Amylose is a promising carrier for bioactive compounds, offering potential for controlled release of α-linolenic acid (ALA). This study investigated the effect of complexation temperature on the helical structure and properties of amylose-ALA complexes. Amylose, obtained by debranching waxy maize starch, was complexed with ALA at 30-90 °C. The solid-state nuclear magnetic resonance analysis showed that complexes prepared at 30 °C contained a higher proportion of single helical structures compared with those formed at elevated temperatures. These complexes also exhibited the highest complexation efficiency (>56%), smallest particle size (0.57 μm), greatest dispersion stability, and highest melting temperature (83.1 °C). Molecular dynamics simulations showed that higher complexation temperatures preferentially strengthened amylose-amylose interactions over amylose-ALA interactions, shifting the balance of competitive interactions and reducing complexation efficiency. These results demonstrate that complexation temperature governs structural organization and inclusion behavior through changes in intermolecular interactions.

Over the past decade, urban transportation infrastructure has experienced rapid expansion. However, due to variations in deployment time and environmental conditions, noticeable discrepancies have emerged in both the operational status and data quality of traffic sensors, which severely hinder the deployment, development, and in-depth exploitation of traffic data. To address these challenges, this study proposes an evaluation and optimization framework for multi-source urban traffic sensing data. By integrating data-cleansing algorithms, the framework enables simultaneous data quality analysis and operational status assessment of heterogeneous sensors. A one-year empirical study was conducted using flow data collected from 91 detector stations across the urban road network. The framework successfully evaluated the operational status of all sensors and achieved a recovery accuracy of 97.47

Polysaccharide-based hydrogels have been utilized as flexible strain sensors because of their renewability, biocompatibility, and biodegradability. However, their widespread application is hindered by the complexity of their manufacturing processes and the inevitable degradation of their mechanical properties with repeated use. The introduction of reversible bond chemistry offers the potential to impart self-healing properties to hydrogels, extending their functional lifespan. In this study, we prepared a starch-based conductive hydrogel (starch/poly(vinyl alcohol) (PVA)/cellulose nanocrystals (CNCs)) via a straightforward method using borax as a cross-linking agent. The hydrogel demonstrated improved strength and self-healing property because of the addition of CNCs, which formed dual reversible cross-links with starch and PVA via hydrogen and borate ester bonds. Additionally, the sodium ions (Na+) and borate ions (B(OH)4-) within the network enhanced the electrical conductivity and strain sensitivity of the hydrogel. The resulting hydrogel demonstrated potential for application as a wearable sensor capable of monitoring a range of human movements, sensing handwriting, and enabling Morse code communication. Notably, the hydrogel could be easily remolded at room temperature after being sectioned, highlighting its practical applicability. This work expands the scope of the use of starch-based hydrogels in sustainable wearable sensor technologies.

Pedestrian detection in crowded situation is a challenging task. This study presents a straightforward and effective method called Det RCNN to detect pedestrians in crowded situation, while also pairing the body and head of individual pedestrian. On the one hand, pedestrians' heads have their characteristics of stable shape and distinct feature. On the other hand, their heads are usually positioned higher in image, so even in crowded situation, it is difficult to completely cover the pedestrians' heads. Therefore, this study equipped the DETR model with a Head Decoder (HDecoder) parallel to the Decoder. HDecoder takes the head knowledge generated in the Decoder phase as head queries. Simultaneously, the HDecoder uses a key-query mechanism to search the entire image for the body bounding boxes corresponding to the head queries. Lastly, the proposed method conducts a straightforward IOU (Intersection over Union) matching between the body bounding boxes produced in the Decoder and HDecoder phases. This HDecoder resembles the second stage of the Faster RCNN model, hence this paper termed it Det RCNN (DETR RCNN). Compared to Deformable DETR, the experimental results on the CrowdHuman dataset show that the proposed model can increase AP $_{m}$ from 53.02 to 53.87. Furthermore, the mMR $^{-2}$ decreased from 52.46 to 42.32 compared to the existing BFJ. The code and experiments will soon be open-sourced at https://github.com/zefeichen/Det-RCNN .

