In situ Synthesis of Au-Induced Hierarchical Nanofibers/Nanoflakes Structured BiFeO3 Homojunction Photocatalyst With Enhanced Photocatalytic Activity

In order to further improve the photocatalytic performance of BiFeO3 (BFO), novel Au-induced hierarchical nanofibers/nanoflakes structured BiFeO3 homojunctions (Aux-BFO, x = 0, 0. 6, 1.2, 1.8, 2.4 wt%) were in situ synthesized through a simple reduction method with assist of sodium citrate under the analogous hydrothermal environment. The effect of loading amount of Au nanoparticles (NPs) on the physicochemical properties and photocatalytic activity was investigated in detail. The Au1.2-BFO NFs sample show the best photocatalytic activity (85.76%), much higher than that for pure BFO samples (49.49%), mainly due to the hierarchical nanofibers/nanoflakes structured homojunction, the surface plasmon resonance (SPR) effect of Au NPs, as well as the presence of defects (Fe2+/Fe3+ pairs and oxygen vacancy). Furthermore, the possible formation mechanism of the unique homojunction and the enhanced photocatalytic mechanism for the degradation of methylene blue (MB) dye are proposed. It is proven that holes (h+) play the decisive role in the photocatalytic process. The present work provides a fascinating way to synthesize efficient homojunctions for the degradation of organic pollutes

Ultrasonic monitoring for adhesive materials

Resins play an extremely crucial role in many applications from dental composites, pipe insulator and adhesive to shellac. Among the wide categories of resins, thermosetting resins such as epoxies are well-known for its outstanding mechanical properties, high resistivity to environmental degradation and high adhesive strength. The epoxy employed in this experiment is Loctite EA 9396, which is a two-part adhesive of tetraglycidyl diaminodiphenylmethane and epichlorohydrin-4,4’-isopropylidene diphenol resin. A non-destructive testing technique, Ultrasound would also be employed. The objective of this project is to characterize the material properties of Loctite EA 9396 and investigate the possibility to use ultrasonic bulk waves for the monitoring of modulus of adhesive materials. The transmission of ultrasonic waves to determine the cure state of epoxy will be discussed in this report, since longitudinal and shear velocity are components of Young’s modulus and Poisson’s ratio. The fundamentals of ultrasound and graphs of amplitude with respect to time are also examined. In this report, the recent experiments conducted on ultrasonic cure monitoring will be analyzed to show the reliability of ultrasonic wave propagation for characterization of the cure state of epoxy. It would also focus on the principles of ultrasound testing on epoxy.Bachelor of Engineering (Mechanical Engineering

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

ABSTRACT: Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management

Investigation of the efficacy and feasibility of combined therapy of PD‐L1‐enhanced exogenous peripatetic adoptive natural killer (NK) cells in combination with antiangiogenic targeted therapy in the treatment of extensive‐stage small cell lung cancer

Abstract A 67‐year‐old male patient presented with extensive‐stage small cell lung cancer with the primary lesion located in the right upper lung, accompanied by multiple metastases to the pleura and abdominal cavity with enlarged mediastinal lymph nodes. A combination therapy approach was used to target the patient's multiple systemic metastases after localized radiotherapy. The approach involved adoptive transfer of programmed death ligand 1 (PD‐L1) enhanced exogenous natural killer (NK) cells, along with antiangiogenic treatment. Allogeneic cord blood NK cells were infused back into the patient over two consecutive days. On the first day, the treatment was followed by a dose of 1200 mg of atezolizumab. Subsequently, the patient received a daily dose of 10 mg of anlotinib administered orally for 14 days. This was followed by a 7‐day break, and each cycle lasted 21 days. After delivering localized radiation to the primary lesion in the right lung and metastatic mediastinal lymph nodes, complete remission was achieved in the local lesion, effectively avoiding the risk of superior vena cava syndrome. Following six cycles of combined therapy, most of the metastatic lesions had disappeared, and the remaining metastatic lesions had significantly reduced in size. The recent therapeutic effect resulted in partial remission. The combination therapy of immune checkpoint inhibitor PD‐L1‐enhanced exogenous adoptive transfer NK cells, along with antiangiogenic targeted treatment, demonstrated a satisfactory short‐term effect, with disappearance of most of the metastases and noticeable shrinkage in the remaining metastatic lesions

