Repositioning fertilizer manufacturing subsidies for improving food security and reducing greenhouse gas emissions in China

China removed fertilizer manufacturing subsidies from 2015 to 2018 to bolster market-oriented reforms and foster environmentally sustainable practices.However,the impact of this policy reform on food security and the environment remains inadequately evaluated.Moreover,although green and low-carbon technologies offer environmental advantages,their widespread adoption is hindered by prohibitively high costs.This study analyzes the impact of removing fertilizer manufacturing subsidies and explores the potential feasibility of redirecting fertilizer manufacturing subsidies to invest in the diffusion of these technologies.Utilizing the China Agricultural University Agri-food Systems model,we analyzed the potential for achieving mutually beneficial outcomes regarding food security and environmental sustainability.The findings indicate that removing fertilizer manufacturing subsidies has reduced greenhouse gas(GHG)emissions from agricultural activities by 3.88 million metric tons,with minimal impact on food production.Redirecting fertilizer manufacturing subsidies to invest in green and low-carbon technologies,including slow and controlled-release fertilizer,organic-inorganic compound fertilizers,and machine deep placement of fertilizer,emerges as a strategy to concurrently curtail GHG emissions,ensure food security,and secure robust economic returns.Finally,we propose a comprehensive set of government interventions,including subsidies,field guidance,and improved extension systems,to promote the widespread adoption of these technologies.

Ballistic performance of additive manufacturing 316l stainless steel projectiles based on topology optimization method

Material and structure made by additive manufacturing(AM)have received much attention lately due to their flexibility and ability to customize complex structures.This study first implements multiple objective topology optimization simulations based on a projectile perforation model,and a new topologic projectile is obtained.Then two types of 316L stainless steel projectiles(the solid and the topology)are printed in a selective laser melt(SLM)machine to evaluate the penetration performance of the projectiles by the ballistic test.The experiment results show that the dimensionless specific kinetic energy value of topologic projectiles is higher than that of solid projectiles,indicating the better penetration ability of the topologic projectiles.Finally,microscopic studies(scanning electron microscope and X-ray micro-CT)are performed on the remaining projectiles to investigate the failure mechanism of the internal structure of the topologic projectiles.An explicit dynamics simulation was also performed,and the failure locations of the residual topologic projectiles were in good agreement with the experimental results,which can better guide the design of new projectiles combining AM and topology optimization in the future.

Treatment of Synovial Cysts in Relation to the Tibial Tunnel of Anterior Cruciate Ligament Grafts by Filling the Tunnel with Acrylic Cement

Introduction: Synovial cyst of the tibial tunnel in connection with anterior cruciate ligament (ACL) reconstruction is a rare but particularly troublesome complication. Medical treatment is often doomed to failure, and surgical treatment usually consists of excising the cyst and filling the tunnel with bone. The aim of this study was to evaluate the results of filling the tunnel with acrylic cement. Hypothesis: Filling the tibial bone tunnel with acrylic cement should eliminate communication between the joint cavity and the pre-tibial surface and prevent cyst recurrence. Patients and Methods: This retrospective series is composed of 13 patients, 9 men and 4 women, mean age 48.5 years (31 to 64) operated on between 2011 and 2019 for an intra- and extraosseous synovial cyst consecutive to the tibial tunnel of an ACL graft. Between 1983 and 2016, 12 of the patients had had a bone graft without bone block fixation (DI-DT or Mac Intosh) and one patient, a bone-bone transplant (KJ). The cyst was of variable size, located on the anteromedial aspect of the proximal end of the tibia, and often painful, warranting consultation. At the time of the initial operation, 9 patients had undergone meniscectomies (6 medial, 2 lateral, 1 double). In 7 knees, there were 7 cartilage lesions in the femorotibial and/or patellofemoral compartments (one stage 1 lesion, 2 stage 2 lesions, 4 stage 3 lesions, and no stage 4 lesions). Only 2 knees had neither cartilage nor meniscus lesions. After curettage of the bone tunnel /− removal of the non-resorbed or PEEK interference screw, the tunnel was filled with acrylic cement /− reinforced with a ligament staple to prevent expulsion. All patients underwent regular follow-up consultations until recovery. Results: At a maximum follow-up of 8 years, only 1 cyst recurred, representing a 7.69% failure rate. It was reoperated with another technique, which involved filling the tibial bone tunnel with bone graft taken from a half-bank head. After recovery, the cyst healed definitively. All patients were able to return to their previous activity within 15 days of surgery. Conclusion: Filling the tibial tunnel with acrylic cement reinforced /− with a ligament staple is a reliable and rapid solution for the treatment of intra- and extra-articular synovial cysts in relation to the tibial tunnel of ACL grafts.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Influence of manufacturing process-induced geometrical defects on the energy absorption capacity of polymer lattice structures

