Search

When an order arrives at a warehouse it is usually assigned to a batch and a decision is made on how long to wait before assigning the batch to a picker and starting the picking tour. If the idle time of the pickers is minimised, the batch is immediately assigned, and the picking starts. Alternatively, if a time window is introduced, other orders may arrive, and more efficient batches may be formed. The method to decide how long to wait (the time-window strategy) is therefore important but, surprisingly, almost completely overlooked in the literature. In this paper, we demonstrate that this lack of attention is unwarranted, and that the time-window method significantly influences the overall warehouse performance. In the context of the online order batching problem (OOBP), we first demonstrate that the effects of different time-window strategies are independent of the methods used to solve the other subproblems of the OOBP (batching and routing). Second, we propose two new time-window strategies, compare them to existing methods, and prove that our methods outperform those in the literature under various scenarios. Finally, we show how time-window methods influence different objective functions of the OOBP when varying numbers of orders and pickers. [ABSTRACT FROM AUTHOR]

Background: The HuoXueTongFu Formula (HXTF) originates from the classic prescription " DaHuangMuDan Decoction" from the "Synopsis of the golden chamber". Our previous study revealed that HXTF has a positive effect on postoperative peritoneal adhesion (PPA). However, the specific mechanism of HXTF on PPA formation within the time-to-treatment window has not been fully elucidated. This study aimed to determine the critical roles of HXTF as a result of its specific anti-inflammatory and antifibrinolytic activities for PPA treatment. Methods: The eight main bioactive components of HXTF were subjected to high-performance liquid chromatography-mass spectrometry. The core targets, critical biological processes, and underlying pathways of HXTF and PPA were identified via a series of network pharmacological methods. The specific anti-inflammatory function in the initial step of PPA formation was validated in peritoneal macrophages (PMs) isolated from PPA mice on Day 3 postsurgery. The potential anti-fibrinolytic activity in the next stage of PPA formation was subsequently explored in PPA mice on Day 7 postsurgery. Results: Network pharmacology revealed 160 common targets between HXTF and PPA. Several core targets, i.e., matrix metalloproteinase 9 (MMP9), tissue-type plasminogen activator (tPA), and plasminogen activator inhibitor 1 (PAI-1), were annotated as important biological processes (extracellular matrix disassembly and the collagen catabolic process). Validation experiments revealed that HXTF could induce macrophage polarization-mediated anti-inflammatory reactions by increasing the phagocytic capacity of PMs and promoting the release of anti-inflammatory cytokines (IL-4 and IL-10). In addition, HXTF promoted fibrinogenolysis and improved fibrinolytic activity, thereby inhibiting collagen deposition and reducing adhesion development. Conclusion: The ameliorative effects of herbal HXTF on PPA formation are attributable to the induction of macrophage polarization-mediated anti-inflammatory reactions in the early stage of PPA formation and the promotion of fibrinogenolysis and fibrinolytic activity in the middle stage of PPA formation. HXTF may be a promising alternative agent for the prevention and treatment of PPA. [ABSTRACT FROM AUTHOR]

Validating satellite data using buoys presents a significant challenge due to the differing spatial and temporal representativeness of these data sources. This discrepancy introduces two critical issues in satellite data validation: the selection of the matchup window during the collocation process and the estimation of representativeness error. While numerous studies have addressed these issues independently, research on their interaction remains limited. Utilizing quadruple Sea Surface Salinity (SSS) matchup datasets, this study investigates the impact of the time window on the estimation of SSS representativeness error between two satellite observations: SMOS and SMAP. Our results reveal a clear dependence of representativeness error on the selected time windows. In the global ocean, the variance of representativeness errors within a small time window is 0.040 psu2, which is substantially greater than that of the entire dataset (0.016 psu2). In contrast, the representativeness errors within larger time windows are systematically lower than those within smaller time windows. This research also finds that this dependency is not influenced by the randomness of the matchup data, climatological Sea Surface Salinity, or geographic locations. Instead, our findings suggest that the impact of the time window on representativeness error may reflect the characteristics of satellite data across different time intervals. Satellites are more likely to capture SSS signals within smaller time windows, resulting in larger representativeness errors when comparing satellite SSS with buoy measurements. Conversely, larger time windows lead to significant mismatches between satellite observations and hinder the ability of satellites to detect SSS signals, which can result in smaller or even reversed representativeness errors. [ABSTRACT FROM AUTHOR]

