Search

Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development. [ABSTRACT FROM AUTHOR]

Echinococcosis is a serious zoonotic parasitic disease transmitted from canines to humans and livestock. Periodic deworming is recommended by the WHO/OIE as a highly effective measure against echinococcosis. However, manual deworming involves significant challenges, particularly in remote areas with scarce resources. The insufficient awareness delivering praziquantel (PZQ) baits for dogs leads to low compliance rate. The aim of this study was therefore to develop a novel smart collar for dogs to address these challenges. We developed a smart Internet of Things (IoT)-based deworming collar which can deliver PZQ baits for dogs automatically, regularly, quantitatively with predominant characteristics of being waterproof, anti-collision, cold-proof and long life battery. Its performance was tested in two remote locations on the Tibetan Plateau. A cross-sectional survey was conducted to evaluate the compliance of the dog owners. Further, a randomized controlled study was performed to evaluate the difference between smart-collar deworming and manual deworming. The collar's effectiveness was further assessed on the basis of Generalized Estimation Equations (GEE). The testing and evaluation was done for 10 smart deworming collars in factory laboratory, 18 collars attached for 18 dogs in Seni district, Tibet Autonomous Region, China, and 523 collars attached for 523 dogs in Hezuo city, Gansu province, China. The anti-collision, waterproof, and coldproof proportion of the smart collars were 100.0%, 99.5%, and 100.0%, respectively. When compared to manual deworming, the dogs' risk of infection with Echinococcus on smart-collar deworming is down to 0.182 times (95% CI: 0.049, 0.684) in Seni district and 0.355 (95%CI: 0.178, 0.706) in Hezuo city, the smart collar has a significant protective effect. The owners' overall compliance rate to attach the smart collars for their dogs was 89%. The smart deworming collar could effectively reduce the dogs' risk of infection with Echinococcus in dogs, significantly increase the deworming frequency and coverage and rapidly remove worm biomass in dogs. Thus, it may be a promising alternative to manual deworming, particularly in remote areas on the Tibetan Plateau. Author summary: Echinococcosis remains a critical but neglected zoonotic parasitic disease transmitted between canines and livestock or wild rodents. Dogs play a key role in Echinococcus granulosus sensu lato (s.l.) and E. multilocularis transmission, dual infection also occurs in dogs in co-endemic regions in China. The initial egg-production phase occurs over a span of 34–58 days (E. granulosus) or 28–35 days (E. multilocularis) following infection. The free-living eggs, voided with faeces of the definitive host, can withstand extreme weather conditions and remain viable for 240 days (E. granulosus) or 41 months (E. multilocularis) in the environment. Removal or reduction of the worm biomass in dogs will have the greatest and fastest effect in terms of reducing active transmission. In China, although significant efforts have been expended to achieve monthly manual deworming, the actual frequency and coverage thereof remain low; therefore, Echinococcus spp. is still highly prevalent among dogs. Moreover, echinococcosis transmission is still rampant, as the heavy disease burden suggests. We propose a novel, smart, Internet of Things (IoT)-based deworming tool that can deliver PZQ baits to dogs regularly and automatically. It could increase the deworming frequency and coverage significantly, reduce the risk of infection by down to 0.182–0.355 times, and prevent canine infections by removing the worm biomass in dogs rapidly. This deworming collar could also potentially prevent the transmission of echinococcosis from dogs to humans and livestock completely. It may be an excellent alternative to existing manual deworming methods, and the difficulties associated with performing deworming in remote areas with scarce resources can be overcome. Since the discovery of PZQ as the most effective antiworm drug, few breakthroughs have been achieved in terms of novel tools and technologies for the control of echinococcosis. Over the last 50 years, echinococcosis control measures have lagged to keep up with the World Health Organization (WHO) roadmap for the elimination of the disease owing to the practical difficulties in remote areas with scarce resources as well as the lack of promotion of new technologies. We expect the proposed smart deworming collar to herald the development of more innovative technologies for controlling echinococcosis by accelerating the elimination of the disease. [ABSTRACT FROM AUTHOR]

Low-carbon manufacturing is an emerging industrial trend and an important way to achieve sustainable development. As the country with the largest manufacturing scale and carbon dioxide emissions, it is of great practical and theoretical significance to study China's low-carbon manufacturing issues. Based on the ternary analysis perspective of simmelian tie, this paper chooses to identify the influencing factors through the technology-organization-environment research framework, and derives the pathways of low-carbon manufacturing through the fuzzy-set qualitative comparative analysis method. The research results show that: (1) Under the low-carbon manufacturing goal, stable industrial chain structure and policy-sensitive are the formation reasons for the simmelian tie. (2) From the perspective of simmelian tie, low-carbon manufacturing will be influenced by the driving role of green technological advances, the supporting role of organizational strategies, and the regulatory role of the policy environment. (3) Manufacturing enterprises' low-carbon pathways are directly related to their industrial chain position. Compared with upstream enterprises, downstream enterprises have more diverse low-carbon manufacturing pathways. (4) Active governmental support can more effectively promote the realization of low-carbon manufacturing than environmental pressure. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the diurnal variation in soil respiration under different land use types on Taihang Mountain, North China, and to understand its response to environmental factors (e.g., soil temperature and moisture) and forest management. Diurnal variations in soil respiration from plantations ( Robinia pseudoacacia, Punica granatum , and Ziziphus jujuba ), naturally regenerated forests ( Vitex negundo var. heterophylla ), grasslands ( Bothriochloa ischaemum ), and farmlands (winter wheat/summer maize) were measured using an LI-8100 automated soil CO 2 flux system from May 2012 to April 2013. The results indicated that land use type had a significant effect on the diurnal variation of soil respiration. The diurnal soil respiration from farmlands was highest, followed by Ziziphus jujube, R. pseudoacacia, P. granatum , the lower soil CO 2 efflux was found from B. ischaemum and V. negundo var. heterophylla . The diurnal soil respiration across different land use types was significantly affected by soil temperature and moisture, and their interaction. Precipitation-stimulated soil respiration increased more in soil with low water content and less in soil with high water content. The lower diurnal soil respiration from naturally regenerated forests suggests that naturally regenerated vegetation is the optimal vegetation type for reducing global warming. [ABSTRACT FROM AUTHOR]