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Ultrasound, as an efficient, clean, and safe processing technology, is widely used in industries. Grain refiners are widely used in the refinement of cast alloys. In this study, ultrasonic technology (UT) and Al–5Ti–1B refiner prepared by continuous rheological extrusion (CRE Al–5Ti–1B) were used to modify the recycled Al–Mg–Si-based alloy, and the microstructure and mechanical properties of the recycled Al–Mg–Si-based alloy were studied. The differences in porosity and grain refinement caused by ultrasonic power were discussed. Results showed that, increasing the ultrasonic power can effectively reduce the porosity of the recycled Al–Mg–Si-based alloy. The synergistic effect of UT and CRE Al–5Ti–1B refiner significantly refined α-Al grains and second phases. The increase of ultrasonic power can effectively cause deagglomeration while reducing the nucleation undercooling of α-Al, which promotes the refinement of the recycled Al–Mg–Si-based alloy and significantly improves the mechanical properties. The yield strength (YS), ultimate tensile strength (UTS) and elongation (EL) of the recycled Al–Mg–Si-based alloy were 106 MPa, 210 MPa and 9.4%, respectively. When 0.2 wt% CRE Al–5Ti–1B refiner was added and the ultrasonic power was 150 W, the YS, UTS, and EL were improved by 12.8%, 28.8% and 228.4%, respectively.

In recent decades, grain refiners had not only improved the properties on cast aluminum alloy, but also changed the application field of cast aluminum alloys, which had attracted widespread attention. However, there were few reports on microstructure evolution of A356/CRE Al-X-Ce(Ti, C) system by combining traditional grain refiners with element Ce. In this study, refined A356 alloys with excellent strength and plasticity were obtained by adding CRE Al-X-Ce(Ti, C) (0, 0.4, 0.8, 1.2, 1.6 and 2.0 wt%, respectively). Using XRD, SEM and TEM, the morphology of phases in the refined A356 alloy and phases of grain refiner were identified. The microstructure evolution of α-Al grains and eutectic Si with the introduction of TiC and Ce was analyzed. The results showed that the novel CRE Al-X-Ce(Ti, C) could not only effectively refine α-Al grains, but also modify Si microstructure of the alloy. The refined A356 alloys with excellent strength and plasticity were obtained with 2.0 and 1.6 wt% grain refiner (CRE) additions, respectively. TiC as nucleation particle and Ce as the surfactant refined the α-Al grains. Also, Ce modified eutectic Si by changing the way it grows. When the CRE Al-X-Ce(Ti, C) content researched 2.0 wt%, the long plate-like Al–Si–Ce phase was formed which can easily caused cracks and reduced the elongation of alloy.

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well-designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance-constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network.

The catalyst intrinsic area-specific activity for the oxygen reduction reaction (ORR) was constrained by the scaling relations governing the adsorption of reaction intermediates. In this study, we strategically modified the electronic band structures of Pt(CuNi)x alloy nanoparticles by varying their composition, resulting in a specific activity trend resembling a volcano shape. The introduction of MoOy shattered the existing scaling relations, leading to a significant enhancement in ORR activity of Pt alloys, surpassing the activity of Pt(CuNi)x catalysts. These findings proved the effectiveness of MoOy deposition on Pt(CuNi)x in disrupting the scaling relations, ultimately improving ORR activity.

Abstract This paper develops a distributed cooperative control strategy (DCCS) to plan and optimize the collision‐free trajectory for the connected automated vehicle (CAV) at an isolated roundabout. The representation of the trajectories of CAVs is defined within a roundabout. The central angle and lane radius are used to indicate the location of vehicles, and the corresponding angular velocity is used to describe the vehicle's speed through the roundabout. This trajectory definition method simplifies the proposed problem into two parts: the trajectory optimization of each vehicle in a specific lane and the lane change coordination of CAVs in the roundabout. To solve the trajectory optimization problem, a mixed‐integer linear programming (MILP) model is formulated at the vehicle level to minimize the travel time and make the trajectory as smooth as possible. A model predictive control (MPC) framework is designed to coordinate lane‐change conflicts and push CAVs’ trajectories toward global optimization. The proposed framework integrates the vehicles’ trajectory optimization results so that all CAVs reach a consensus. Numerical experiments test the effect of the strategy in terms of traffic efficiency and mobility performance under different demand pattern scenarios. The results show that the proposed RA‐DCCS (Roundabout‐DCCS) can make all CAVs smooth and conflict‐free trajectories and enable vehicles to execute reasonable lane change decisions.