A common combinatorial optimization issue in actual engineering is the vehicle routing problem (VRP). Examples of these problems include logistics distribution, solid waste recycling planning, and underwater routing planning. The optimization algorithms are important for the solution quality of the proposed VRP. As the scale of the vehicle routing problem increases, the problem becomes more difficult. It is hard for the traditional algorithm to obtain the optimal solution to the problem in an acceptable computing time. In this paper, an adaptive large neighborhood water wave optimization (ALNSWWO) algorithm is designed to solve multi-depot capacitated vehicle routing problems with time windows (MDCVRPTW). Aimed at addressing the main problems of the original algorithm, an improvement strategy is designed. In the breaking operation, variable neighborhood search (VNS) and large neighborhood search (LNS) local search strategies are added. In the refinement operation, the learning operator based on the genetic algorithm and the adaptive large neighborhood search (ALNS) search mechanism is added. The above mechanism solves the problems that the original algorithm is prone to falling into local optima. The experimental results demonstrate that the distribution path scheme of fresh agricultural products (FAP) can be optimized through the ALNSWWO. The proposed ALNSWWO can reduce the distribution distance, time, cost, carbon emissions, and improve customer satisfaction. [ABSTRACT FROM AUTHOR]

China's freshwater resources are relatively small per capita, and the traditional passive control of hydrographic outbreaks can no longer meet modern water management needs. Data-driven models, such as Long Short-Term Memory Networks (LSTMs), have been gradually applied to water resources management, but most of the research has focused on the enhancement of the prediction effect of hybrid models while neglecting the importance of data structure. In this study, we predicted the number of dominant algae (blue-green algae) in a water source based on LSTM and explored the effects of different feature combinations and time window steps on the prediction performance. It was found that the model prediction was significantly improved by adding multiple features, and the R2 improved by 31.98% compared with single feature prediction. Meanwhile, as the time window (T-value) increased from 7 to 300, the R2 improved by 0.4%, but the iteration time increased by 96%. The results suggested that appropriate input feature selection is beneficial for model prediction, while longer time windows led to reduced model prediction benefits. Lastly, this study offers insights into future research directions from three key dimensions: the input indicator, optimization algorithm, and model combination. [ABSTRACT FROM AUTHOR]

This article conducts a detailed study on the vehicle routing problem with time window constraints. We constructed an objective function for the vehicle routing problem with time windows, established a mathematical model, and proposed an improved genetic algorithm to solve the problem. The algorithm first constructs a chromosome encoding method, designs a heuristic initialization algorithm to generate a better initial population, and determines the fitness function. During the operation of the algorithm, selection, crossover, and mutation operations are designed to generate offspring populations, enhancing the diversity of the population and avoiding premature convergence of the algorithm. Meanwhile, in order to improve the optimization and local search capabilities of genetic algorithms, this paper constructs a local search operation. Finally, the algorithm implements an elite retention strategy on the parent population and reconstructs a new population. We conducted simulation experiments on the algorithm using MATLAB and selected examples from the Solomon dataset for testing. The simulation experiment results have verified that the improved genetic algorithm is feasible and effective in solving vehicle routing problems with time windows. [ABSTRACT FROM AUTHOR]

This study focuses on the delivery routing problem faced by a transport company located in Phuket, Thailand. The goal of this study is to find a daily optimum route in order to minimize the total transportation cost, which comprises fixed costs associated with vehicle rental and variable costs calculated based on factors of travel distance, fuel prices, and fuel consumption. The complexity of this problem is compounded by the fact that customer demand often exceeds a vehicle capacity, in terms of weight and volume. In addition, delivery must be made within specific time windows. To tackle this issue, the delivery routing problem is classified as a multi-trip capacitated vehicle routing problem with time window (MTCVRPTW). Since the problem is NP-hard, an application of metaheuristic is more practical to determine the delivery routing of the company within a reasonable computing time. In this study, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are applied to solve MTCVRPTW. The numerical results show that DE provides better solution quality compared to those obtained from PSO and company current practices.

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).

Myoelectric is a biological signal produced from physiological variations in muscle fibers when they contract and relax. The study of muscle activity through the recording and analysis of myoelectric signals is called electromyography. Electromyography can provide a comprehensive view of the operation and performance of internal muscle groups and cells. The objective of this study is an investigation into hand movement classification using surface electromyography (sEMG) signals. For training and evaluating the proposed model, two data sets from the Ninapro project, a publicly available database for prosthetic hand control, were used: DB5 with low‐cost 16 channels and 200 Hz sampling rate setting and DB10 with 12 channels and 1926 kHz sampling rate setup. First, we divided the EMG data into segments using the windowing technique. These segments were then used to extract a set of time features. Finally, the retrieved feature information was loaded into a simple pattern recognition model: Artificial Neural Network. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models; 2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest; 3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security. [ABSTRACT FROM AUTHOR]