Modern additive manufacturing processes enable fabricating architected cellular materials of complex shape,which can be used for different purposes.Among them,lattice structures are increasingly used in applications requiring a compromise among lightness and suited mechanical properties,like improved energy absorption capacity and specific stiffness-to-weight and strength-to-weight ratios.A dedicated modeling strategy to assess the energy absorption capacity of lattice structures under uni-axial compression loading is presented in this work.The numerical model is developed in a non-linear framework accounting for the strain rate effect on the mechanical responses of the lattice structure.Four geometries,i.e.,cubic body centered cell,octet cell,rhombic-dodecahedron and truncated cuboctahedron 2+,are investigated.Specifically,the influence of the relative density of the representative volume element of each geometry,the strain-rate dependency of the bulk material and of the presence of the manufacturing process-induced geometrical imperfections on the energy absorption capacity of the lattice structure is investigated.The main outcome of this study points out the importance of correctly integrating geometrical imperfections into the modeling strategy when shock absorption applications are aimed for.

Bionic lightweight design of limb leg units for hydraulic quadruped robots by additive manufacturing and topology optimization

Galloping cheetahs,climbing mountain goats,and load hauling horses all show desirable locomotion capability,which motivates the development of quadruped robots.Among various quadruped robots,hydraulically driven quadruped robots show great potential in unstructured environments due to their discrete landing positions and large payloads.As the most critical movement unit of a quadruped robot,the limb leg unit(LLU)directly affects movement speed and reliability,and requires a compact and lightweight design.Inspired by the dexterous skeleton–muscle systems of cheetahs and humans,this paper proposes a highly integrated bionic actuator system for a better dynamic performance of an LLU.We propose that a cylinder barrel with multiple element interfaces and internal smooth channels is realized using metal additive manufacturing,and hybrid lattice structures are introduced into the lightweight design of the piston rod.In addition,additive manufacturing and topology optimization are incorporated to reduce the redundant material of the structural parts of the LLU.The mechanical properties of the actuator system are verified by numerical simulation and experiments,and the power density of the actuators is far greater than that of cheetah muscle.The mass of the optimized LLU is reduced by 24.5%,and the optimized LLU shows better response time performance when given a step signal,and presents a good trajectory tracking ability with the increase in motion frequency.

A survey of composite robot for aviation intelligent manufacturing application

With the rapid development of the aviation industry,the development of intelligent manufacturing equipment represented by composite robots has been paid close attention by the aviation industry.Based on the analysis of the background and main structure function of composite robots,this paper focuses on the analysis of key technologies such as composite robot hardware design,visual sensing and planning system,integrated control of‘hands,feet,and eyes',multi-robot collaborative operation,and safety.The typical applications of composite robots in aviation intelligent manufacturing such as automatic drilling and connection of aircraft,aircraft surface spraying and finishing,parts handling,aircraft measurement,and inspection are presented.The development trends such as standardization of composite robots,integration of‘5G+cloud computing+AI',and fusion of intelligent sensors are proposed.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