Autonomous vehicles (AVs) have the potential to improve safety, traffic capacity, and energy efficiency, but these advantages can only be realized when the AV market penetration is sufficiently high. To promote the adoption of AVs, it would be crucial for the government to take policy measures. This paper develops a two-stage model to explore the effects of subsidy and AV lanes’ policies on AV adoption. In the first stage, given the subsidy policy, the vehicle manufacturer sets AV price to pursue maximum profit while anticipating the choice of consumers for AVs or conventional vehicles (CVs), from which the AV market penetration can be accessed. Subsequently, based on the AV market penetration acquired from the first stage model, an optimization model integrating the mixed traffic assignment is developed in the second stage to determine the time-dependent progressive AV lanes’ deployment plan. The first and second stage models are solved using the simulation-optimization and genetic-algorithm-based approaches, respectively. Due to the mutual influence of the two models, an iterative optimization approach is applied to solve the whole model. Two numerical experiments are conducted, and the results demonstrate the positive effects of subsidy and AV lane policies on increasing AV market penetration. The analysis provides significant managerial insights for policymakers to promote the development of AVs.

Based on relevant complex network theory, this paper analyzes the characterization parameters of traffic network complexity, such as the causes of traffic congestion and evacuation models. From the perspective of the traffic capacity of traffic network nodes, combined with the transit time of each road, model is proposed for selecting the weights of congestion evacuation nodes. According to the evacuation target, combined with characteristic parameters, such as node degree, node strength, clustering coefficient and closeness, the grey system evaluation method and the analytic hierarchy process (AHP)are combined. Based on the grey relational analysis model, a model is established for determining the priority connectivity evaluation value of each node. The complex characteristics of the actual traffic network in the Chaoyang District of Changchun City are analyzed, then the selection weights of each node of the traffic network are obtained. Having defined the distance between complex network nodes, a congestion evacuation path selection model is proposed, and an evacuation path scheme is given for specified start and end points.

An efficient freight transportation system is a core part of the modern urban logistics. The transportation system depends on an efficient urban road network. In urban road networks, the failure of some components leads to others failing in succession, triggering cascading failures. This may cause the collapse of the freight transportation system. A cascading failure model is proposed for analyzing the propagation of failures on urban road networks, which can provide guidance for the planning of freight transportation routes in case of emergency. When analyzing the cascading failure process, failed components are commonly deleted permanently. Travelers will avoid failed components, resulting in no increase in traffic flow on the failed components. At the same time, vehicles on these failed components will evacuate slowly to alleviate congestion. Considering this point, this paper proposes a dynamic cascading failure model. This model is established based on a hybrid routing strategy in which traffic flow is distributed according to global and local information. Specifically, the new input traffic demand is assigned along efficient paths in an urban road network (using global information). Efficient paths are determined according to the weights of all edges in urban road networks. Meanwhile, failed edges transfer some traffic flow to their normally working neighbors by self-organization (using local information). The proportion of transferred traffic flow is determined by the residual capacity of the failed edges' normally working neighbors. The feasibility of the proposed model is verified in a partial road network in Changchun, China.

Compared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategic planning and operational decision problem separately while ignoring the complicated interaction between them and the distinct features of autonomous vehicles. It is imperative to determine the relocation and pricing strategies at the operational level. In this study, in terms of the pricing strategy, we formalize a logit model to capture the mode choice behavior in a multimodal network, where the reduced VoT is considered simultaneously. A time-space network is employed to capture the daily operation problem based on the elastic demand. The minimum customer service rate is regarded as a constraint to ensure the system’s reliability. Moreover, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly determine the number of stations and parking spaces, fleet size, relocation, and pricing strategies to maximize the total profit. Then, we integrate the Particle Swarm Optimization (PSO) algorithm with the optimization solver Gurobi to address the complex problem. Numerical experiments and comparative analyses are conducted to demonstrate the feasibility and efficiency of the proposed model.