The treatment time window for acute cerebral infarction in global guidelines is within 24 h. We report a patient who was admitted to the hospital and underwent endovascular treatment reaching 40 h. During vascular examination, the thrombus moved to distant segment, and then the surgeon quickly performed endovascular treatment. The patient ultimately achieved a good outcome. This case indicates that thrombus is moveable at any time, we expected to provide advice to clinical doctors that vascular examination should also be arranged as soon as possible to clarify the etiology in stroke patients especially with low NIHSS scores. [ABSTRACT FROM AUTHOR]

In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation. [ABSTRACT FROM AUTHOR]

BackgroundThe HuoXueTongFu Formula (HXTF) originates from the classic prescription “DaHuangMuDan Decoction” from the “Synopsis of the golden chamber”. Our previous study revealed that HXTF has a positive effect on postoperative peritoneal adhesion (PPA). However, the specific mechanism of HXTF on PPA formation within the time-to-treatment window has not been fully elucidated. This study aimed to determine the critical roles of HXTF as a result of its specific anti-inflammatory and antifibrinolytic activities for PPA treatment.MethodsThe eight main bioactive components of HXTF were subjected to high-performance liquid chromatography-mass spectrometry. The core targets, critical biological processes, and underlying pathways of HXTF and PPA were identified via a series of network pharmacological methods. The specific anti-inflammatory function in the initial step of PPA formation was validated in peritoneal macrophages (PMs) isolated from PPA mice on Day 3 postsurgery. The potential anti-fibrinolytic activity in the next stage of PPA formation was subsequently explored in PPA mice on Day 7 postsurgery.ResultsNetwork pharmacology revealed 160 common targets between HXTF and PPA. Several core targets, i.e., matrix metalloproteinase 9 (MMP9), tissue-type plasminogen activator (tPA), and plasminogen activator inhibitor 1 (PAI-1), were annotated as important biological processes (extracellular matrix disassembly and the collagen catabolic process). Validation experiments revealed that HXTF could induce macrophage polarization-mediated anti-inflammatory reactions by increasing the phagocytic capacity of PMs and promoting the release of anti-inflammatory cytokines (IL-4 and IL-10). In addition, HXTF promoted fibrinogenolysis and improved fibrinolytic activity, thereby inhibiting collagen deposition and reducing adhesion development.ConclusionThe ameliorative effects of herbal HXTF on PPA formation are attributable to the induction of macrophage polarization-mediated anti-inflammatory reactions in the early stage of PPA formation and the promotion of fibrinogenolysis and fibrinolytic activity in the middle stage of PPA formation. HXTF may be a promising alternative agent for the prevention and treatment of PPA.

In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation.

Abstract Background Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. Methods We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. Results Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P

Abstract The diversity of consumer demand for take-out food has led to the characteristic structure of one order with multiple items, where the different food items in a single order are provided by two or more merchants. In the context of multi-item delivery for take-out orders with time windows, this study investigates vehicle routing for order delivery. This research aims to improve the service level of merchants and the efficiency of delivery vehicles. Food vendors receive orders from consumers through online platforms, then package the food items according to the orders. This method is a preliminary exploration of the issue of fulfilment of takeout orders on online platforms, and can provide preliminary theoretical support for decision-making on the delivery process of takeout orders on online platforms. In the context of online catering sales platforms and offline food sales merchants, this paper studies the delivery problem of takeaway orders with a time window and the characteristics of one order and multiple items, and constructs a method that takes the order delivery time requirements into account and minimizes the total order fulfilment cost. Taking into account the time window constraints of both physical restaurants and consumers, genetic algorithms are utilized to solve the order delivery problem. Finally, through case studies and experiments, the effectiveness and feasibility of the mathematical model are validated. Practical recommendations and insights are provided from the perspective of management and route planning.