Research Progress of Additive Manufacturing Technology in Energetic Material Field at Home and Abroad

As a subversive manufacturing technology, additive manufacturing technology has many technical advantages such as high freedom of design and not limited by complex structure of parts. The application of additive manufacturing technology to the charge molding of energetic materials will subvert the traditional manufacturing concept of energetic materials, realize the advanced charge design concept, shorten the research and development time of weapons and equipment, and improve the comprehensive performance of weapons and equipment, which is of great significance for the rapid development of high-tech weapons and equipment. This paper analyzes the research progress of additive manufacturing technology in the field of energetic materials at home and abroad and puts forward some suggestions for future research of this technology. .

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Research on the Design and Application of Virtual and Real Integrated Training Platform Based on Intelligent Manufacturing

In this paper,we built a robot training platform using virtual simulation software,and the robot assembly,handling,and palletizing were realized.The workstation includes an industrial robot,gas control unit,track function module,assembly function module,palletizing function module,vision module,etc.,and robot movement is achieved through language programming.The platform provides conditions for the practical ability training of application-oriented talents.

Analysis of the Effectiveness of Modified Non- Traumatic Filling Technology in the Treatment of Pediatric Dental Caries

Objective:To analyze the effectiveness of modified non-traumatic filling technology in the treatment of pediatric dental caries.Methods:Ninety-seven children with dental caries who were treated in our hospital(Panyu Maternal and Child Care Service Center of Guangzhou)from January 2022 to December 2023 were selected and randomly divided into two groups,with 48 cases in the experimental group and 49 in the control group.The experimental group was treated with modified non-traumatic filling techniques,while the control group was treated with conventional filling techniques.Observation indicators such as the total effectiveness of the treatment,incidence of adverse events,treatment compliance,and pain scores were analyzed after the intervention.Result:After intervention,the total effectiveness of the treatment in the experimental group was higher than that in the control group(P<0.05).The incidence of adverse events in the experimental group was lower than that of the control group(P<0.05).The treatment compliance of patients in the experimental group was higher than that in the control group(P<0.05).The pain score of the experimental group was lower than that of the control group(P<0.05).Conclusion:The application of modified non-traumatic filling technology in the treatment of pediatric dental caries showed good therapeutic outcomes.After the intervention,the child’s symptoms were significantly alleviated,the incidence of adverse events such as filling material falling off was reduced,their compliance was improved,and the pain was relieved.This procedure is worth to be promoted for clinical application.

Evaluation of Energy Efficiency and Analysis of Influencing Factors of Company CW’s Manufacturing Workshops

In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.

Exploration and Application Practice of Energy Conservation Theory and Method in Steel Manufacturing Process System

This article briefly discusses the theoretical basis and overall goals of energy conservation in the steel manufacturing process system.It is proposed that in the process of implementing system energy conservation,it is necessary to fully recognize and utilize the characteristics and functional advantages of the steel manufacturing process,pay more attention to energy quality,firmly grasp the overall goal of system optimization,focus on the integrated optimization of gas,steam,and waste heat systems,and propose the idea of constructing a"steel chemi-cal gas electricity heating cooling multi generation system".Based on practice,the main principles,models,and effects of implementing systematic energy conservation in steel enterprises have been proposed.

Additive manufacturing of magnesium matrix composites: Comprehensive review of recent progress and research perspectives