Demand-responsive transit (DRT) is a flexible public transportation mode offering affordable door-to-door services. However, its widespread adoption still faces large hurdles such as demand variability, immediacy, and financial sustainability. Most DRT studies focus on fleet management, often leading to underutilization of capacity due to passenger spatial dispersion. This issue calls for multi-objective optimization for both service coverage and cost efficiency. This study proposes a dynamic DRT scheduling problem that integrates vehicle-passenger coordination and time-dependent travel times, optimizing fleet management by leveraging passengers’ spatial and temporal flexibility. We propose a multi-objective optimization model within a rolling horizon framework to optimize vehicle routing, departure times, and passenger assignment, with dual objectives of maximizing profit and minimizing passenger spatiotemporal displacement. To solve this problem efficiently, we develop a dynamic multi-objective Memetic algorithm entailing three salient features: 1) distinguishing static and dynamic phases while identifying similar environments by comparing the scheduling records in the environment and the updated request pool; 2) using memory-based environment inheritance to accelerate multi-period decision-making; 3) developing a heterogeneous elite selection strategy during iterations to address the issues of speeding proliferation in dynamic problems. Our approach is validated through a real-world case study in Nansha District, Guangzhou, China. Results show that our algorithm performs comparably to benchmark algorithms in both solution quality and efficiency, and outperforms advanced methods across multiple metrics. Managerial insights are also provided.

In this study, a biodegradable and self-healing starch-based hydrogel (starch/polyvinyl alcohol (PVA)/chitosan/ borax) was synthesized via a one-pot method. The effects of the chitosan concentration on the resulting hydrogel properties were systematically investigated. An increase in the chitosan concentration increased the gel fraction and decreased the swelling ratio. The addition of chitosan and borax significantly increased the strength of the hydrogel, as the maximum storage modulus increased by 10 times, from 441 Pa to 4276 Pa. In addition, the hydrogel showed self-healing ability and thermal responsiveness due to the dynamic borate ester between the hydroxyl groups of PVA and starch and the hydrogen bonding between the hydroxyl or amino groups of chitosan and the hydroxyl groups of starch and/or PVA. The synergistic dual reversible crosslinking of hydrogen bonds and borate ester bonds imparts hydrogel with self-healing capabilities. The hydrogel demonstrated excellent recovery behavior under continuous step strain. Furthermore, the prepared hydrogels are prone to biodegradation in activated sludge, with a relatively high degree of biodegradation between 23 and 32 % after 28 days, demonstrating their good biodegradability. We expect that the present research could broaden the application of biodegradable starch-based hydrogels, such as those used in agriculture and sensors.

This paper classified and defined traffic flows from various directions to achieve a refined estimation of vehicle inflow rates at the downstream intersections. Using sampled trajectory data from these flows, the paper analyzed vehicle arrivals, selected an exponential distribution as the prior distribution, and constructed the likelihood function for vehicle arrivals. The Maximum A Posteriori (MAP) estimation method was then applied to estimate the traffic arrival rate. Results have indicated that the Mean Absolute Percentage Error (MAPE) for arrival rate estimations from the proposed model has fluctuated around 13

Existing arterial signal coordination predominantly adopts physical traffic performance indicators or alternative progression bandwidth as optimization objectives, which lack a constraint mechanism between traffic efficiency and the environmental impacts associated with signal parameters. This study proposes a novel arterial ecosignal coordination approach to minimize gas emissions, energy consumption, and passenger delays by leveraging vehicle trajectory data. Initially, a vehicle kinematic analytical model incorporating driving behavior is developed to extract second-by-second individual vehicle trajectories. Through the analysis of micro-trajectory data and comparison with the evaluation metrics from VISSIM simulator, the model is validated to accurately simulate vehicle operating conditions. Subsequently, an exact ecological cost unit, encompassing gas emissions, fuel consumption, and delays for all vehicles by explicitly accounting for both electric and conventional fuel vehicles, is calculated based on the trajectory data. This cost unit is then selected as the objective function of the optimization problem, which is formulated as a bi-level model. Numerical experiments on a five-intersection arterial segment demonstrate that the proposed ecosignal coordination can significantly enhance environmental benefits at the cost of a small amount of delay growth compared to existing methods.

Driven by advances in artificial intelligence, deep reinforcement learning (DRL) has made remarkable strides in adaptive traffic signal control (ATSC), empowering improved handling of fluctuating traffic volumes and congestion. However, in most existing studies, trained agents exhibit poor transferability in scenarios with varying vehicle turning ratios, and the switching rules for the signal stage sequence do not align with the actual traffic demands. To address these issues, this paper presents action masking based proximal policy optimization with the dual-ring phase structure (AMPPO-DR), a novel ATSC model based on DRL that can simultaneously optimize the stage sequence and duration. Specifically, we consider the correlation between states and actions and utilize intersection channelization to predict vehicle turning directions. Moreover, we define the action as selecting the next green stage and establish variable-stage-sequence constraint rules on the basis of the dual-ring phase structure. To satisfy the constraints on the stage sequence, we propose the AMPPO algorithm, which dynamically adjusts the policy network outputs to mask invalid stages in real time. The simulation experiments demonstrate that the proposed method can effectively adapt to changing turning flows, enabling flexible and rational stage switching and ultimately increasing traffic efficiency.

