Reasonable Path Research Articles

Energy‐Efficient Task Migration and Path Planning in UAV‐Enabled Mobile Edge Computing System

With the rapid development of unmanned aerial vehicles (UAVs) technology and the advent of the 5G era, the role of UAV‐enabled mobile edge computing (MEC) system has attracted much attention, especially in the event of some emergencies. However, considering the limited battery life and computing capabilities of UAVs, it is challenging to provide energy‐efficient services for mobile devices. To solve this challenge, we propose an energy‐efficient dynamic task migration algorithm (EDTM) that minimizes the total energy consumption of the system while ensuring UAVs system load balance. Based on the improved ant colony algorithm and path elimination strategy, the proposed algorithm comprehensively considers task migration distance between UAVs, the load situation of UAVs, and environmental factors (e.g., wind speed and air density) and finally plans a reasonable task migration path. The simulation results show that the performance of the proposed EDTM is superior to the benchmark schemes.

Research on the development and path exploration of autonomous underwater robots

With the increasing development of marine science and technology, autonomous underwater robots have become a hot spot in the field of underwater research and applications, and have been widely used in deep-sea scientific research, marine resources research, and deep-sea security. This paper focuses on analyzing the current research and development status and application prospects of autonomous underwater robots and predicts their future development trend. Aiming at the difficulties in engineering practice of path search in underwater unknown space, the traditional space path exploration method is analyzed, and a hybrid search algorithm is proposed to plan a reasonable and efficient dynamic path for target search in unknown underwater space.

Low-Carbon Development of Livestock in China: Evolution, Predicaments and Paths Ahead

Livestock production is an important source of carbon emissions, and the low-carbon transformation of the livestock sector is a critical path to achieving sustainable development of the global economy and society. China is one of the most important livestock-producing countries in the world, so it is necessary to study the evolution and predicaments of low-carbon development in the livestock sector and put forward a scientific and reasonable emission reduction path. Our study first describes the development process of the low-carbon livestock sector in China and calculates the carbon emissions of the livestock sector from 2000 to 2019. It is found that carbon emissions from China’s livestock sector show a trend of increasing and then decreasing continuously from 2000 to 2019, which has a great potential for emission reduction. Then the predicament of low-carbon development of the livestock sector in China is put forward. Finally, from the perspectives of the government, livestock farmers, enterprises, the public, and other social institutions, a multi-subject collaborative mechanism is constructed, and corresponding implementation paths are designed, to provide theoretical support and suggestions for accelerating the low-carbon transformation of the livestock sector in China.

Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

Currently, long‐distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision‐making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision‐makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost.

Effect Evaluation of Scientific and Technological Achievements Transformation Policy Mix: Evidence from China's Provincial Panel Data

Nations and regions are increasingly focusing on the transformation of scientific and technological (S&T) achievements. To accomplish this, they look for reasonable paths for policy optimization, and improve the efficiency of transformation. Replying on the panel data of thirty-one provinces in mainland China from 2010 to 2019, this study uses the panel fixed-effects model to analyze the effects of R&D subsidies, intermediary services, intellectual property protection policy tools and their interaction terms on the transformation efficiency of S&T achievements. All results indicate that the interaction between the two policy tools can enhance the efficiency of such transformations. The interaction between R&D subsidy and intermediary service is the strongest, which is the most effective tool combination; the interaction between R&D subsidy and intellectual property protection, the interaction between intermediary services and intellectual property protection, have relatively weak impact on transformation. The combination effect of policy tools will vary depending on the regional innovation capability level. Therefore, it is necessary to tailor the policy mix pattern to suit local conditions.