Digital Twin for Agricultural Machinery: From Concept to Application

SignificanceAgricultural machinery serves as the fundamental support for implementing advanced agricultural production concepts. The key challenge for the future development of smart agriculture lies in how to enhance the design, manufacturing, operation, and maintenance of these machines to fully leverage their capabilities. To address this, the concept of the digital twin has emerged as an innovative approach that integrates various information technologies and facilitates the integration of virtual and real-world interactions. By providing a deeper understanding of agricultural machinery and its operational processes, the digital twin offers solutions to the complexity encountered throughout the entire lifecycle, from design to recycling. Consequently, it contributes to an all-encompassing enhancement of the quality of agricultural machinery operations, enabling them to better meet the demands of agricultural production. Nevertheless, despite its significant potential, the adoption of the digital twin for agricultural machinery is still at an early stage, lacking the necessary theoretical guidance and methodological frameworks to inform its practical implementation.ProgressDrawing upon the successful experiences of the author's team in the digital twin for agricultural machinery, this paper presents an overview of the research progress made in digital twin. It covers three main areas: The digital twin in a general sense, the digital twin in agriculture, and the digital twin for agricultural machinery. The digital twin is conceptualized as an abstract notion that combines model-based system engineering and cyber-physical systems, facilitating the integration of virtual and real-world environments. This paper elucidates the relevant concepts and implications of digital twin in the context of agricultural machinery. It points out that the digital twin for agricultural machinery aims to leverage advanced information technology to create virtual models that accurately describe agricultural machinery and its operational processes. These virtual models act as a carrier, driven by data, to facilitate interaction and integration between physical agricultural machinery and their digital counterparts, consequently yielding enhanced value. Additionally, it proposes a comprehensive framework comprising five key components: Physical entities, virtual models, data and connectivity, system services, and business applications. Each component's functions operational mechanism, and organizational structure are elucidated. The development of the digital twin for agricultural machinery is still in its conceptual phase, and it will require substantial time and effort to gradually enhance its capabilities. In order to advance further research and application of the digital twin in this domain, this paper integrates relevant theories and practical experiences to propose an implementation plan for the digital twin for agricultural machinery. The macroscopic development process encompasses three stages: Theoretical exploration, practical application, and summarization. The specific implementation process entails four key steps: Intelligent upgrading of agricultural machinery, establishment of information exchange channels, construction of virtual models, and development of digital twin business applications. The implementation of digital twin for agricultural machinery comprises four stages: Pre-research, planning, implementation, and evaluation. The digital twin serves as a crucial link and bridge between agricultural machinery and the smart agriculture. It not only facilitates the design and manufacturing of agricultural machinery, aligning them with the realities of agricultural production and supporting the advancement of advanced manufacturing capabilities, but also enhances the operation, maintenance, and management of agricultural production to better meet practical requirements. This, in turn, expedites the practical implementation of smart agriculture. To fully showcase the value of the digital twin for agricultural machinery, this paper addresses the existing challenges in the design, manufacturing, operation, and management of agricultural machinery. It expounds the methods by which the digital twin can address these challenges and provides a technical roadmap for empowering the design, manufacturing, operation, and management of agricultural machinery through the use of the digital twin. In tackling the critical issue of leveraging the digital twin to enhance the operational quality of agricultural machinery, this paper presents two research cases focusing on high-powered tractors and large combine harvesters. These cases validate the feasibility of the digital twin in improving the quality of plowing operations for high-powered tractors and the quality of grain harvesting for large combine harvesters.Conclusions and ProspectsThis paper serves as a reference for the development of research on digital twin for agricultural machinery, laying a theoretical foundation for empowering smart agriculture and intelligent equipment with the digital twin. The digital twin provides a new approach for the transformation and upgrade of agricultural machinery, offering a new path for enhancing the level of agricultural mechanization and presenting new ideas for realizing smart agriculture. However, existing digital twin for agricultural machinery is still in its early stages, and there are a series of issues that need to be explored. It is necessary to involve more professionals from relevant fields to advance the research in this area