This paper presents a priori knowledge-based low-light image enhancement framework, termed Priori DCE ( Priori Deep Curve Estimation). The priori knowledge consists of two key aspects: (1) enhancing a low-light image is an ill-posed task, as the brightness of the enhanced image corresponding to a low-light image is uncertain. To resolve this issue, we incorporate priori channels into the model to guide the brightness of the enhanced image; (2) during the enhancement of a low-light image, the brightness of pixels may increase or decrease. This paper explores the probability of a pixel’s brightness increasing/decreasing as its prior enhancement /suppression probability. Intuitively, pixels with higher brightness should have a higher priori suppression probability, while pixels with lower brightness should have a higher priori enhancement probability. Inspired by this, we propose an enhancement function that adaptively adjusts the priori enhancement probability based on variations in pixel brightness. In addition, we propose the Global-Attention Block (GA Block). The GA Block ensures that, during the low-light image enhancement process, each pixel in the enhanced image is computed based on all the pixels in the low-light image. This approach facilitates interactions between all pixels in the enhanced image, thereby achieving visual balance. The experimental results on the LOLv2-Synthetic dataset demonstrate that Priori DCE has a significant advantage. Specifically, compared to the SOTA Retinexformer, the Priori DCE improves the PSNR index and SSIM index from 25.67 and 92.82 to 29.49 and 93.6, respectively, while the NIQE index decreases from 3.94 to 3.91.

To seek the best traffic management and control method to alleviate traffic congestion under sudden traffic accidents on highways, the paper proposed a method for toll station entrance control aimed at accident bottlenecks. Firstly, an modified METANET model was used to predict traffic density and flow of highway network. Taking the storage space of ramp and toll plaza, the traffic capacity of road section as constraints, and aiming to minimize the travel time on the main line and the waiting time at toll station, an optimal control rate model for toll station entrance was established. The particle swarm optimization algorithm (PSOA) was used to solve the optimal control rate at the toll station entrance, which was substituted into the established toll station channel control model to obtain the control scheme for each toll station channel. Finally, the VISSIM simulation software was applied for secondary development, and case simulations were conducted to verify the proposed method can reduce vehicle delays and congestion duration time. After accident cleaning, the vehicles could quickly dissipate, which reached a ideal effect.

In the context of digital road networks, lane-level data support and dynamic traffic perception enable the precise calculation of operational parameters for different lanes during various time periods. Delay metrics play a crucial role in evaluating traffic states. The delay time index defined in this paper intuitively reflects traffic delay states and eliminates the influence of network scale on delay assessment, making it an appropriate evaluation metric. By establishing relationships among the indices at different levels of the road network through weighting, and considering factors such as traffic flow and free-flow travel time, this paper presents definitions and calculation methods for the weight coefficients of various evaluation objects within the network. Based on the constructed weight system, a multi-scale delay time index calculation method based on weight coefficients is derived. Case results indicate that the traffic states evaluation metrics obtained through weight coefficient estimation are consistent with those derived from simulation and defined calculations. Additionally, when the data acquisition rate for the road network is 10

The network efficiency and real-time power of the urban rail transit traction power supply system (TPSS) are closely linked to train speed and output power. Collaborative optimization of energy-efficient train control (EETC) and railway systems can reduce TPSS-level energy consumption. This paper proposes a high-accuracy, high-efficiency EETC model integrated with TPSS-train integration to minimize TPSS energy, utilizing a shrinking horizon model predictive control (SHMPC) framework. The proposed iterative model uses spatial-to-temporal domain conversion to update the train’s future operational states and network topology. By optimizing spatial discretization and iteration count, the model reduces solution deviations caused by speed limit and time discrepancies, enhancing its accuracy. With a minimum operation time of 0.066 s per iteration. The result reveals that less mechanical energy does not necessarily equate to less traction energy sourced from the TPSS. Compared to the distance-based EETC model, the proposed model achieves an 11.74% savings rate in traction energy from the TPSS. Within this, the proportion of traction energy loss is 8.53%, indicating less traction energy loss than the total energy loss incurred by the model without considering TPSSs-train integration.

This study develops an integrated framework combining computer vision and traffic simulation for optimizing traffic management in high-density urban commercial areas. The framework employs a two-phase data acquisition approach: UAV-captured video is first processed using an enhanced YOLOv10 algorithm to identify critical road segments and key intersections, followed by handheld video recordings at target intersections for extracting dynamic traffic parameters (including vehicle counts, speeds, pedestrian density, and mixed-traffic interactions). The proposed framework was applied to the Xianlie East Road-Lianquan Road intersection in Guangzhou, a conflict-prone hub adjacent to densely clustered garment wholesale markets. Key improvement measures evaluated include crosswalk relocation and non-motorized lane adjustments. Additionally, the signal cycle duration was extended from 80 to 90 seconds to alleviate phase transition conflicts. Simulation inputs integrate field-observed behavioral patterns (e.g., 83% pedestrian compliance with signals). Results demonstrate significant improvements: 16.7% reduction in eastbound queue lengths (240.23m), 80.4% decrease in vehicle delays (56.46s), 44.8% shorter travel times (51.61s), and 83% fewer pedestrian-vehicle conflicts. This approach provides a scalable technical pathway for adaptive traffic governance in complex urban environments.