Debate for Civic Learning: A Model for Renewing Higher Education’s Civic Mission

Rapidly eroding financial support and tuition increases that outpace inflation threaten the viability of an education that considers civic engagement as foundational. Simultaneously, institutions of higher education are increasingly perceived by the public as market-driven entities existing for the economic benefit of the individual, the upward mobility of a social class, and in turn the further sedimentation of racial and class differences. Now, more than ever, our nation is in need of deliberate attempts to fashion common understandings, ways to navigate inevitable disagreements, and reasonable paths forward. Higher education is positioned to respond to these civic needs but requires a commitment to be bold and remain dedicated to our shared civic mission in the face of alarming polarization and vacated institutional trust. One way institutions of higher education can return to their shared sense of civic mission is with the integration of debate across the curriculum through innovative partnerships and collaborative design. Debate across the curriculum utilizes intentional course redesign to offer active learning experiences that combine public speaking, evidence-based reasoning, collaborative learning, and argumentation into various advocacy simulations. The debate for civic learning model has faculty partnered across multiple institutions to design, integrate, and assess debate-based pedagogy to positively impact student civic learning. Students and faculty across disciplines have reported that debate-based pedagogy helped improve classroom engagement, critical problem solving, perspective taking, empathy, and advocacy skills. This mixed-method research provides insights not only into debate-based course design and learning improvement strategies but also into how faculty, students, and administrators can partner between institutions to demonstrate a shared commitment to the civic mission of higher education and democratic promise of our nation.

Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency.

The energy transition and carbon emission efficiency are important thrust and target functions, respectively, for achieving carbon neutrality in the future. Using a sample of 30 Chinese provinces from 2006 to 2018, we measured their carbon efficiency using the game cross-efficiency data envelopment analysis (DEA). Then, a random forest regression model was used to explore the impact of renewable energy development on regional carbon emission efficiency. The results are as follows. First, China’s carbon emission efficiency in the southeast coastal area was better than that in the northwest area. Second, renewable energy development first inhibited and then promoted carbon emission efficiency, and there existed a reasonable range. Third, through a regional heterogeneity analysis, the trend of the influence of renewable energy development on carbon emission efficiency was found to not be significantly different in eastern, central, and western China, but there was a certain gap in the reasonable range. Our study not only helps to promote the study of renewable energy development and the carbon neutral target, but also provides an important reference for Chinese policy-makers to design a reasonable carbon emissions reduction path.

The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm

More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.

Retraction Note: Design of green city landscape based on GIS system

In recent years, the concept of green development has begun to penetrate into all levels of society. With the deepening of urbanization, the development of landscape architecture in cities has stopped. How to develop green garden in the environment of deepening urbanization has become an urgent problem. The development of urban landscape architecture has changed in the new period. In the new period, the urban landscape industry should be combined with urban development closely by using advanced science and technology on the basis of fully understanding the urban development situation and forming a dynamic system. Urban garden protection and construction is a comprehensive project, which needs to be planned and maintained by integrating knowledge of various disciplines. This process is very complex. The application of digital technology simplifies the complex steps in the process and reduces people’s brain and material consumption. This paper explores the new methods of landscape design and construction from the new technology of landscape design GIS. Before applying GIS technology, we should first build database, including the construction of application layer, analysis of the requirements of database construction, testing the performance of data information carrier, finding out relevant strategies and methods, and designing and implementing the database. In order to better manage the modern urban landscape architecture and plan a more reasonable path for the development of urban gardens, we should make full use of the power of science and technology and combine the development level of society to build a green urban garden ecosystem.

A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment

Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.

Research Progress on the Development of Domestic and Foreign Tourism Road

This paper compares and analyzes the development process of tourism road in the United States, Germany, Japan and China, and summarizes the relevant research results of tourism road with great significance so far. From the development of other countries, we put forward suggestions for our country's tourism roads. The goal is to explore the scientific and reasonable development path of our country's tourism roads, and build our country's tourism road design and evaluation system.

Optimal path selection of multimodal transport based on Ant Colony Algorithm

In the process of multimodal transport, the cost and time of transportation are particularly important. In order to avoid unreasonable container transportation and unnecessary waste of transportation capacity and transportation cost, we must effectively integrate the advantages of various transportation modes, select the most suitable transportation mode and the most reasonable transportation path, and take the minimum cost and time as the goal to ensure the smooth transportation of goods to the destination. Therefore, optimizing multimodal transport network has very important practical significance. This paper starts with the multimodal transport network under a single task, designs the solution method of the model combined with ant colony algorithm, and gives an example. Finally, the model and algorithm design are proved to be reasonable by using MATLAB solution algorithm.

