Rolling Time Research Articles

Optimization of Truck–Cargo Matching for the LTL Logistics Hub Based on Three-Dimensional Pallet Loading

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing the three-dimensional loading of goods onto standardized pallets and assigning these loaded pallets to a fleet of heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed to solve this complex logistics challenge. In the first stage, a tree search algorithm based on residual space is developed to determine the optimal layout for the 3D loading of cargo onto pallets. In the second stage, a heuristic online truck–cargo matching algorithm is introduced to allocate loaded pallets to trucks while optimizing the number of trucks used and minimizing transportation costs. The algorithm operates within a rolling time horizon, allowing it to dynamically handle real-time order arrivals and time window constraints. Numerical experiments demonstrate that the proposed method achieves high pallet loading efficiency (close to 90%), near-optimal truck utilization (nearly 95%), and significant cost reductions, making it a practical solution for real-world LTL logistics operations.

Theoretical development and validation of asphalt concrete density measurement using non-contact interdigital coplanar capacitive sensor

Asphalt concrete (AC) density serves as a critical criterion for quality control and quality assurance in asphalt pavement construction. Current non-destructive testing (NDT) approaches have shown potential for AC density measurement, but they present disadvantages such as inefficiency and unstable accuracy. This paper developed a novel NDT approach for AC density measurement based on non-contact interdigital coplanar capacitive sensor (ICCS). The feasibility of this approach was evaluated through both numerical and experimental validations. Firstly, the theoretical model of non-contact ICCS for AC density measurement was established based on conformal mapping technique, partial capacitance technique, and dielectric mixing model. Secondly, numerical simulations were performed to validate the developed theoretical model under two scenarios. Thirdly, laboratory experiments were conducted to measure capacitance data and predict density for three common types of ACs. Finally, the error of non-contact ICCS was discussed and compared with that of other NDT instruments. The results indicated that the developed theoretical model could establish the relationship between AC density and capacitance value, which provided the basis for AC density measurement using non-contact ICCS. It was also observed that the capacitance data obtained by the proposed model closely matched those computed by numerical simulations. The maximum errors between theoretical and numerical data were 4.10% and 1.08% for two cases respectively, which proved the feasibility of the theoretical model. Furthermore, experimental results indicated that the capacitance value of AC specimens increased by about 1 pF as the rolling time increases due to a decrease in the volume of air in AC, which reaffirms the validity of AC density measurement using non-contact ICCS. Additionally, the non-contact ICCS demonstrated excellent accuracy with a maximum error of 6.69%, which is comparable to the current mainstream NDT approaches. And it has the advantages of lower cost, simpler data processing and higher efficiency. The findings of this paper offer a new and reliable method and tool for non-destructive and large-scale AC density measurement.

Autonomous longitudinal separation control of aircraft in random wind fields based on MPC

Aiming at the problem of autonomous longitudinal separation under random disturbance of flight path, the random factor of high-altitude wind often leads to poor robustness of longitudinal separation between two aircraft. This paper proposes an autonomous longitudinal separation control method for aircraft based on model predictive control. Firstly, the nonlinear kinematic differential equation of the wind field difference between the two aircraft and the longitudinal separation is established, and the linear time-varying prediction model is derived. The longitudinal separation and path deviation distance of the two aircraft are selected as the optimization objectives, the high-altitude wind is the random disturbance, and the true airspeed and yaw angle of the leading aircraft are measurable. Terminal equality constraints are added to the air safety and aircraft performance constraints to maintain the stability of the system. To verify the effectiveness of the algorithm, within the prescribed simulation time of 120 seconds, this paper sets three groups of different expected intervals 12, 13, 14km. Through the designed MPC controller, by controlling the true airspeed and yaw angle of the trailing aircraft within the rolling time domain period, the interval curve between the two aircraft is relatively smooth and is always not less than the minimum safety interval of 10 km. It stabilizes at the expected target interval at the 74th, 90th and 118th seconds respectively, and begins to return to the route at the 58th, 74th and 95th seconds, and finally returns to the center line of the route; two groups of wind field control groups are set up, one group is the forecast wind intensity increased by 2 times, and the other group is the turbulent wind disturbance intensity increased by 8 times. Both can smoothly and stably establish the expected interval of 12 km at the 61st and 72nd seconds respectively.