The magnesium matrix composites(MMCs) formed by introducing reinforcements to magnesium alloys overcome the limitations of the mechanical properties to a certain extent, presenting unique and excellent properties that any component does not have, such as high specific stiffness and specific strength, good dimensional stability, outstanding shock absorption performance, excellent electromagnetic shielding and hydrogen storage characteristics, etc. As an emerging manufacturing technology, additive manufacturing(AM) is based on the design of threedimensional(3D) data model to obtain 3D objects through layer-by-layer processing, which possesses the advantages of short manufacturing cycle, high material utilization rate, high degree of design freedom, excellent mechanical properties and the ability to fabricate complex structural components. Combining the high stiffness and high strength properties of MMCs and the technical advantages of AM forming complex structural parts with high performance, the prepared AM MMCs have huge potential advantages and broad application prospects in new high-tech industries such as automobile, aerospace, consumer electronics and biomedicine, etc. This paper reviews the research progress in the field of AM MMCs, mainly introduces the main AM technologies, including selective laser melting(SLM), electron beam selective melting(EBSM), laser engineered net shaping(LENS) and wire and arc additive manufacturing(WAAM). The formation mechanism and control methods of the typical defects including balling effect, porosity, poor fusion, loss of alloy elements and cracks produced during AM are discussed. The main challenges of AM MMCs are proposed from the aspects of composition design and the preparation of powder raw material. The relationship between the microstructure and mechanical properties, corrosion performance and biocompatibility of AM MMCs are elaborated in detail. The application potential of AM MMCs in various fields at present and in the future is introduced. Finally, the development direction and urgent problems to be solved in the AM MMCs are prospected.

In-situ deposition of apatite layer to protect Mg-based composite fabricated via laser additive manufacturing

Biodegradable magnesium(Mg) and its alloy show huge potential as temporary bone substitute due to the favorable biocompatibility and mechanical compatibility. However, one issue deserves attention is the too fast degradation. In this work, mesoporous bioglass(MBG)with high pore volume(0.59 cc/g) and huge specific surface area(110.78 m^(2)/g) was synthesized using improved sol-gel method, and introduced into Mg-based composite via laser additive manufacturing. Immersion tests showed that the incorporated MBG served as powerful adsorption sites, which promoted the in-situ deposition of apatite by successively adsorbing Ca2+and HPO42-. Such dense apatite film acted as an efficient protection layer and enhanced the corrosion resistance of Mg matrix, which was proved by the electrochemical impedance spectroscopy measurements. Thereby, Mg based composite showed a significantly decreased degradation rate of 0.31 mm/year. Furthermore,MBG also improved the mechanical properties as well as cell behavior. This work highlighted the advantages of MBG in the fabrication of Mg-based implant with enhanced overall performance for orthopedic application.

A review on stress determination and control in metal-based additive manufacturing

Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly localized energy can produce large temperature gradients,which will,in turn,lead to compressive and tensile stress during the MAM process and eventually result in residual stress.Being an issue of great concern,residual stress,which can cause distortion,delamination,cracking,etc.,is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts.In this review paper,the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models.We also provide insight on what still requires to be achieved and the research opportunities and challenges.

Laser additive manufacturing of zinc:formation quality,texture,and cell behavior

Laser powder bed fusion(LPBF)makes it possible for biodegradable zinc(Zn)to be used to produce customized orthopedic implants.In this research,we investigate the impact of laser power and scanning speed on the development of surface quality,relative densification,and texture during LPBF of Zn implants.Increasing laser power was able to decrease melt viscosity and surface tension,which improved the metallurgical bonding between adjacent tracks.Uneven and twisted tracks also became continuous and straight.Scanning speed could controlmolten-pool temperature to restrain grain natural orientation,achieving various crystal orientations and a weakened texture.Importantly,it further avoided the thermal expansion and contraction caused by excessive energy storage and accumulation in the matrix,thus reducing the generation of high-dislocation density.As a result,by selecting a reasonable laser power and scanning speed,the LPBF parts exhibited a flat surface morphology and a high density over 99.5%.Their average hardness,mechanical strength,and elongation reached 50.2 HV,127.8 MPa,and 7.6%,respectively.Additionally,the parts displayed a moderate degradation rate and excellent osteogenic properties.All these results provide a basis for selecting process parameters to optimize the comprehensive properties of LPBF-processed Zn parts for biodegradable applications.