In order to improve the delivery efficiency of logistics, this paper proposed a vehicle routing problem with drone and time window considered by the time window, UAV (unmanned aerial vehicle) power change and multi-point continuous delivery of UAV. Then, this paper proposed a hybrid particle swarm optimization algorithm with local search to solve it. Based on the hybrid particle swarm optimization algorithm, the proposed algorithm transformed the problem solution space into the algorithm search space by constructing an efficient encoding and decoding strategy. Further, the proposed algorithm combined a single point insertion strategy, a vehicle replacement strategy and a UAV replacement strategy to form a local search strategy to improve the optimization ability. The experimental results show that the proposed model is more efficient than the model of pure vehicle distribution and saves 31.51% of the cost. The proposed algorithm is better than the four comparison algorithms, and its highest optimization rate is 82.08%. [ABSTRACT FROM AUTHOR]

In urban environments, the bicycle-sharing system (BSS) has emerged as a vital complement to public transportation, effectively bridging gaps in urban mobility. The successful operation of these systems is contingent upon ensuring the timely availability of bicycles and docking spaces, a challenge particularly pronounced during peak hours. Addressing this, our study introduces a novel approach for optimizing the rebalancing of BSS. We have developed a time-windowed rebalancing request generator, which, in conjunction with a route planning algorithm based on variable neighborhood search (VNS), provides a robust solution to this issue. This iterative evolutionary algorithm not only refines solutions progressively but also incorporates a shaking process to enhance the possibility of achieving global optimization. Our methodology further includes an algorithm for generating initial solutions, alongside two shaking processes designed to improve efficiency. We validate our approach using real-world data from Taipei's Youbike system, demonstrating superior performance in rebalancing efficiency compared to existing algorithms. Our study contributes to the optimization of BSS rebalancing, offering practical insights for urban transportation planning. [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. Methods: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. Results: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E–H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China. Conclusions: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease. [ABSTRACT FROM AUTHOR]

Soft skills, such as personality characteristics, communication skills and leadership, affect personal career performance greatly. Therefore, predicting the soft skills of interviewees can provide interviewers with a strong reference for the decision of hiring. Nowadays, as asynchronous video interviews have gradually become a popular form of interviews, automatic interview evaluation of soft skills has attracted widespread attention from researchers. However, existing automatic evaluation methods have two significant drawbacks. First, most of them model the problem as multi-modal fusion of long-term sequences, while ignoring the consistency of multi-modal expression in short-time windows, which is a key attribute of the interview scene. Second, without embedding of professional knowledge in the interview field, the interpretability of the model is relatively weak. To address the above problems, we propose a novel Multi-modal Window-Consistency Fusion network, namely MWCF, to capture the expression consistency of different modalities in a short-time window and re-weight the language signals to enhance important portions in verbal clues. Meanwhile, in order to enhance the interpretability of the evaluation model, we introduce the professional knowledge of interviewers by proposing a topic generation module based on question attention, and embedding the most representative keywords under different soft skills into the model. Furthermore, a real-world interview dataset is built by developing an asynchronous interview platform, and extensive experiments are conducted to show the superior performance of our proposed model. [ABSTRACT FROM AUTHOR]

The diversity of consumer demand for take-out food has led to the characteristic structure of one order with multiple items, where the different food items in a single order are provided by two or more merchants. In the context of multi-item delivery for take-out orders with time windows, this study investigates vehicle routing for order delivery. This research aims to improve the service level of merchants and the efficiency of delivery vehicles. Food vendors receive orders from consumers through online platforms, then package the food items according to the orders. This method is a preliminary exploration of the issue of fulfilment of takeout orders on online platforms, and can provide preliminary theoretical support for decision-making on the delivery process of takeout orders on online platforms. In the context of online catering sales platforms and offline food sales merchants, this paper studies the delivery problem of takeaway orders with a time window and the characteristics of one order and multiple items, and constructs a method that takes the order delivery time requirements into account and minimizes the total order fulfilment cost. Taking into account the time window constraints of both physical restaurants and consumers, genetic algorithms are utilized to solve the order delivery problem. Finally, through case studies and experiments, the effectiveness and feasibility of the mathematical model are validated. Practical recommendations and insights are provided from the perspective of management and route planning. [ABSTRACT FROM AUTHOR]