An algebraic method of arterial progression based on the green center line was presented in this study, which optimized the common signal cycle, phase sequence of each intersection, and offsets by seeking the ideal intersection positions with a small bias distance from the actual intersection positions. Since the bias split would determine the progression bandwidth and impact the progression effect directly, this method took the maximum difference of all bias splits as an evaluation index for scheme optimization. This method was applied to obtain an arterial progression for the 21 signalized intersections on Huanshi West Road in Nansha District, Guangzhou. Compared with the MAXBAND, MULTIBAND model, and the Synchro software, the VISSIM simulation results showed that the algebraic method achieved the best coordinated control effect for the through vehicles along the artery. The schemes obtained by the MULTIBAND model and algebraic method achieved the minimum delay and number of stops for all through vehicles on each road section.

The biocompatibility and renewability of starch-based hydrogels have made them popular for applications across various sectors. Their tendency to incur damage after repeated use limits their effectiveness in practical applications. Improving the mechanical properties and self-healing of hydrogels simultaneously remains a challenge. This study introduces a new self-healing hydrogel, synthesized by grafting acrylamide onto starch using ceric ammonium nitrate (CAN) as an initiator, followed by borax cross-linking. We systematically examined how the starch-to-monomer ratio, borax concentration, and CAN concentration impact the grafting reactions and overall performance of the hydrogels. The addition of borax significantly reinforced the strength of the hydrogel; the maximum storage modulus increased by 1.8 times. Thanks to dynamic borate ester and hydrogen bonding, the hydrogel demonstrated remarkable recovery properties and responsiveness to temperature. We expect that the present research could broaden the application of starch-based hydrogels in agriculture, sensors, and wastewater treatment.

Abstract In pedestrian detection task, numerous predicted boxes and their corresponding scores are generated and these scores are used to filter these predicted boxes by non‐maximum suppression. This paper analysed the training process of the popular anchor‐based pedestrian detection models (e.g. YOLO and Faster RCNN), and found that the score of the predicted box reflects the overlap between the corresponding anchor and the ground truth, rather than the predicted box itself. Due to the many‐to‐one strategy adopted by anchor‐based methods, multiple predicted boxes could be generated around one predicted box. This study refers to the number of other predicted boxes around the target predicted box as its local density. When a predicted box has a higher local density, it should have a greater overlap with the ground truth. Therefore, this study proposed the fused score by introducing local density into the score. The experiments showed that replacing the score with the fused score can effectively improve the model's detection accuracy. The code and experiments will soon be open‐sourced at https://github.com/zefeichen/FusedScore.

This paper presents a synchronization-constraints-based dual bands method of traffic signal optimization for multiple traffic flows. The synchronization coefficient, traffic volume ratio, synchronization benefit, and their thresholds of traffic flow pair are proposed to select synchronized traffic flow pairs for progression. Then, progression bands of multiple traffic flows can be achieved by merging time windows, combining equivalent phase, and optimizing benefits evaluation index of the synchronized traffic flow pair. The proposed method adopts step-by-step optimization processing, and the combinatorial optimization of signal cycle, phase sequence, and offset is decomposed by performing synchronization and progression. A real-world case is presented to illustrate the application of the proposed method with VISSIM simulations. The results show that compared with the MULTIBAND model and TRANSYT, this method significantly decreased the average delay and average stops along the arterial of the entire traffic system composed of buses and cars.

Herein, we simultaneously prepared borax-crosslinked starch-based hydrogels with enhanced mechanical properties and self-healing ability via a simple one-pot method. The focus of this work is to study the effects of the amylose/amylopectin ratio of starch on the grafting reactions and the performance of the resulting borax-crosslinked hydrogels. An increase in the amylose/ amylopectin ratio increased the gel fraction and grafting ratio but decreased the swelling ratio and pore diameter. Compared with hydrogels prepared from low-amylose starches, hydrogels prepared from high-amylose starches showed pronouncedly increased network strength, and the maximum storage modulus increased by 8.54 times because unbranched amylose offered more hydroxyl groups to form dynamic borate ester bonds with borate ions and intermolecular hydrogen bonds, leading to an enhanced crosslink density. In addition, all the hydrogels exhibited a uniformly interconnected network structure. Furthermore, owing to the dynamic borate ester bonds and hydrogen bonds, the hydrogel exhibited excellent recovery behavior under continuous step strain, and it also showed thermal responsiveness.