The work zone on the urban road network will affect the surrounding road traffic. To represent the influence area of the work zone, the concept of a subnetwork is proposed in this paper. Delineating a suitable subnetwork quantitatively is a challenging problem. To address this issue, the node synthesized indexes (NSI) are deployed as a variability measure that captures both the change of link flow and origin-destination (OD) demand. The inertia-based stochastic user equilibrium with the elastic demand (ISUEED) model is proposed to accurately provide the data of link flow and OD demand for the network with the work zone. Correspondingly, the data of the network without a work zone can be obtained by the stochastic user equilibrium (SUE) model. According to the value of NSI, the initial range of the subnetwork is determined. Finally, the connectivity and compactness can be guaranteed by the modified L-shell algorithm. To demonstrate the performance of the method, two case studies and sensitivity analyses are conducted based on the Braess network and the local road network in Changchun, China. The proposed method is beneficial to reduce the complexity of the traffic model by substituting the entire network with a subnetwork.

With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.

Solar-to-fuel conversion under mild conditions offers a renewable means to store energy. Here authors report a Brownmillerite oxide for neutral-pH oxygen evolution that, when integrated with an anodized silver cathode, enables a 13.9% energy-conversion efficiency for light-driven CO2 reduction

This paper presents a real-time dynamic path planning method for autonomous driving to avoid collision with crossing pedestrian on branch streets. The velocity obstacle algorithms are introduced to pick up the collision-free velocities for vehicles. In this method, the curvilinear lane edges are considered as static obstacle while crossing pedestrians and approaching vehicles are considered as velocity obstacles. The paths planning of vehicles are optimized by considering the delay minimum and comfort of drivers under the constraints of appropriate parameters for veer, throttle, or brake systems. A single vehicle's path planning and multi-vehicles 'coordinated or uncoordinated paths planning with crossing pedestrian collision avoidance are experimentally simulated including the longitudinal and lateral motions planning of vehicles. The simulation results demonstrate the effectiveness of the proposed method and indicate its wide practical application on autonomous driving to improve the traffic safety of branch streets.

In this paper, we propose junction conditions for discontinuities due to local perturbation, diverging, merging, and multi-in-multi-out junctions. Traffic flows on junctions can be described by a system of coupled Hamilton-Jacobi equations. At their connection points, it is necessary to propose appropriate junction conditions to close the system. Then, we provide an effective numerical method to compute approximate solutions to these Hamilton-Jacobi equations on junctions. The numerical boundary conditions to close the Hamilton-Jacobi system are also proposed. Numerical tests demonstrate the effectiveness of both the proposed junction conditions and the numerical method.

To support urban sustainable development, cities need innovative green logistics solutions to meet the needs of economic development. Crowdshipping is regarded as a promising sustainable freight solution. This article aims to assess the feasibility of implementing intercity express crowdshipping using private vehicle traveling. The crowdshipping carrier, who drives his or her private vehicle and travels on the intercity route, carries out the express delivery task between the regional cargo center and the dispatch center along the route. The feasibility assessment covers three parts. First, face-to-face interview and stated preference survey are used to investigate the willingness and preferences of express companies and car owners to participate in crowdshipping activities, respectively. Then, it calculates the environmental benefit and economic benefit brought by the crowdshipping mode in comparison with the traditional mode. The results show that the express companies are willing to pay 30 yuan for each car participating in the crowdshipping activities, and there are considerable numbers of car owners to support this crowdshipping activity, and this crowdshipping activity could bring positive impacts on environment as well as corporate economics. It is hoped that the research could enrich the knowledge of the development of green logistics.

The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin-destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link's closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.

A transportation (automotive service) facility location problem is important in urban infrastructure planning and construction. To handle it, researchers have proposed a number of stochastic/random models for locating an automotive service enterprise. However, most of them fail to describe all kinds of uncertainty, e.g., data imprecision. By considering regional constraints, this work proposes a new random fuzzy cost-profit equilibrium model by using uncertainty data and management methods. It presents a hybrid algorithm integrating stochastic fuzzy simulation and particle swarm optimization to solve the location problem of an automobile service enterprise. In addition, since risk factors can impact a decision, this work conducts a risk performance analysis when locating an automotive service enterprise. A practical example is given to illustrate the proposed model and algorithm.