Surgical site infections (SSI) are one of the common complications following spinal fusion surgery. Unfortunately, several studies had shown conflicting results regarding optimal timing of surgical antimicrobial prophylaxis (SAP) administration. Due to limitations in population homogeneity and sample size, these studies have not provided significant statistical correlations or clear practical recommendations. The purpose of the study was to investigate the impact of timing of cefuroxime SAP on the risk of SSI in patients undergoing spinal fusion surgery, and to determine the optimal timing of administration. Retrospective nested case-control study. We retrospectively analyzed consecutive patients who underwent spinal fusion surgery at our institution between October 2011 and October 2021. In the current study, the primary outcome measure was SSI. This was a retrospective nested case-control study. All consecutive patients who underwent spinal fusion surgery at our institution between October 2011 and October 2021 formed a retrospective cohort. For each SSI case, 2 controls free of SSI at the time of the index date of their corresponding case were selected, matched by age, sex, and calendar year. Electronic record and radiographic data were reviewed retrospectively in electronic database. SAP related data included timing of administration, preoperative dose, intraoperative second dose, and postoperative use. To examine the effects of mismatched variables, we further adjusted for possible confounding factors using conditional logistic regression models. Subsequently, subgroup analyses were conducted to assess the robustness of the statistical associations. According to the preplanned statistical scheme and matching factors, we matched 236 controls for these SSI cases, and the subsequent statistical analysis was performed on these 354 patients. After adjusting for confounding factors, the results indicated that the risk of SSI was 70% higher in the group receiving SAP 31 to 60 minutes before incision compared to the group receiving SAP 0 to 30 minutes before incision (OR=1.732, 95%CI 1.031–2.910, p=.038). Additionally, the risk of SSI was 150% higher in the group receiving SAP 61 to 120 minutes before incision compared to the group receiving SAP 0 to 30 minutes before incision (OR=2.532, 95%CI 1.250–5.128, p=.010). In subgroup analysis, this statistical trend persisted for both deformity surgeries and different SSI classifications. Administering cefuroxime SAP within 30 minutes before skin incision significantly reduces the risk of SSI, whether they are deep or superficial, in spinal fusion surgery. This pattern remains consistent among spinal deformity patients. [ABSTRACT FROM AUTHOR]

This paper introduces an multi-compartment open vehicle routing problem with time window (MCOVRPTW) arising in fresh products distribution, which doesn't require the finished service vehicles return to the distribution center. There are three types of fresh products to be delivered by the multi-compartment vehicles, and the objective aims to find the vehicles scheduling scheme with the lowest cost. For this problem, we established a mathematical model in which the capacity constraint of each compartment and time window constraint must be satisfied. Then, we proposed a hybrid genetic algorithm with some local search-based operators, called GA-LS, to solve it. Each individual in initial population was generated by the nearest neighborhood method to obtain highquality initial population. When a child population was built, the local search procedure was executed on each individual in the child population to enhance its quality. Finally, we carried out some experiments to evaluate the effectiveness of the proposed algorithm. According to the results, the proposed algorithm can yield better solutions compared with existing approaches. Meanwhile, the proposed algorithm reveals good stability and convergence. [ABSTRACT FROM AUTHOR]

Even though in physics "time" is considered to be continuous, how the brain and mind deal with time might be different. It has been proposed that in cognition, time windows provide logistic platforms for information processing, such as the low‐frequency 3‐s time window. The following series of behavioral experiments may shed light on the dynamics within such a time window. Using a duration reproduction paradigm, we first replicated a pattern of reproduced duration observed in a previous single‐case study. Specifically, the reproduction increases as the pause between standard duration and reproduction increases, but only within the time window of some 3 s; when the pause goes beyond 4 s, the reproduction reaches a plateau of a subjective set‐point. This increasing phase is named the "temporal transition zone." Three more experiments were performed to test the features of the transition zone as a low‐frequency time window. It is also observed with different standard durations (2, 3, 4.5 s, in Experiment 2), and even when the frequency of the auditory stimuli was different in standard and reproduction (300 Hz in standard duration and 400 Hz in reproduction, in Experiment 4). The transition zone was observed only with pause durations of 2 to 3 s; when the shortest pause duration was 5 s, the transition zone was no longer observed, and the reproduction was stable at the subjective set‐point (in Experiment 3). Taken together, we suggest that the temporal transition zone indicates a pre‐semantic logistic platform to organize and process the information flow; in such a time window of some 3 seconds, the identity of an ongoing event is substantiated, building the "subjective present." [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A multi-objective vehicle routing optimization model is established to minimize total cost and equilibrium degree for multi-objective vehicle routing problem with time windows, and a hybrid multi-objective grey wolf algorithm is proposed to solve the problem. Mainly design three strategies: (1) A new encoding and decoding method is designed to achieve the conversion of continuous grey wolf position vectors to discrete customer sequences. (2) Convergence and distribution indicators are used to select decision individuals. (3) Multiple deletion and insertion operators have been designed to implement local routing optimization. To demonstrate the effectiveness of the algorithm, some numerical examples in Solomon are used as examples to experimentally compare the proposed algorithm with MOIGA and improved ACO algorithms. Experimental results show that the hybrid multi-objective grey wolf algorithm proposed in this paper can find a better Pareto solution, and its performance is better than other evolutionary algorithm. [ABSTRACT FROM AUTHOR]