위험지역을 고려하는 수정된 JPS (jump point search) 기법 개발

Graph search-based path planning algorithms have been widely applied for path planning in autonomous robot systems. Among these algorithms, the jump point search (JPS) method, adopted in this study, is a variant of the well-known algorithm A* and utilizes a unique node search strategy called “jump.” The jump makes the JPS method one of the fastest graph search-based path planning algorithms. However, while the A* algorithm can be easily modified for identifying uneven environmental conditions (e.g., danger zones), danger zones cannot be handled in the JPS algorithm because the jump strategy removes plenty of nodes, including the essential ones for avoiding danger zones. To obtain a reasonable path in uneven environments, this study proposes a modified version of the JPS algorithm called the modified jump search method (MJPS). The MJPS method enables danger zones to be recognized and thus avoided during path generation by introducing bordering and additional nodes. To validate the proposed method, numerical simulations will be performed, and the results will be analyzed by comparing it with the A*-based path planning results.

Potential Impacts of Fukushima Nuclear Leakage on China’s Carbon Neutrality—an Investigation on Nuclear Power Avoidance and Regional Heterogeneity

Ten years have passed since the Fukushima nuclear accident, but its impact on the environment and energy consumption structure has continued up to now. This accident delayed the process of China’s nuclear power construction and may have a certain potential impact on China’s goal of carbon neutrality by 2060. This paper aims to properly understand the negative impact of the Fukushima nuclear leakage on China’s nuclear power industry, to reawaken the attention of Chinese academic and governmental departments to nuclear energy, and to explore a reasonable path to achieve carbon neutrality. Based on the idea of a quasi-natural experiment, this paper collected the carbon emissions data of 30 provinces and cities in China from 2000 to 2017, and explored the accident impact and mechanism on carbon emissions in the provinces with nuclear power. The research results showed that the Fukushima nuclear accident had different impacts on China’s nuclear power provinces. Due to the large proportion of manufacturing industry and high dependence on energy, the carbon emissions in Jiangsu Province rose after being impacted by the incident, in contrast, the research results in Guangdong and Zhejiang provinces were opposite. Through the mechanism test, it was found that the incident impact had reduced the carbon emissions of Guangdong and Zhejiang by improving the industrial structure and energy efficiency, with the explanation ratios of 10.45 and 15.1%, respectively. Technological innovation had obscured the emission reduction effect of the incident impact, and the innovation driving force for green development in nuclear power base provinces was insufficient. These findings are helpful to analyze the regional layout of China’s nuclear power and have implications for achieving carbon neutrality. Finally, this study offers relevant policy recommendations.

Synthesis and Characterization of Methylammonium Lead Bromide Perovskite Based Photovoltaic Device

Hybrid electronic devices give a reasonable path for feasible power sources and other further applications due to its easy arrangement, preparation, producing, ease of materials, and less environmental impact. In this paper, we have discussed electrical properties of hybrid bromide perovskite nanoparticles and current progressions in perovskite photovoltaic devices have also been discussed. In order to fabricate, low-temperature solution-processed devices using one-step spin coating methods play a key role in producing uniform thin films. The spin coating technique has been used for the deposition of the precursor solution including methylammonium bromide (MABr) and lead bromide (PbBr2) with molar ratio (3:1) to prepare the thin films onto FTO-substrate. One-step solution processes has been used for deposition of the CH3NH3PbBr3 absorber layers and have explained an overview of material composition, deposition technique and the architecture of the device, the selection of charge transport layer and electrode.

How to Account for Different ETRs of Enterprises in the Same Industry? A Verification Based on the Perspective of Tax Laws and Local Discretion

