One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed.

https://www.mdpi.com/1996-1073/14/10/2756/pdf

The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey

Tempo-spatial changes of ecological vulnerability in the arid area based on ordered weighted average model

The land topic has generally become a major socioeconomic issue that currently attracts attention globally. To explore the issue, various countries devote much attention to land use planning. This paper searches the Core Collection of the Web of Science and collects 1771 land use planning articles published between 1990 and 2019. The R software, biblioshiny package, and CiteSpace are applied in this paper. The results show the following: (1) the number of papers related to land use planning has shown an overall growth trend, which can be divided into the following four stages: initial exploration, preliminary growth, stable development, and rapid development; (2) the United States is in an absolute leading position as the primary research force among developed countries. The top 3 countries related to international cooperation are the United States, China, and Italy; (3) land use planning, land use, GIS, urban planning, China, spatial planning and climate change are some high-frequency keywords used in land use planning; (4) the current research hot spots mainly focus on the relationship between land use spatial planning and regional planning, land informatization development, public participation, urban sprawl, and sustainable development; (5) the themes show 8 evolutionary paths related to the following four topics: ecosystem protection, climate and natural change, urban sprawl and agricultural development, and GIS development; (6) this paper proposes that strengthening integration within disciplines, introducing new theories and methods, and sustainable land use will become future research directions requiring more land use planning attention.

Exploring the Global Research Trends of Land Use Planning Based on a Bibliometric Analysis: Current Status and Future Prospects

Selection of suitable sites for sustainable urban development is one of the critical and complex issues in urban planning. Since many criteria must be considered and analysed in the selection of sites for urban development, it is necessary to use the most effective techniques to identify the best location for urban expansion. The application of a geographic information system (GIS) can help with successful analysis and decision making. This study aims to find the most suitable locations for urban development in Aqaba based on four main planning criteria, including thirteen sub-criteria. Socioeconomic, topographic, eco-environmental and political and security aspects were considered the main planning goals. Geophysical, cultural, and socioeconomic data were used to assess land suitability for future urban growth by integrating the analytic hierarchy process and GIS technology. The findings of this research based on multi-criteria decision analysis reveal that most of the area attains a moderate suitability (47.7%) for sustainable development, while high-suitability areas account for 2.9% of the total study area. Areas with the highest suitability are rarely found, accounting for 0.1% of the total area, composed of well-connected regions that can be reserved as second-degree suitable locations for future sustainable urban development in the study area. The study provides insights into sustainable urban development using GIS techniques and highlights the significant constraints on the study area.

Land suitability analysis of urban development in the Aqaba area, Jordan, using a GIS-based analytic hierarchy process

Ecosystem service trade-offs and identification of eco-optimal regions in urban agglomerations in arid regions of China

Land-use suitability assessment for urban development using a GIS-based soft computing approach: A case study of Ili Valley, China

Analysis and implementation of multi-port bidirectional converter for hybrid energy systems

Improving integrated environmental zoning from the perspective of logic scoring of preference and comparative advantage: A case study of Liangjiang New Area, China

The Intrusion detection system (IDS) is one of the most important tools for defending against abnormal flow and attack messages. Most of the existing IDSs use detection technology based on security policies, and there is a risk that it cannot be accurately analyzed and evaluated. Therefore, machine learning techniques provide a new direction for solving this problem. This paper uses and analyzes the CIC-IDS series datasets, but there is a data imbalance in this dataset. In order to solve the problem of data imbalance and reduce the accuracy of the model, this paper proposes a hierarchical detection model. Experiments have shown that the stratified detection module has good classification accuracy for attack types with a small sample size.

https://iopscience.iop.org/article/10.1088/1742-6596/2294/1/012033/pdf

A Hierarchical Intrusion Detection System Based on Machine Learning

Object detection technology has been widely used in the field of garbage classification, but embedded devices cannot withstand the computational load of most current object detection models, which brings great challenges to the development of garbage detection technology. In order to realize the real-time garbage detection in low computing power environment, a real-time garbage detection method based on improved PicoDet is proposed. The original CSP- PAN structure is replaced by BiFPN, and higher-level features are obtained through residual connection and weighted bidirectional feature fusion, which reduces the computational cost of the model while ensuring improved accuracy. The CIoU Loss is used as the Reg loss to improve the positioning accuracy and convergence speed of the model. Comparative experiments are carried out on self-built datasets. The experimental results show that the model has an accuracy rate of 97.30% and a speed of 98FPS, which is higher than the test results of other models, and the detection speed and accuracy rate are significantly improved.

Sicheng Peng

Papers

Land-use suitability assessment for urban development using a GIS-based soft computing approach: A case study of Ili Valley, China

Analysis and implementation of multi-port bidirectional converter for hybrid energy systems

Improving integrated environmental zoning from the perspective of logic scoring of preference and comparative advantage: A case study of Liangjiang New Area, China

A Hierarchical Intrusion Detection System Based on Machine Learning

Real-time Waste Detection Algorithm Based on Optimized PicoDet