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The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.

Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.

The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.

Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

This chapter estimates how minimum parking requirements increase the cost of constructing housing, office buildings, and shopping centers. It also explains proposed legislation to limit how much parking cities can require in transit-rich districts.

I assembled data on the cost of constructing office buildings, shopping centers, and parking spaces in eight American cities, and data on the minimum parking requirements in these cities. I then combined the parking construction costs with the number of required parking spaces for each land use to estimate how the minimum parking requirements increase development costs for office buildings and shopping centers.

Minimum parking requirements increase the cost of constructing a shopping center by up to 67 percent if the parking is in an aboveground structure and by up to 93 percent if the parking is underground.

In suburban Seattle, parking requirements force developers to spend between $10,000 and $14,000 per dwelling to provide unused parking spaces.

On a typical construction site in Los Angeles, parking requirements reduce the number of units in an apartment building by 13 percent.

To mitigate the high costs imposed by minimum parking requirements, California is considering legislation to set an upper limit on how much parking cities can require in transit-rich districts: no more than one space per dwelling unit or two spaces per 1,000 square feet (93 square meters) of commercial space. This legislation would limit parking requirements, but it would not limit the parking supply because developers can always provide more than the required number of spaces if they think demand justifies the added cost.

This chapter measures how minimum parking requirements increase the cost of housing, office buildings, and shopping centers in order to subsidize parking. Urban historians often say that cars have changed the city, but urban planning has also changed the city to favor cars.

The purpose of this study is to prepare a chemically stable superhydrophobic coating with remarkable mechanical properties and concrete protective properties.

One synthetic step was adopted to prepare superhydrophobic coating. The process and product were analyzed and confirmed by fourier transform-infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), water contact angle (WCA), transmission electron microscopy (TEM), scanning electron microscope (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The mechanical properties were confirmed by tensile test. The concrete protective properties were confirmed by solution immersion test and rapid chloride migration coefficient test.

MSiO₂ nanoparticles (NPs) were chosen to enhance the hydrophobicity of fluorosilicone coatings. With a 4:1 mass ratio of fluorosilicone resin and MSiO₂ NPs, the coatings show superhydrophobicity with a WCA of 156° and a SA of 3.1°. In addition, the tensile mechanical property was improved, and the chloride ion diffusion coefficient was decreased significantly after the addition of MSiO₂ NPs.

This new fluorosilicone coating hybrid by MSiO₂ NPs could be applied as a concrete protective layer with properties of self-cleaning, antifouling, etc.

Introduction of MSiO₂ NPs hybrid to prepare fluorosilicone coating with superhydrophobicity on concrete surface has not been systematically studied previously.

The purpose of this study is to compare self-reported antisocial (SRA) behaviour of 10-year-old boys in China (in Zhuhai) and the USA (in Pittsburgh).

In Pittsburgh, 868 boys were given a SRA behaviour questionnaire in 1987–1988. In Zhuhai, 1,154 boys were given the same questionnaire in 2017.

The prevalence of 23 acts in the two countries was significantly correlated (r = 0.69), showing that the acts that were admitted by most boys in one country also tended to be admitted by most boys in the other country. Also, the mean prevalence in the two countries was very similar, at about 13%. However, several acts (e.g. stealing) were more prevalent in Zhuhai, while violent acts were more prevalent in Pittsburgh. The frequencies of the acts in the two countries were also significantly correlated (r = 0.51), although frequency was usually greater in Pittsburgh.

While most theories and research in criminology and psychology are based on Western industrialised countries, it is important to carry out more cross-cultural comparisons of antisocial behaviour in other countries. These results show encouraging generalizability and replicability, despite differences in time and place.

To the best of the authors’ knowledge, this study presents the first comparison of age-matched Chinese and American children on the prevalence and frequency of specific antisocial acts.

Evaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.

Monte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.

The review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.

Performance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

This paper proposes a self-supervised monocular depth estimation algorithm under multiple constraints, which can generate the corresponding depth map end-to-end based on RGB images. On this basis, based on the traditional visual simultaneous localisation and mapping (VSLAM) framework, a dynamic object detection framework based on deep learning is introduced, and dynamic objects in the scene are culled during mapping.