ANALYSIS AND STUDY OF ROLLING PARAMETERS OF COILS ON AN INCLINED PLANE

In the article, the patterns of movement of rolls of long-fiber plant crops on an inclined plane are investigated. Experimental data on determining the rolling time of rolls on an inclined plane with angles of inclination of 25° and 10° to the horizon for rolls of different mass and radius are processed. An analysis and investigation of the patterns of movement of these rolls have been carried out, including angular velocity, velocity of roll centers, rotation angle, and kinetic energy of the rolls.

The evolution of tire-road wear particles and road surface texture under rolling friction

Traffic-related non-exhaust emissions are an important part of air pollution, which can adversely affect human health when inhaled. Tire-road wear is a significant source of these emissions and originates from tire-road friction. To gain a deeper understanding of the relationship between tire-road wear particles and road surface texture under rolling friction, this study conducted long-term durability experiments using a Model Mobile Load Simulator at 1/3rd scale to form a closed environmental test system. The experiments were conducted under 30°C, 50 % relative humidity, 0.7 MPa tire-ground pressure, and 5.4 km/h rolling speed. Based on an analysis of asphalt concrete-13 Marshall samples’ British Pendulum Number (BPN) value, Mean Texture Depth (MTD), wear particle size distribution, particulate matter (PM) concentration, and thermogravimetric analysis during 1600,000 rolling times, this study draws the following conclusions: This indoor research has indicated that the weight ratio of tire and asphalt wear to aggregates and mineral fillers is approximately 4.5:5.5 (after stabilization). When the tire contacts the aggregate exposed after asphalt film peeling, it significantly increases PM10 concentration. Under the rolling friction of the tire-road contact, the quality loss of asphalt concrete follows an exponential decay curve with an R2 of 0.99. Road (asphalt concrete pavement) wear per square meter of tire-road contact was 5.32×10^−4 g and 2.13×10^−4 g (before and after asphalt film peeling). It is hoped that this study will contribute to the field of tire-road wear, including non-exhaust emissions, and provide methods and theoretical support for mitigating re-suspension dust.

Real-time voltage control of centralized and distributed coordination in active distribution network under multiple coupling

Abstract At present, the “source-load interaction” distribution network operation mode has gradually replaced the “source-load operation” mode. The uncertainty and volatility of the distribution system have been enhanced, the controllability has been weakened, the voltage fluctuations have been frequent, and the power quality has been decreased. Voltage regulation is crucial to fully absorbing new energy generation and improving the operation safety of the active distribution network. To solve the problem of real-time control of distribution networks, this paper proposes a distributed voltage control method of active distribution networks with global sensitivity, establishes a distributed communication mechanism, and builds a distributed forward push-back communication rule to transfer power and voltage information between nodes. The global sensitivity of voltage, active power, and reactive power is used for real-time, independent, and effective control of multiple devices at each node, and the distributed control process is simulated according to actual production conditions. Furthermore, the centralized and decentralized real-time voltage control methods are proposed, and the centralized and distributed methods are implemented in medium voltage primary distribution network (MVPN) and low voltage secondary distribution network (LVSN), respectively, to play the advantages of high computing efficiency and fast control response. Based on model predictive control (MPC), intra-day rolling time scale voltage regulation is constructed, and second-level control is used to adapt the MPC results, finally promoting the optimal scheduling and rapid response of various controllable flexible resources in the multi-level active distribution network.

