Effects of tea oil camellia (Camellia oleifera Abel.) shell-based organic fertilizers on the physicochemical property and microbial community structure of the rhizosphere soil

Soil microorganisms play important roles in promoting soil ecosystem restoration, but much of the current research has been limited to changes in microbial community structure in general, and little is known regarding the soil physicochemical property and microbial community structure. In this study, four organic fertilizers were first prepared based on tea oil camellia shell (TOCS). Our findings indicate that the application of BOFvo increased both total pore volume and BET surface area of the rhizosphere soils, as well there was a remarkable enhancement in total organic matter (TOM), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), total potassium (TK), and available potassium (AK) contents of the rhizosphere soils. Meanwhile, in comparison to the CK and CF groups, the utilization of BOFvo led to a substantial increase in both average yield and fruiting rate per plant at maturity, as well resulted in a significant increase in TN and TP contents of tea oil camellia leaves. Furthermore, our findings suggest that the application of TOCS-based organic fertilizers significantly enhances the microbial diversity in the rhizosphere soils with Proteobacteria and Ascomycota being the dominant bacterial and fungal phyla, respectively, and Rhodanobacter and Fusarium being the dominant bacterial and fungal genus, respectively. Redundancy analysis (RDA) indicates that the physicochemical characteristics of TOCS-based organic fertilizers had a significant impact on the composition and distribution of microbial communities in the rhizosphere soils. This study will facilitate the promotion and application of TOCS-based organic fertilizers, thereby establishing a foundation for the reuse of tea oil camellia waste resources

The second stage of tempering

This thesis reports an investigation into the transformation of austenite during the second stage of tempering of a 1.86 % C experimental steel. A survey of relevant literature relating to phases, phase transformation, heat treatment and tempering in steel is presented in Chapters 1 to 4. Experimental work is presented in Chapters 5 to 7 and concerns the surface relief effects generated during the second stage, the characterisation of the carbide in the major product, the comparative effects of pre-existing martensite and bainite on the progress of the second stage and the habit plane of small platelets in the transformation product. The crystallographic properties of the products were studied using a combination of optical and transmission electron microscopy

A Prediction Approach for Video Hits in Mobile Edge Computing Environment

Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope

Modeling and Optimization for Collaborative Business Process Towards IoT Applications

The rapid development of Internet of Things (IoT) attracts growing attention from both industry and academia. IoT seamlessly connects the real world and cyberspace via various business process applications hosted on the IoT devices, especially on smart sensors. Due to the discrete distribution and complex sensing environment, multiple coordination patterns exist in the heterogeneous sensor networks, making modeling and analysis particularly difficult. In addition, massive sensing events need to be routed, forwarded and processed in the distributed execution environment. Therefore, the corresponding sensing event scheduling algorithm is highly desired. In this paper, we propose a novel modeling methodology and optimization algorithm for collaborative business process towards IoT applications. We initially extend the traditional Petri nets with sensing event factor. Then, the formal modeling specification is investigated and the existing coordination patterns, including event unicasting pattern, event broadcasting pattern, and service collaboration pattern, are defined. Next, we propose an optimization algorithm based on Dynamic Priority First Response (DPFR) to solve the problem of sensing event scheduling. Finally, the approach presented in this paper has been validated to be valid and implemented through an actual development system

A Soft Collaborative Robot for Contact‐based Intuitive Human Drag Teaching

Abstract Soft material‐based robots, known for their safety and compliance, are expected to play an irreplaceable role in human‐robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co‐bot) with intuitive and easy programming by contact‐based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra‐low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self‐sensing system, thus providing both precise actuation and dragging‐aware collaboration. Hence, the Soft Co‐bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co‐bots exhibit a high potential for safe and intuitive human‐robot collaboration in unstructured environments, promoting the immediate practical application of soft robots

New insights into transcriptome variation during cattle adipocyte adipogenesis by direct RNA sequencing

Summary: We performed direct RNA sequencing (DRS) together with PCR-amplified cDNA long and short read sequencing for cattle adipocyte at different stages. We proved that the DRS was with advantages to avoid artificial transcripts and questionable exitrons. Totally, we obtained 68,124 transcripts with information of alternative splicing, poly (A) length and mRNA modification. The number of transcripts for adipogenesis was expanded by alternative splicing, which lead regulation mechanisms far more complex than ever known. We detected 891 differentially expressed genes (DEGs). However, 62.78% transcripts of DEGs were not significantly differentially expressed, and 248 transcripts showed opposite changing directions with their genes. The poly (A) tail became globally shorter in differentiated adipocyte than in primary adipocyte, and had a weak negative correlation with gene/transcript expression. Moreover, the study of different mRNA modifications implied their potential roles in gene expression and alternative splicing. Overall, our study promoted better understanding of adipogenesis mechanisms in cattle adipocytes