Typical SLAM algorithms or data sets assume a static environment and do not consider the potential consequences of accidentally adding dynamic objects to a 3D map. This shortcoming limits the applicability of VSLAM in many practical cases, such as long-term mapping. In light of the aforementioned considerations, this paper presents a self-supervised monocular depth estimation algorithm based on deep learning. Furthermore, this paper introduces the YOLOv5 dynamic detection framework into the traditional ORBSLAM2 algorithm for the purpose of removing dynamic objects.

Compared with Dyna-SLAM, the algorithm proposed in this paper reduces the error by about 13%, and compared with ORB-SLAM2 by about 54.9%. In addition, the algorithm in this paper can process a single frame of image at a speed of 15–20 FPS on GeForce RTX 2080s, far exceeding Dyna-SLAM in real-time performance.

This paper proposes a VSLAM algorithm that can be applied to dynamic environments. The algorithm consists of a self-supervised monocular depth estimation part under multiple constraints and the introduction of a dynamic object detection framework based on YOLOv5.

This study aims to realize the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified Color Index Pigment Red 57:1 (C.I. Pigment Red 57:1, PR 57:1).

In this paper, the inorganic materials (sepiolite and SiO₂·nH₂O) were used in both PR 57:1 production wastewater treatment and its core-modification. The inorganic material firstly adsorbed 3-hydroxy-2-naphthoic acid (bon acid) in the pigment wastewater to reduce chemical oxygen demand. Then, the inorganic material adsorbed with bon acid was reused to prepare core-modified PR 57:1.

In the pigment wastewater adsorption experiment, it was found that under pH = 3, the adsorption percentage of bon acid by inorganic material can reached up to 46.00%. The pigment characterization results showed that the core-modified PR 57:1 had a core-shell structure. Under UV light irradiation for 1 h, the core-modified PR 57:1 prepared with sepiolite and SiO₂·nH₂O showed total color difference ΔE value of 1.43 and 2.05, respectively, which was lower than that of unmodified PR 57:1 (ΔE = 2.89). In addition, the transmittance of pigment water suspension test results showed that the core-modified PR 57:1 showed better water dispersibility.

This paper attempts to develop a synergistic strategy based on the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified PR 57:1.

An alliance portfolio can help latecomer firms to acquire the necessary knowledge and resources to catch up with market leaders. However, how latecomer firms construct an alliance portfolio in terms of the nature of windows of opportunity has not been fully analyzed. This paper aims to explore how latecomer firms can build appropriate coalitions according to the nature of the window of opportunity to achieve technological catch-up in different catch-up phases.

Based on a longitudinal case study from 1984 to 2018 of Sunny Group, now a leading manufacturer of integrated optical components and products, this paper explores the process of technological catch-up of latecomer firms building different types of alliance portfolio in different windows of opportunity.

This paper finds that there is a sequence when latecomers build an alliance portfolio in the process of catch-up. When the uncertainty of opportunity increases, the governance mechanism of the alliance portfolio will change from contractual to equity-based. Also, latecomer firms build market-dominated and technology-dominated alliance portfolios to overcome their market and technology disadvantages, respectively.

These conclusions not only enrich the theory of latecomer catch-up from the perspective of windows of opportunity but also expand research on alliance portfolio processes from a temporal perspective.

Property management companies in residential properties are increasingly using ICT and specific property management software applications to conduct daily property management tasks. However, the benefits of using specific software applications and how it can add value to property management companies are largely unexplored. The present study examines the impact of property management mobile applications usage features on strata property residents' satisfaction in Kuala Lumpur.

A total of 205 residents from 3 strata residential buildings in Kuala Lumpur using property management applications were surveyed. The study employed structural equation modelling using SPSS Amos software.

The findings suggest that visitor management and security management via software applications positively influence the residents' satisfaction. However, facility management via software application was found to be negatively affecting residents' satisfaction.

The study highlighted the benefit of property managers adopting property management software to achieve increased residents' satisfaction in strata residential properties.

This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.

The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.

The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.

The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.

Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.