A humidity-driven film with fast response and continuous rolling locomotion

The widespread availability and easy accessibility of humidity make it essential to explore simple and low-cost methods to prepare structurally and materially efficient humidity-driven actuators with spontaneous and continuous locomotion to facilitate the application of bionic devices, intelligent sensors, and soft robots. Herein, this paper reports a humidity-driven monolayer film actuator. It is self-assembled from agarose (AG) and graphene oxide (GO) modified with polyvinylpyrrolidone (PVP), referred to as PGO, using a simple casting method. The PGO/AG composite film actuator exhibits an ultra-fast humidity response time (124.58° s−1) and recovery time (18.87° s−1), enabling rapid rolling locomotion driven by water evaporation. Notably, the programmable surface patterning of the PGO/AG composite film actuator with graphite results in a shorter single rolling time (0.43 s) and a faster rolling speed (28.73 mm s−1). Based on these advantages, the PGO/AG film actuator can mimic the opening and closing of flowers, mimosas, and fast-crawling robot driven by periodic water vapor (9.9 body length min−1). This finding provides new insights into the design of multifunctional actuators based on humidity-driven films.

The Effect of the Size of SUS316L Flake Powder on the Characteristics of Metal Powder Filt ers with a Double-Layered Pore Structure Fabricated by the WPS Process

When fabricating a metal powder filter with a double-layered pore structure, which includes a microporous layer, by coating flake-shaped powder using the Wet Powder Spray (WPS) process, it is crucial to ensure that the coating is applied parallel to the surface of the metal powder filter. Failure to do so can result in a non-uniform pore structure, leading to a rapid decline in particle filtration efficiency. Therefore, it is necessary to improve the alignment of the coated flake-shaped powder on the filter surface. In this study, a metal powder filter with a double-layered pore structure was fabricated by applying a flake-shaped metal powder coating to a tube-shaped SUS316L powder filter using the WPS process. In order to improve the flake-shaped powder orientation of coating layer, the impact of the rolling process was investigated. The characteristics of the pores were analyzed based on the powder size and the rolling time. The microstructure of the fabricated metal powder filter was observed using an optical microscope, surface roughness, variations in thickness, and air permeability were analysed.

Rolling discrete grey periodic power model with interaction effect under dual processing and its application

Time series characterized by periodic fluctuations are widespread in reality behavioural systems, and accurate forecasting of them has important practical application value. In this paper, based on the idea of discrete grey model, dual interaction effects and the nonlinear characteristics of the system are considered under the perspective of dual processing, and the fluctuating trend of the periodic sequence is portrayed through the introduction of the periodic dummy variable, and at the same time, combined with the extended periodic rolling time window method, the rolling discrete grey periodic power model with the interaction effect under dual processing is proposed, namely DIE-RDGM(1,1,T)α model. Differential evolution algorithm (DE) are employed to determine the optimum power index. Finally, the new model was compared with nine other forecasting models by three instances and applied to the problem of agricultural drought forecasting in Zhoukou City and Anyang City, Henan Province. The results show: (1) The new model has demonstrated strong competitiveness, confirming its rationality, validity, and superiority. (2) The DE is suitable for power index optimization search. (3) The new model can better describe the periodic change rule of seasonal-scale soil moisture in the two cities, and the forecast results are consistent with the actual drought conditions.

The effects of myofascial release in combined with task-oriented circuit training on balance in people with Parkinson’s disease: a randomized pilot trial