Effective tax rate （ETR） measures the tax payment burden, which is a comprehensive reflection of a series of behaviors of enterprises and government departments in the whole process of tax administration. According to tax laws, the difference of business essence will lead to differentiated tax treatments, resulting in the difference of ETRs based on tax laws. At the same time, due to the fact that tax elements stipulated in the law are not clear and specific, it creates conditions for local government departments to derive discretion. Therefore, local governments are able to influence the ETR of enterprises within the scope of laws according to their own interests, which leads to the difference of ETRs based on local discretion. So, how to understand the two source factors of ETR differences? Do they contain the reasonable optimization path of China’s tax system?In general, there are few comprehensive and systematic studies on the causes of different ETRs of enterprises. Therefore, continuing the ideas of the existing literature, this paper starts from the ETR of enterprises in the same industry, taking the manufacturing industry as an example. Firstly, it verifies the existence of different ETRs of enterprises in the same industry. Secondly, it constructs a theoretical framework based on tax administration, dividing the source factors of tax differences into two categories: One is from tax laws, and the other is from local discretion in tax administration. Finally, it uses the Shapley value decomposition method to analyze the static contribution level and dynamic trend of the two factors, and discusses the heterogeneity between different regions. The results show that: （1） Tax law factors and local discretion factors are the main sources of different ETRs, and the former’s contribution is obviously higher than the latter. （2） On the one hand, the factor contribution from tax laws to different ETRs has been maintained at a high level; on the other hand, the factor contribution from local discretion shows a significant downward trend. It has proved the significant regional heterogeneity in the contribution of the two factors to different ETRs. （3） Based on the above results, this paper finally constructs the Tax Legalization degree index, and its dynamic trend shows that the construction of Tax Legalization in China has achieved remarkable results from 2007 to 2019. The marginal contribution of this paper lies in that: Firstly, it systematically explores the causes of different ETRs of enterprises in the same industry, and improves the theory of the difference in ETRs. Secondly, it uses the Shapley value decomposition method to quantitatively verify the relative contribution and trend of the two factors, and depicts the real environment of tax collection and management in China from a new perspective.

Surface plasmon resonance improved photocatalytic activity of Au/TiO2 nanocomposite under visible light for degradation of pollutants

AbstractThe development of nonprecious and highly efficient photocatalyst by a green technique is highly desirable for environmental remediation. Herein, gold nanoparticles (Au Nps) have been successfully loading on the nanoparticle TiO2 surface by a deposition‐precipitation (DP) method. The optimized amount of 3Au/TiO2 nanocomposite used for the degradation of toxic pollutant shows 36‐time photo‐activity improvement compared to the pure TiO2. According to the experimental results, the study confirmed that the prepared Au/TiO2 nanocomposite material has excellent visible light photo‐activity. This is due to the modification of Au surface plasmon resonance (SPR) and its catalytic function which leads to the expansion of visible light response, and the transfer of electrons from SPR‐Au to TiO2 enhances the charge separation nanocomposite. In addition, the as‐prepared nanocomposite exhibits best stability; even after five cycles, no serious deactivation was observed. Besides, the main active species involved during photocatalytic degradation were confirmed through trapping experiment that super oxide radicals are the leading species for the 2,4‐DCP degradation. It is expected that this work would provide a reasonable path to perceptibly improve the photocatalytic activity of TiO2‐based photocatalyst for the environmental remediation.

P‐4.4: Path Planning Analysis of Pad Bending Process for the Full Screen OLED Panel

In the pad‐bending process of the full‐screen panel, the metal wire in the bending area is easily broken due to deformation, and the occurrence of the break is related to the bending path. This paper analyzes the influence of different bending paths on the peak stress of the metal wire, determines the method to explore the reasonable path based on mathematical theory, and provides guidance for the pad‐bending process design, which can be used to improve the yield of pad‐bending process of OLED panels.

To hop or not, that is the question: Towards effective multi-hop reasoning over knowledge graphs

With the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose RL-MHR, a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs—the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model RL-MHR, comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of RL-MHR over state-of-the-art methods.

Dual-Channel Reasoning Model for Complex Question Answering

Multihop question answering has attracted extensive studies in recent years because of the emergence of human annotated datasets and associated leaderboards. Recent studies have revealed that question answering systems learn to exploit annotation artifacts and other biases in current datasets. Therefore, a model with strong interpretability should not only predict the final answer, but more importantly find the supporting facts’ sentences necessary to answer complex questions, also known as evidence sentences. Most existing methods predict the final answer and evidence sentences in sequence or simultaneously, which inhibits the ability of models to predict the path of reasoning. In this paper, we propose a dual-channel reasoning architecture, where two reasoning channels predict the final answer and supporting facts’ sentences, respectively, while sharing the contextual embedding layer. The two reasoning channels can simply use the same reasoning structure without additional network designs. Through experimental analysis based on public question answering datasets, we demonstrate the effectiveness of our proposed method