Purpose: Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries. Methods: This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. Results: According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 − 25.42, p = 0.002), 2.85 (95% CI: 1.11 − 7.31, p = 0.030), 2.62 (95% CI: 1.12 − 6.13, p = 0.027), 2.44 (95% CI: 1.25 − 4.76, p = 0.009), and 1.5 (95% CI: 1.10 − 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Conclusion: Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

This study explored the efficacy of electromagnetic forming in addressing the limited formability of aluminum–Lithium (AlLi) alloy at room temperature, focusing on the AA2195-T4 sheet. Comparative analyses were conducted to assess the formability. The results indicated that the limit strains obtained through electromagnetic forming were at least 50%, 10%, and 50% higher compared with those obtained through rigid punch forming under the uniaxial tension, plane strain, and biaxial tension. Upon utilizing forming limit diagrams, the failure processes during electromagnetic flanging were predicted. Notably, electromagnetic flanging enabled the creation of hole-flanging structures with a 60% increase in the flanging limit strain around the circumference, surpassing the capabilities of rigid punch flanging. Moreover, the maximum time window for electromagnetic flanging was extended to 168 h. Analysis of the flanging strain path indicated that cracks were prone to occur near round corners during electromagnetic flanging, contrasting with rigid punch flanging where cracks tended to form near the edges of straight walls. This study offered valuable insights for aerospace component formation utilizing Al–Li alloys at room temperature.

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW) is a NP hard problem, which has a wide application in modern logistics. Memetic algorithm based on deep reinforcement learning is proposed to solve the problem. The large neighborhood search process of Memetic algorithm for VRPSPDTW is modeled into a Markov decision process. An encoder-decoder neural network architecture is designed for the removal operation in large neighborhood search. The extracted individual characteristics and location characteristics of all nodes in the current solution are input into the encoder for information interaction. The decoder outputs the nodes to be removed. Two kinds of decoders are designed including non-autoregressive and autoregressive structures. The neural network architecture uses reinforcement learning for training. A hybrid strategy is also designed, combining manually designed heuristic strategies with strategies learned through deep reinforcement learning to improve the optimization ability. Experimental results show that the proposed algorithm has a stronger ability to jump out of the local optimum, and can provide better solutions than the comparison algorithms in an effective time, especially in solving large-scale problems. In addition, ablation experiments are conducted on the new components of the proposed algorithm to show the effectiveness.

At present, there are almost no reports on vehicle scheduling methods for three-dimensional rail transit system, and the real-time performance of existing vehicle scheduling algorithms is poor. Aiming at the scheduling problem of three-dimensional rail transit vehicles, this paper studied an order allocation algorithm combining high and low frequency station judgment and a Dijkstra path planning algorithm combining time window, namely intelligent scheduling algorithms, to improve the operating efficiency of vehicles. Firstly, it used the order allocation algorithm to select the appropriate execution vehicle for the order to reduce the waiting time of passengers. Secondly, it added the judgment of high and low frequency stations on the basis of the order allocation algorithm, and scheduled vehicles to the high frequency stations in advance to ensure the balance of supply and demand. Then, it combined the ordinary Dijkstra algorithm and time window judgment to realize multi-vehicle conflict-free path planning. Finally, it redeveloped the OpenTCS software and simulated the scheduling algorithm with the software. The results show that the average waiting time of the passenger from calling the vehicles is 8.043 s only using the order allocation algorithm. After the advance scheduling of vehicles combined with high and low frequency stations, the average waiting time is reduced to 5.724 s, and the waiting time of each passenger is reduced by 2.319 s. During the path planning, both the ordinary Dijkstra algorithm and the Dijkstra algorithm combined with time window took less than 1 ms to plan. However, the Dijkstra algorithm combined with time window only increases the time about 0.1 ms, and solves the problems of the vehicle path conflicts. The studied intelligent scheduling algorithm can reduce the waiting time of the passengers and improve the running efficiency of vehicles. The algorithm has good real-time performance and can meet the scheduling requirements of three-dimensional rail transit vehicles. [ABSTRACT FROM AUTHOR]

Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries. This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q 1 , Q 3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 − 25.42, p = 0.002), 2.85 (95% CI : 1.11 − 7.31, p = 0.030), 2.62 (95% CI : 1.12 − 6.13, p = 0.027), 2.44 (95% CI : 1.25 − 4.76, p = 0.009), and 1.5 (95% CI : 1.10 − 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R 2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R 2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of the "last-mile" distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and load constraints (TW-LCs). Taking the optimal transportation path as the objective function and considering the time-window and vehicle load constraints, a charging station siting model was established. For the TW-LC problem, an improved genetic algorithm combining the farthest-insertion heuristic idea and local search operation was designed. Three different types of standardized arithmetic examples, C type, R type, and RC type, were used to test the proposed algorithm and compare it with the traditional genetic algorithm. The results indicate that, under the same conditions, compared to the traditional genetic algorithm, the improved genetic algorithm reduced the optimal path length by an average of 11.12%. It also decreased the number of charging stations selected, the number of vehicles in use, and the algorithm complexity by 22.97%, 13.71%, and 46.81%. Building on this, a case study was conducted on the TW-LC problem in a specific area of Chongqing, China. It resulted in a 50% reduction in the number of charging stations and a 25% reduction in the number of vehicles selected. In terms of economic indicators, the proposed algorithm improves unit electricity sales by 73.88% and reduces the total annualized cost of the logistics company by 18.81%, providing a theoretical basis for the subsequent promotion of ELVs. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effects of hyperbaric oxygen therapy at different time windows on serum tight junction protein (ZO-1), caveolin-1 (Caveolin-1) and Toll-like receptor 4 (TLR4) /nuclear transcription factor-κB (NF-κB) signaling pathway in patients with acute cerebral infarction (ACI) . Methods: 158 ACI patients who were treated in The First Affiliated Hospital of Air Force Military Medical University from April 2020 to April 2023 were selected as the research objects, patients were divided into group A (accept routine treatment, hyperbaric oxygen therapy was given within 12 h～7 d after the onset of the disease) and group B (accept routine treatment, hyperbaric oxygen therapy was performed within 12 h onset) by random number table method, with 79 cases in each group.The changes of each scale score, cerebral blood perfusion, serum ZO-1, Caveolin-1 levels and TLR4/NF-κB signaling pathway related indicators were compared in two groups. Results: After treatment, the National Institutes of Health Stroke Scale (NIHSS) in group B was lower than that in group A, and the activity of daily living (ADL) score was higher than that in group A (P＜0.05) . After treatment, the cerebral vascular reserve capacity (CVR), bilateral cerebral artery peak flow velocity and bilateral cerebral artery peak average flow velocity in group B were higher than those in group A (P＜0.05) . After treatment, serum Caveolin-1 in group B was lower than that in group A, and serum ZO-1 was higher than that in group A (P＜0.05) . After treatment, TLR4 and NF-κB in group B were lower than those in group A (P＜0.05) . Conclusion: Hyperbaric oxygen therapy in ACI patients within 12 hours of onset can effectively promote the recovery of neurological function and regulate the levels of serum ZO-1 and Caveolin-1, which may be relate to the inhibition of TLR4/NF-κB signaling pathway activation. [ABSTRACT FROM AUTHOR]

Introduction: Whether time window affects the intravenous thrombolysis (IVT) effect before endovascular thrombectomy (EVT) is uncertain. We aimed to investigate the effect of different time windows (0–3 h and >3–4.5 h from stroke onset to randomization) on clinical outcomes of EVT with or without IVT in a subgroup analysis of DIRECT-MT. Methods: The primary outcome was the 90-day modified Rankin Scale (mRS) according to time window. Logistic regression models were used to analyze the effect of different treatments (EVT with or without IVT) on outcomes within 0–3 h or >3–4.5 h. Results: Among 656 patients who were included in the analysis, 282 (43.0%) were randomized within >3–4.5 h after stroke onset (125 without IVT and 157 with IVT), and 374 (57.0%) were randomized within 0–3 h (202 without IVT and 172 with IVT). We noted no significant difference in the thrombectomy-alone effect between the time window subgroups according to 90-day ordinal mRS (adjusted common odds ratio [acOR] in patients within 0–3 h: 1.06 [95% CI: 0.73–1.52], acOR in patients within >3–4.5 h: 1.19 [95% CI: 0.78–1.82]) and 90-day functional independence. Thrombectomy alone resulted in an increased proportion of patients with 90-day mRS 0–3 treated within >3–4.5 h (62.90 vs. 48.72%) but not within 0–3 h (65.84 vs. 63.95%). However, there was no interaction effect regarding all outcomes after the Bonferroni correction. Conclusions: Our results did not support thrombectomy-alone administration within 3–4.5 h in patients with acute ischemic stroke from large-vessel occlusion in the subgroup analysis of DIRECT-MT. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acute ischemic stroke (AIS) remains to be a challenging cerebrovascular disease. The mainstay of AIS management is endovascular reperfusion therapy, including thrombectomy and thrombolysis. However, ineffective (futile) reperfusion (FR) or reperfusion injury (RI) can be seen in a significant number of patients undergoing reperfusion strategy. In this article, we discuss two clinically relevant concepts known as “time window” and “tissue window” that can impact the clinical outcome of reperfusion therapy. We also explore patient risk factors, leading to FR and RI as well as an emerging concept of “no-reflow phenomenon” seen in ineffective reperfusion. These fundamental concepts provide insight into the clinical management of AIS patients and provide references for future research.