ABSTRACT Objectives Task-oriented circuit training (TOCT) has been used to improve balance in people with Parkinson’s disease (pwPD). To investigate the effectiveness of TOCT on balance, quality of life, and disease symptoms when combined with myofascial release in pwPD. Methods Twenty-six pwPD were randomized into two groups for this randomized controlled study. The groups received TOCT three days a week for eight weeks. At the end of each session, the myofascial release was applied to the neck, trunk, and lumbar region with three sets of 60-s foam rolling body weight (Intervention group-IG) and perceived discomfort level 0/10 (Control group-CG) using a numeric rating scale. Primary outcome measures were measured by the Berg Balance Scale (BBS), Parkinson’s Disease Questionnaire (PDQ-8), and Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Secondary outcome measures included posturographic assessment, timed-up and go test (TUG), Trunk Impairment Scale (TIS), and rolling time. Results Data obtained from 26 pwPDs in equal numbers in both groups were analyzed. All groups reported a significant change in MDS-UPDRS, MDS-UPDRS-III, PDQ-8, TIS, and rolling time after treatment compared to pretreatment. Post-hoc analyses showed that IG significantly improved motor symptoms, TUG, and TIS dynamics compared to CG. The mediolateral limits of stability and anterioposterior limits of stability distances of IG increased (p < 0.05). Discussion Myofascial release, when combined with TOCT, may help to reduce disease-related motor symptoms and improve dynamic balance in pwPD. These findings suggest that myofascial release can be a beneficial addition to TOCT programs for pwPD. Clinical Trial Number: NCT05900934 (ClinicalTrials.gov).

An optimization framework for efficient and sustainable logistics operations via transportation mode optimization and shipment consolidation: A case study for GE Gas Power

General Electric (GE) Gas Power, a leading manufacturer of gas and steam turbines, manufactures and installs these turbines in power generation plants worldwide. They source components for these turbines from suppliers globally and transport these components to manufacturing and assembly locations in the United States using various modes of transportation, including ocean, air, and ground. These transportation options have different lead times and costs. The challenge lies in identifying the most cost-effective solution that meets the assembly requirements, given the high volume of shipments and the complexity of the intermodal freight network. To address this challenge, a customized, multi-period (dynamic), multi-commodity network flow model and a novel heuristic approach with a rolling time horizon are developed. This model incorporates consolidation and storage options at intermediate nodes, allowing the business to optimize its shipments. This research addresses a unique real-world logistics problem and offers an optimization model together with an efficient solution approach for industry. Computational experiments indicate that the heuristic approach proposed can yield results closely aligned with those produced by state-of-the art commercial solvers. Furthermore, the experiments show the criticality of full container load shipments in achieving substantial cost-saving in global shipments.

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

Research on Continuous Casting–Hot Rolling Scheduling Model Involving Reheating Furnace Conversion Mode and Improved Bat Optimization Solution Algorithm

As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit.

Collaborative optimization decision making of cement grinding process operational indicators considering dual dynamic problem

The cement grinding process requires monitoring of unit power consumption and specific surface area indicators to improve production efficiency and product quality. The strong coupling between operational variables necessitates careful adjustment of these variables to prevent production from becoming instability. Furthermore, the continuous and time-dependent nature of the process makes this adjustment particularly challenging. To address this dual dynamic problem, we have developed a multi-objective optimization model, with global optimization as the desired outcome, to optimize unit power consumption and specific surface area. Our optimization algorithm, termed “Optimization Algorithm - Dynamic Search Space and Rolling Time Domain” (OA-DSTR), considers the problem of dynamic production process. First, the algorithm uses a dynamic search space strategy, allowing for changes in the constraint range of the operational variables and introducing a fluctuation coefficient (FC) to measure solution rationality. Second, to deal with the time-dependent problem, we have added a rolling time domain strategy, enabling real-time monitoring of the cement grinding process. The experimental results show that OA-DSTR not only ensures global optimization, but also realizes the tracking of dynamic conditions, improving the stability of the cement grinding process and achieving a lower FC value.

Clinical Efficacy of Application-Linked Stretching Ball as Digital Therapeutics in Plantar Fasciitis.

