (MB) test, a classical test based on the natural tendency of rodents to dig in diverse substrates and to bury small objects, is sensitive to some intrinsic and extrinsic factors. Here, under emerging neuroethological quantitative and qualitative analysis, the MB performance of 12-month-old male and female 3xTg-AD mice for Alzheimer’s disease and age-matched counterparts of gold-standard C57BL6 strain with normal aging unveiled sex-dependent signatures. In addition, three temporal analyses, through the (1) time course of the performance, and (2) a repeated test schedule, identified the optimal time frames and schedules to detect sex- and genotype-dependent differences. Besides, a (3) longitudinal design from 12 to 16 months of age monitored the changes in the performance with aging, worsening in AD-mice, and modulation through the repeated test. In summary, the present results allow us to conclude that (1) the marble burying test is responsive to genotype, sex, aging, and its interactions; (2) the male sex was more sensitive to showing the AD-phenotype; (3) longitudinal assessment shows a reduction in females with AD pathology; (4) burying remains stable in repeated testing; (5) the time-course of marbles burying is useful; and (6) burying behavior most likely represents perseverative and/or stereotyped-like behavior rather than anxiety-like behavior in 3xTg-AD mice.

Digital twins for managing railway maintenance and resilience

Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM’s applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.

Optimal Sizing and Operation of Airport Infrastructures in Support of Electric-Powered Aviation

The adoption of hybrid-electric aircraft is expected to have a considerable impact on airport operations, with the need of new infrastructural requirements to support electric-powered fleets. In particular, battery-charging requirements shall play a decisive role. Preliminary investigations useful to perform scenario studies for the future implementation of electric-powered aviation can take advantage of the ARES methodology presented here, which provides the optimal solution to the sizing of airport battery recharging infrastructures. Based on the flight schedule and on the specifications of the aircraft fleet and the charging equipment, the solution assesses the number and type of charging points, the related electrical consumption in terms of energy and power, and further information needed to guarantee the required operational level while minimizing the procurement and operating costs. The method allows considering and comparing two charging strategies: plug-in recharge and battery swapping. Energy price variation in time is also taken into account and a full description of the optimal time scheduling of recharging operations is provided. Application studies to the reconfiguration of two existing aerodromes, a General Aviation airport and a large regional hub, are discussed, showing the potential of the proposed approach.

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

ABSTRACT Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.

Integrated decision making for attributes sampling and proactive maintenance in a discrete manufacturing system

An integrated optimal design of attributes sampling and proactive maintenance for a discrete manufacturing system is studied in this paper. In the system, the failure of a critical component causes the process to shift. The new mathematical model for online sampling of the discrete manufacturing system is based on the binomial and truncated negative binomial distributions. In addition to performing scheduled maintenance and unscheduled corrective maintenance at the time of a true alarm, an additional maintenance opportunity when a false alarm occurs is also considered. The optimal scheduled maintenance time and sampling parameters are determined by solving a mixed integer nonlinear programming problem to minimise the long-run cost rate. A numerical example is provided to illustrate the proposed integrated attributes sampling and maintenance plan. The results show that the integrated approach outperforms the alternatives that consider different models separately. More importantly, showing the benefit of doing maintenance upon a false alarm provides a stakeholder with a new idea in managing a deteriorating manufacturing system.

Optimal Time Scheduling in Relay Assisted Batteryless IoT Networks

In this letter, we propose a novel relay transmission scheme in a batteryless IoT network for practical implementation and high energy-efficiency, where communications between a hybrid access point (HAP) and multiple batteryless sensors are assisted by energy-constrained gateways. In the proposed system, while a batteryless sensor backscatters the incident signals from the HAP to transmit data to its gateway, other gateways can simultaneously harvest energy from the HAP. Then, the gateways can use their harvested energy to forward the received signals to the HAP. Under this setup, we formulate the achievable sum-rate maximization problem by optimizing the time allocation between data backscattering, energy harvesting, and data forwarding. Then, an efficient method is proposed to find the optimal solution. Simulation results show that the proposed relay transmission scheme can achieve up to 34% sum-rate gain over two benchmark schemes.

Research on Loading and Unloading Resource Scheduling and Optimization of Rail–Road Transportation in Container Terminal Based on “Internet +” —for Ghana Container Port Development Planning

Based on the “Internet +” technology, this paper establishes a logistics information platform for the container terminal. Under the premise of the scheduled arrival time and quantity of the truck, this paper aims at minimizing the working time of the loading and unloading equipment and the stay time of the train and truck in the station, and develops a scheduling optimization of loading and unloading model combing equilibrium assignment and flexible scheduling to realize the seamless transfer between rail and road transportation in container terminal. In order to solve the model, a multi-layer coding genetic algorithm with chromosome feasibility is designed to obtain the optimal scheduled time for the truck, and the optimal operation sequence of the gantry crane. Referring to China’s container station, this paper takes Takoradi container terminal of Ghana as a case to verify the accuracy and effectiveness of the model and algorithm and provides the medium or long term planning for Ghana’s development.

Multilevel CLOS data stream exchange algorithm based on smoothed round-robin fair scheduling

ABSTRACT The traditional scheduling algorithm has the problems of reducing the success rate of data stream exchange, increasing the exchange time, and increasing the number of required priorities. Therefore, a multi-level CLOS data stream exchange algorithm based on smooth polling fair scheduling is proposed. A multi-level CLOS network data flow scheduling model based on dynamic priority is established, and the value attribute is used as the priority parameter. Design time attribute evaluation criteria to schedule data streams in the best order to ensure that most data streams can be exchanged within the optimal scheduling time. By adjusting the two parameters of data stream value and execution urgency, you can change the degree of influence on the priority of data stream exchange. A smooth cyclic fair scheduling algorithm is used to implement data flow exchange in a multi-stage Clos network system composed of periodic data flows. Experimental results show that the algorithm improves the success rate of data stream exchange, reduces the number of exchanges, reduces the number of exchanges, and reaches the number of priorities required by the system.