During coal self-heating, reactions of carboxyl groups feature in the evolution of the spontaneous combustion of coal. However, their elementary reaction pathways during this process still have not been revealed. This paper selected the Ar–CH2–COOH as a typical carboxyl group containing structure for the analysis of the reaction pathways and enhancement effect on the coal self-heating process by quantum chemistry calculations. The results indicate that the hydrogen atoms in carboxyl groups are the active sites, which undergo the oxidation process and self-reaction process during coal self-heating. They both have two elementary reactions, namely (i) the hydrogen abstraction of –COOH by oxygen and the decarboxylation of the –COO· free radical and (ii) the hydrogen abstraction of –COOH and its pyrolysis. The total enthalpy change and activation energy of the oxidation process are 76.93 kJ/mol and 127.85 kJ/mol, respectively, which indicate that this process is endothermic and will occur at medium temperatures. For the hydrogen abstraction of –COOH by hydrocarbon free radicals, the thermal parameters are 53.53 kJ/mol and 56.13 kJ/mol, respectively, which has the same thermodynamic properties as the oxidation process. However, for the pyrolysis, the thermal parameters are –42.53 kJ/mol and 493.68 kJ/mol, respectively, and is thus exothermic and would not occur until the coal reaches high temperatures. They affect heat accumulation greatly, generate carbon dioxide, and provide new active centers for enhancing the coal self-heating process. The results would be helpful for further understanding of the coal self-heating mechanism.

The implementation of digital technology has become paramount to facilitating green and low-carbon development in dairy farms amidst the advent of digital agriculture and low-carbon agriculture. This study examined the impact of digital technology implementation on the carbon emission efficiency of Chinese dairy farms via an assessment of micro-survey data, incorporating an Undesirable Outputs-SBM model, a Tobit model, the propensity score matching technique, a quantile regression model, and an instrumental variable approach. This study examined the potential moderating influence of environmental regulations on digital technology applications and the carbon emission efficiency of dairy farms. The findings of the research indicate that the implementation of digital technology had a considerable beneficial consequence on the carbon emission proficiency of dairy farms. The statistical significance level of the mean treatment effect was 0.1161, with the most profound influence of precision feeding digital technology on the carbon emission efficiency in dairy farms. The application of digital technology has a more pronounced effect on dairy farms with lower levels of carbon emission efficiency compared to those with medium and high levels of carbon emission efficiency. The application of digital technology toward the carbon emission efficiency of dairy farms is positively moderated by environmental regulations. Finally, this paper puts forward some specific policy recommendations to achieve the strategic goal of low carbon and efficient development in dairy farms through the application of digital technology, which enriches the existing research on carbon emission reduction in dairy farms from theoretical and practical aspects.

Reducing agricultural carbon emissions (ACE) is important for the sustainable development of agriculture. Agricultural productive services (APS), a novel form of agricultural technology extension, offer new avenues for promoting sustainable and green agriculture. The present study aims to explore the impact of APS on ACE. In line with the aim of the study, the Kernel density, Moran’s I index, spatial Durbin model, and threshold regression model are employed. The findings reveal that APS demonstrate a significant inhibitory effect on ACE, reducing ACE not only within the region itself but also exerting a negative spatial spillover effect on other regions. Furthermore, a non-linear relationship between APS and ACE is observed, characterized by an inverted U-shaped curve with the arable land operating area serving as the threshold. Therefore, to fully harness the inhibitory effect of APS on ACE and to promote environmentally friendly and sustainable agricultural development, policymakers should vigorously develop APS, strengthen regional cooperation, and promote land transfer. Our research can help in understanding the impact of APS on ACE and to promote sustainable agricultural development.

There are imbalances and uncertainties in the global supply and demand of dairy products, owing to the adverse influence of overall economic changes, dairy prices, agricultural politics, the COVID-19 pandemic, and severe climate. This paper aims to explore the evolving characteristics and influencing factors of the global dairy trade pattern and make recommendations for the sustainable development of the global dairy trade. This paper studies the evolutionary characteristics of the global dairy trade pattern from the perspective of the overall structure, individual characteristics, and core–periphery structure through complex network analysis (CNA), using the countries involved in dairy trade from 2000 to 2020. Furthermore, this study explores the influencing factors of the dairy trade network using a quadratic allocation procedure (QAP). The results indicate that the global dairy trade network has been expanding, with prominent scale-free features and small-world characteristics. Individual countries display obvious heterogeneity, whereas the core import regions of the dairy shift from Europe, East Asia, and America to North America, the Middle East, and East Asia. Contrary to this, there is no significant change in the core export regions. Consequently, the entire dairy trade network represents a clear core–periphery structure. Moreover, the income per capita gaps, geographic distance gaps, and common language always affect the trade value and dairy trade relations across the countries. Meanwhile, economic level gaps and regional trade agreements have become increasingly significant. Thus, the dairy trade may not follow the “border effect”. Lastly, this paper also extends recommendations for the sustainable development of the dairy trade.

Multipath routing, which is a common approach to achieve load balancing, will enable multiple active paths between two end‐hosts. However, it will cause out‐of‐order delivery of packets, which will not only severely debilitate the performances of TCP (Transmission Control Protocol) but also will make many single‐path–based applications work improperly. In this paper, we propose to utilize passive delay measurements between end‐hosts, to detect multipath routing with two different hypothesis testing approaches, ie, t‐Test and variance test. The motivation is that the obtained distributions of passive delays between end‐to‐end communication flows can be very similar to each other when they are routed by the identical end‐to‐end path, whereas distinct differences are observed among the ones of different end‐to‐end paths. Simulations based on NS2 validate the efficiency of both t‐Test and variance test and demonstrate the robustness of t‐Test against asynchronous measurements between different flows.