Background/Objectives: This study aimed to evaluate the efficacy of application-linked stretching ball instruments that record the rolling time and force of patients compared with a traditional simple stretching ball. Methods: Fourteen participants with plantar fasciitis were divided into a simple massage ball group (group A, n = 8) and an application-linked massage ball group (group B, n = 6). The application-linked massage ball sends information regarding the massages, such as the frequency and force of the massage on the foot, to the application on the patient's smartphone. All clinical outcomes were evaluated at the beginning of the study and 1-, 2-, and 3-month follow-up. The primary outcome measure was the Manchester-Oxford Foot Questionnaire (MOXFQ) score. Results: At the beginning of the study, the initial MOXFQ score was not significantly different between the two groups (p = 0.948). At each time point, the MOXFQ score of the whole population did not improve significantly compared to that of the initial state (p = 0.131). Generalized estimating equation modeling demonstrated that there was no significant difference in the improvement of the MOXFQ score between groups A and B during follow-up (p = 0.826). In addition, no group-by-time interactions were observed (p = 0.457). Conclusions: The efficacy of an application-linked massage ball for the treatment of plantar fasciitis was not as definite as that of a traditional simple stretching ball in patients whose symptoms persisted for at least six months. Future studies that include patients with acute plantar fasciitis are required.

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning.

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive-evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load Variation

To address the issue of centrifugal force affecting the vertical load during the stability and trajectory planning of autonomous vehicles during high-speed cornering and obstacle avoidance, a model predictive control of trajectory planning and tracking is proposed that considers the roll factor using only a two-degrees-of-freedom vehicle dynamics model. Firstly, a trajectory planning controller is designed. As a predictive model, a dual-track two-degrees-of-freedom vehicle dynamics model is established. This model describes the relationship between tire lateral forces and vertical loads using a quadratic nonlinear tire model. To reflect the actual dynamic state of the vehicle, the controller incorporates a nonlinear constraint that considers vertical load variations. The nonlinear optimization problem is transformed into a simplified quadratic programming problem by using the Jacobian matrix method to linearize the constraints. By fitting a fourth-degree polynomial curve to the discrete points calculated by the replanning algorithm, an optimal collision-free trajectory is obtained. Secondly, an MPC trajectory tracking controller is designed to control the vehicle in real time along the optimal trajectory from the planning, incorporating control quantity constraints, control increment constraints, and lateral angle constraints to maintain the vehicle’s motion state. We transform the trajectory tracking control problem into a quadratic programming problem, solving for the optimal control sequence for the autonomous vehicle to track the trajectory, achieving an optimized solution and rolling time domain control. Finally, the effectiveness of the vehicle’s obstacle avoidance planning and tracking under high-speed double-lane-change maneuver conditions is validated using the Simulink simulation platform. The results indicate that the designed planning and tracking controllers effectively improve the obstacle avoidance planning and tracking control for high-speed autonomous vehicles.

Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders

This study investigates coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence condition based on experiments involving real biological groups, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled-force Cucker–Smale model with informed leaders. Incorporating informed leaders into the leader-follower group model enables a more accurate representation of biological group behaviors. The paper then extracts the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. Employing time slicing and rolling time windows, the study methodically analyzes group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network’s superior performance. The Kolmogorov-Smirnov test demonstrates that the FLH network conforms to a power-law distribution, a prevalent law in nature. This result further illuminates the crucial role that power-law distribution plays in the evolutionary processes of biological communities. This study offers new perspectives on the evolution of biological groups, contributing to our understanding of the behaviors of both natural and artificial systems, such as animal migration and autonomous drone operations.

First train timetabling and passenger transfer routing problems in urban rail transit networks

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations.

Decomposing risk spillover effect in international stock market: A novel intertemporal network topology approach

This paper investigates the intertemporal risk effects in global stock markets using a novel network topology based on relative importance analysis. The rolling time window approach identifies dynamic and asymmetric risk spillovers. The results reveal complex and asymmetric intertemporal risk spillovers in international stock markets. Europe and America are the main risk transmitters. Countries in the forecast period, receive more risk from the international stock market. Major events that generate stock market turbulence will dramatically increase intertemporal risk spillovers. These findings have implications for risk management and the stability of the international stock market.