An uncertainty facility location allocation (FLA) problem is a complex nonlinear optimization problem. Currently, researchers have discussed the stochastic cost/profit issues of FLA. However, rising energy conservation awareness and environmental concerns, energy-efficientcy and low-carbon emission should be regarded as key criteria in solving it. To do so, this paper presents a new FLA problem from a sustainable development point of view. By taking a typical facility service enterprise as a case, a new stochastic energy-efficient FLA model subject to carbon emission, economical, capacitated, and regional constraints is formulated. Then, stochastic simulation and scatter search are integrated as an intelligent algorithm to resolve it. Some examples are demonstrated to prove the availability of the proposed model and solution algorithm.

Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.

The layout of functional areas has a great impact on the implementing of a logistics park. Existing layout models with multiple planning objectives and influencing factors are too complex to be solved. This article puts forward a two-stage layout method for functional areas in logistics parks combining a layer-by-layer analysis process and a multi-objective programming model. Base on the rational setting of functional areas, the optimization of operational processes and the quantification of the interrelations between functional areas, some green logistic goals are achieved in two stages. In the first stage, the method aims at maximizing the comprehensive relationship among functional areas and obtaining the layout structure by the layer-by-layer analysis process. In the second stage, a multi-objective planning model is established to get the location and shape of each functional area, with the goals of minimizing the material handling costs and maximizing the spatial utilization rate. A genetic algorithm is designed to solve the model. Finally, an example is put forward to verify the feasibility of the two-stage layout method, and the comparison with the previous general model is presented. This article provides a new method for the layout planning of function areas in green logistics parks.

The location problem of logistics park is the basis of logistics network planning. The establishment of conventional logistics park location planning model requires consideration of multiple factors, including position relationships between site selection points and demand points, economic cost, service efficiency, and consumer satisfaction. “Green development concept” gradually leads the trend of social and economic development in the contemporary world. The green logistics park emerges as the requirement of era. There are obvious differences between the site selection rules and the location demand of the green logistics park with the conventional logistics park. On the basis of analyzing the evaluation indexes of green logistics park and the factors affecting the site selection, this article builds a single-objective decision model based on the comprehensive cost. The model sets transportation cost and environment cost as the objective function. In addition, according to the constraints of location and quantity of the proposed logistics park, the model is divided into four parts to refine, and the solving process based on hybrid particle algorithm and alternate location allocation heuristic algorithm is given. The model and algorithm are feasible by analyzing the numerical example. Finally, the green logistics park site selection scheme is designed. The location model that considers the single comprehensive cost target provides a simple and feasible decision method for the green logistics park location.

Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process–techniques for order preference by similarity to an ideal solution and analytical hierarchy process–gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.

This article proposes a machine learning–based travel mode detection method using urban residents’ travel routes as the data source, collected via smartphone global positioning system modules. A data-driven machine learning strategy was chosen in the model construction. This study performed data cleaning and mining on over 4400 pieces of urban resident travel records containing several millions of global positioning system tracking points. Series of characteristic values of speed, travel distance, and direction are calculated, which reflect the travel mode of smartphone holders. In travel mode identification, first, the transition regions of travel segments of different travel modes are effectively distinguished; then, continuous tracking points for single-mode travel are connected into single-mode travel segments. The travel mode of the surveyed subjects is identified based on the calculated features of average speed, average acceleration, and average change of direction within each single-mode segment. The random forest method is chosen as the basis model to classify travel mode. Three-quarters of the travel records were used to construct the random forest classifier, and the detection accuracy of the established model for the remaining ¼ of the travel record reached 94.4%. The proposed method uses massive smartphone global positioning system tracking points as the basis; the detection results are consistent with manually collected prompted recall survey records.

Walking habits can affect the self-organizing movement in pedestrian flow. In China, pedestrians prefer to walk along the right-hand side in the collision-avoidance process, and the same is true for the left-hand preference that is followed in several countries. Through experiments with pedestrian flow, we find that the relative position between pedestrians can affect their moving preferences. We propose a kind of collision-avoidance force based on the social force model, which considers the predictions of potential conflict and the relative position between pedestrians. In the simulation, we use the improved model to explore the effect of moving preference on the collision-avoidance process and self-organizing pedestrian movement. We conclude that the improved model can bring the simulation closer to reality and that moving preference is conducive to the self-adjustment of counterflow.

