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In order to explore the theory of the spatial layout of urban sports facilities, starting with the analysis of theoretical knowledge, the current situation of public sports facilities in the central urban area of Jinan is analyzed, the various factors affecting the planning layout are discussed, and the strategies and methods of the layout planning of public sports facilities in Jinan are summarized. The results show that the layout planning of public sports facilities should follow the corresponding patterns and principles. The layout of public sports facilities at all levels should fully consider the factors of urban public transport, urban management system, urban public functions, and reasonable service radius of public sports facilities. It can be seen that excessive pursuit of efficiency will lead to excessive service radius and poor accessibility of urban public sports facilities; excessive pursuit of fairness will result in a small and scattered layout pattern, which easily leads to idle waste.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

This empirical study of one of China's biggest take-out platforms during coronavirus disease 2019 (COVID-19) explores the legal rights and protections for gig workers' availability time.

The study uses a quantitative survey and interviews of take-out platform riders, investigating the intrinsic features of working time and availability time. Labour law professionals and scholars are interviewed to verify the findings.

The availability time of take-out platform riders is difficult to define under the current legal working-time framework. There is a need for a specific method to define availability time, considering the multiple factors in gig-take-out-sector work. One option is to apply working-time regulations to availability time but to use a proportionality test to preserve flexibility whilst the platform offers protection to the riders during that time.

Pandemic-related travel restrictions limited the authors' study to one take-out platform in Beijing. Future studies should cover a wider geographical area and multiple take-out platforms.

The study uniquely evaluates the availability time of take-out platform riders to determine appropriate public policy and theoretical implications. It proposes a proportionality test regulating riders' availability. In particular, the workers being “at leisure” during availability time could mitigate the platform's liability for full remuneration or moderate the ceiling of working hours. The occupational health and safety of riders must be fully protected, as they are still “at the platform's disposal” at that time.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

The purpose of this study is to investigate the understanding and application of crime of sabotaging production and operation in internet era, and, at the same time, discuss the basic position for criminal law interpretation in cyberspace.

Doctrinal analysis and case study.

Along with the advent of the internet era, how to apply the traditional crime of sabotaging production and operation in virtual space has attracted people’s attention. The controversy caused by the conviction of malicious application of fake transactions is a typical example. The legal interest protected here includes not only the property value of the means of production itself, but also the expectation interest that can be obtained by normal production and operation activities. There is no reliable basis to believe that overlap of articles between special provision and general laws occurs in crime of sabotaging production and operation and crime of intentional damage of property. The production and operation activities carried out online can also be covered by crime of sabotaging production and operation, without doubt. Ejusdem Generis Rule should be fully respected, but crime of sabotaging production and operation has a dual structure of means behavior and purpose behavior, where the purpose behavior, sabotaging production and operation, is the key to the conviction. However, it is not necessarily premised on physical damage and violent characteristics. The understanding and application of traditional crimes should keep pace with the times in the internet era, and we should not stick to a completely rigid subjective interpretation.

This study demonstrates the possible application of crime of sabotaging production and operation in cyberspace, and clarifies many misunderstandings about this crime.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

With the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.

In this paper, a simple and effective RUL prediction method based on the combination method of auto-regression (AR) time-series model and particle filter (PF) was proposed for lithium-ion battery. The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training. By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations, the proposed PF + AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.

Experiments on CALCE dataset have fully compared the conventional PF algorithm and the AR + PF algorithm both on original exponential empirical degradation model and the deformed double-exponential one. Experimental results have shown that the proposed PF + AR method improved the prediction accuracy, decreases the error rate and reduces the uncertainty ranges of RUL, which was more suitable for the deformed double-exponential empirical degradation model.

In the running of UPS device based on lithium-ion battery, the proposed AR + PF combination algorithm will quickly, accurately and robustly predict the RUL of lithium-ion batteries, which had a strong application value in the stable operation of laboratory and other application scenarios.