This paper investigates an integrated scheduling problem of production and outbound distribution with flexible vehicle departure time in a time-sensitive make-to-order supply chain. We develop a hybrid multi-level optimisation framework by decomposing the problem into three sub-problems, including vehicle assignment, parallel machines scheduling and distribution scheduling. In this framework, we propose an efficient procedure to obtain the optimal vehicle departure time and utilise metaheuristics and heuristics to obtain the values of other decision variables. Results from extensive numerical experiments indicate that the proposed framework can solve small-scale instances optimally, and for large-scale instances it also shows the better performance than the compared genetic algorithm in terms of convergence and solution quality. Besides, the distribution cost can be reduced by setting flexible vehicle departure time. [ABSTRACT FROM AUTHOR]

IntroductionReinfections are increasingly becoming a feature in the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, accurately defining reinfection poses methodological challenges. Conventionally, reinfection is defined as a positive test occurring at least 90 days after a previous infection diagnosis. Yet, this extended time window may lead to an underestimation of reinfection occurrences. This study investigated the prospect of adopting an alternative, shorter time window for defining reinfection.MethodsA longitudinal study was conducted to assess the incidence of reinfections in the total population of Qatar, from February 28, 2020 to November 20, 2023. The assessment considered a range of time windows for defining reinfection, spanning from 1 day to 180 days. Subgroup analyses comparing first versus repeat reinfections and a sensitivity analysis, focusing exclusively on individuals who underwent frequent testing, were performed.ResultsThe relationship between the number of reinfections in the population and the duration of the time window used to define reinfection revealed two distinct dynamical domains. Within the initial 15 days post-infection diagnosis, almost all positive tests for SARS-CoV-2 were attributed to the original infection. However, surpassing the 30-day post-infection threshold, nearly all positive tests were attributed to reinfections. A 40-day time window emerged as a sufficiently conservative definition for reinfection. By setting the time window at 40 days, the estimated number of reinfections in the population increased from 84,565 to 88,384, compared to the 90-day time window. The maximum observed reinfections were 6 and 4 for the 40-day and 90-day time windows, respectively. The 40-day time window was appropriate for defining reinfection, irrespective of whether it was the first, second, third, or fourth occurrence. The sensitivity analysis, confined to high testers exclusively, replicated similar patterns and results.DiscussionA 40-day time window is optimal for defining reinfection, providing an informed alternative to the conventional 90-day time window. Reinfections are prevalent, with some individuals experiencing multiple instances since the onset of the pandemic.

Importance: Stroke-to-recanalization time is a strong predictor of outcomes in anterior circulation large-vessel occlusion (LVO). The authors aimed to evaluate functional outcomes in early (<6 h) vs. late (6-24 h) time windows for thrombectomy-treated basilar artery occlusions. Methods: Patients were derived from the Posterior Circulation Ischemic Stroke Evaluation: Analyzing Radiographic and Intra-procedural Predictors of Mechanical Thrombectomy (PC-SEARCH) Registry and retrospectively analyzed early and late basilar artery thrombectomy time windows cohorts. Patients were dichotomized based on the last known well and correlated to 90-day functional outcomes (mRS 0-3). A multiple logistic regression analysis was performed. Results: A total of 405 patients were included in this study: 216 and 189 patients in the early and late time windows, respectively. Baseline demographic, stroke, radiographic, and intraprocedural characteristics were similar between the groups. A total of 99 (46%) and 79 (42%) patients in the early and late time windows, respectively, achieved favorable functional outcomes at 90 days (p = 0.41), and multiple logistic regression analysis did not reveal differences between cohorts (OR: 0.74; 95% CI: 0.46-1.19; p = 0.22). Symptomatic hemorrhage (7% vs. 5%; p = 0.69) and neurological complications (8% vs. 9%; p = 0.83) were similar between the groups; however, hospital complications were more common in the early time window cohort (22% vs. 13%; p = 0.01). Conclusion: The early and late thrombectomy time windows can achieve similar rates of 90-day favorable functional outcomes. However, timely thrombectomy influences the likelihood of achieving excellent functional outcomes (mRS = 2) within the early time window. [ABSTRACT FROM AUTHOR]