Agronomic evaluation of nutrient management for potato in Northwest China

In northwest China the lack of knowledge on soil nutrient fertility and of appropriate nutrient management practices has restricted potato production. Field trials were conducted from 2002 to 2007 to determine the main limiting nutrients for potato (Solanum tuberosum L), and to evaluate the nutrient management practice (NMP) based on an Agro Services International (ASI) systematic approach. Results indicated that average potato yields in nutrient omission plots were in the order 0-N (22.2 t ha-1) ≈ 0-P (22.4 t ha-1) < 0-K (31.5 t ha-1). N deficiency is a general feature of irrigated potato in northwest region, but P and K supply are frequently additional limiting factors. The mean agronomic efficiencies (AE) of N, P and K were 34.4 kg tuber kg-1 N, 32.4 kg tuber kg-1 P2O5 and 41.3 kg tuber kg-1 K2O, respectively, and the mean partial factor productivities (PFP) of N, P and K were 220 kg tuber kg-1 N, 291 kg tuber kg-1 P2O5 and 306 kg tuber kg-1 K2O, respectively. There was a significant negative relationship between AE or PFP of N, P or K and the respective nutrient rate. The positive relationship between yield of nutrient omission plots and PFP and AE suggests nutrient use efficiency is affected by soil indigenous productivity. NMP based on an ASI systematic approach produced significant higher yields, nutrient use efficiencies and economic returns than farmer’s practice

Heterogeneous root zone salinity mitigates salt injury to Sorghum bicolor (L.) Moench in a split-root system.

The heterogeneous distribution of soil salinity across the rhizosphere can moderate salt injury and improve sorghum growth. However, the essential molecular mechanisms used by sorghum to adapt to such environmental conditions remain uncharacterized. The present study evaluated physiological parameters such as the photosynthetic rate, antioxidative enzyme activities, leaf Na+ and K+ contents, and osmolyte contents and investigated gene expression patterns via RNA sequencing (RNA-seq) analysis under various conditions of nonuniformly distributed salt. Totals of 5691 and 2047 differentially expressed genes (DEGs) in the leaves and roots, respectively, were identified by RNA-seq under nonuniform (NaCl-free and 200 mmol·L-1 NaCl) and uniform (100 mmol·L-1 and 100 mmol·L-1 NaCl) salinity conditions. The expression of genes related to photosynthesis, Na+ compartmentalization, phytohormone metabolism, antioxidative enzymes, and transcription factors (TFs) was enhanced in leaves under nonuniform salinity stress compared with uniform salinity stress. Similarly, the expression of the majority of aquaporins and essential mineral transporters was upregulated in the NaCl-free root side in the nonuniform salinity treatment, whereas abscisic acid (ABA)-related and salt stress-responsive TF transcripts were more abundant in the high-saline root side in the nonuniform salinity treatment. In contrast, the expression of the DEGs identified in the nonuniform salinity treatment remained virtually unaffected and was even downregulated in the uniform salinity treatment. The transcriptome findings might be supportive of the increased photosynthetic rate, reduced Na+ levels, increased antioxidative capability in the leaves and, consequently, the growth recovery of sorghum under nonuniform salinity stress as well as the inhibited sorghum growth under uniform salinity conditions. The increased expression of salt resistance genes activated in response to the nonuniform salinity distribution implied that the cross-talk between the nonsaline and high-saline sides of the roots exposed to nonuniform salt stress is potentially regulated