Product style characteristics have a large impact on product function. Making an objective and precise assessment of style characteristic has become an increasing importance to improve the production efficiency and reduce environmental pollution. This work proposes a framework built by analytic hierarchy process and technique for order preference by similarity to ideal solution methods, that is, analytical hierarchy process and technique for order performance by similarity to ideal solution, to evaluate automotive style design alternatives’ performance, together with automotive style design characteristics. Analytic hierarchy process is applied to obtain weights of the performance, and technique for order preference by similarity to ideal solution is adopted to rank the design alternatives. A case study is illustrated to test and verify the proposed method. Simultaneously, sensitivity analysis is provided to monitor the robustness of this method. The results show that it provides an effective and feasible method for evaluation of automotive style design alternatives’ performance.

Staggered shifts is one of the popular TDM (Transportation Demand Management) policies, which can reduce commute travel volume during the AM and PM peak periods, and relieve traffic congestion. In order to make effective staggered shifts program, it is necessary to examine the effect of the program on commute travel behavior. This paper takes Beijing (China) as an example to evaluate the validity of staggered shifts policy. Based on data investigation, the commute travel behavior and the commuters’ preference for staggered shifts are analyzed. This paper makes four staggered shifts programs, and develops a commute departure time choice model with Multinomial Logit method to predict the influence of the programs on commute departure time, and develops a commute travel duration model to analyze the influence of the programs on commute travel time. Departure time prediction shows that Program B can reduce the traffic volumes in 6:30–8:30 period by 15.24%, and commute travel duration analysis indicate that Program B can reduce the home-to-work travel time by 21.73%. Therefore, Program B is proven to be the best staggered shifts program for Beijing. The results of this paper can provide valuable information on how to develop an effective staggered shifts program.

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]

Abstract Aims Zinc‐finger protein 418 (ZNF418) has been confirmed to be expressed in myocardial tissue. However, the role and mechanism of ZNF418 in pathological myocardial remodelling after myocardial infarction (MI) have not been reported. This study was to elucidate the effect and mechanism of ZNF418 on ventricular remodelling after MI in mice. Methods and results MI mice and H9c2 cardiomyocytes were used to conduct in vivo and in vitro experiments, respectively. ZNF418 expression was regulated by adeno‐associated virus 9 and adenovirus vectors. Pathological analysis, echocardiography, and molecular analysis were performed. ZNF418 was down‐regulated in the left ventricular tissues of post‐MI mice. In contrast, ZNF418 overexpression decreased mortality and improved cardiac function in MI mice. The MI mice exhibited a significantly increased cross‐sectional area of myocardial cells and elevated protein expression levels of myocardial hypertrophy markers ANP, BNP, and β‐MHC (all P

Abstract Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4.

The STING (Stimulator of Interferon Genes) pathway is pivotal in activating innate immunity, making it a promising target for cancer immunotherapy. STING agonists have shown potential in enhancing immune responses, particularly in tumors resistant to traditional therapies. This scholarly review examines the diverse categories of STING agonists, encompassing CDN analogues, non-CDN chemotypes, CDN-infused exosomes, engineered bacterial vectors, and hybrid structures of small molecules-nucleic acids. We highlight their mechanisms, clinical trial progress, and therapeutic outcomes. While these agents offer significant promise, challenges such as toxicity, tumor heterogeneity, and delivery methods remain obstacles to their broader clinical use. Ongoing research and innovation are essential to overcoming these hurdles. STING agonists could play a transformative role in cancer treatment, particularly for patients with hard-to-treat malignancies, by harnessing the body’s immune system to target and eliminate cancer cells.

RGD-based self-assembling nanodrugs are a promising advancement in targeted cancer therapy, combining the specificity of RGD peptides with the benefits of nanotechnology. These nanodrugs enhance tumor targeting and cellular uptake while reducing off-target effects. RGD peptides facilitate the self-assembly of stable nanostructures, ensuring efficient drug delivery. Despite their potential, challenges such as immunogenicity, stability, tumor heterogeneity, and manufacturing scalability need to be addressed. Future research should focus on improving biocompatibility, advanced targeting strategies, personalized medicine approaches, and innovative manufacturing techniques. Overcoming these challenges will pave the way for the successful clinical translation of RGD-based nanodrugs, offering more effective and safer cancer treatments.