Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb

The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 points.To solve these problems,a new process using anisole as the extractant was proposed.Anisole exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing steps.After alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried out.The process was modeled and optimized.The design space was constructed.The recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid concentration.The new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer rate.The sinomenine transfer rate was about 20%higher than that of industrial processes.The greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry.

Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization

This work provides an overview of distillation processes,including process design for different distillation processes,selection of entrainers for special distillation processes,system integration and intensification of distillation processes,optimization of process parameters for distillation processes and recent research progress in dynamic control strategies.Firstly,the feasibility of using thermodynamic topological theories such as residual curve,phase equilibrium line and distillation boundary line to analyze different separation regions is discussed,and the rationality of distillation process design is discussed by using its feasibility.Secondly,the application of molecular simulation methods such as molecular dynamics simulation and quantum chemical calculation in the screening of entrainer is discussed for the extractive distillation process.The thermal coupling mechanism of different distillation processes is used to explore the process of different process intensifications.Next,a mixed integer nonlinear optimization strategy for the distillation process based on different algorithms is introduced.Finally,the improvement of dynamic control strategies for different distillation processes in recent years is summarized.This work focuses on the application of process intensification and system optimization in the design of distillation process,and analyzes the challenges,prospects,and development trends of distillation technology in the separation of multicomponent azeotropes.

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.

Application of Natural Language Processing in Virtual Experience AI Interaction Design

This paper investigates the application of Natural Language Processing (NLP) in AI interaction design for virtual experiences. It analyzes the impact of various interaction methods on user experience, integrating Virtual Reality (VR) and Augmented Reality (AR) technologies to achieve more natural and intuitive interaction models through NLP techniques. Through experiments and data analysis across multiple technical models, this study proposes an innovative design solution based on natural language interaction and summarizes its advantages and limitations in immersive experiences.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Flexible Fixture Design for the Top and End Surface Processing of NP2_NP4 Cylinder Heads

This paper focuses on the design of fixtures for NP2 and NP4 cylinder heads on a horizontal machining center of flexible machining automatic lines.It began with an analysis of the diagrams of part processing and working procedure which formed the basis for the design of the processing technology scheme,a selection of suitable machine tools,and the setting of processing parameters.Fixtures tailored to the chosen machine tools were then designed to meet the processing requirements.Additional aspects of the project included the design of part drawings,calculation of working time quota,design of auxiliary guides,support clamping,and hydraulic circuits,all aimed at fulfilling practical production requirements.

The Impact of AIGC on the Design Process of Cultural and Creative Education Products and Its Management Implications

Cultural and creative education products play a crucial role in modern education,as they can enhance students’creativity and cultural understanding.In the field of cultural and creative product development,Artificial Intelligence Generated Content(AIGC)has not yet been maturely applied,while data-driven design methods can achieve personalized and efficient design outputs,thus facilitating the creative generation and rapid iteration of AIGC.This study aims to explore the application of AIGC in the development of cultural and creative education products,and to form a future-oriented design process transformation in combination with rapid output of data analysis.By building a database of cultural elements and user preferences related to educational aspects in cultural and creative education products,training the AIGC system using machine learning technology,and submitting the design drafts formed in the near term to designers for further optimization,the product is finally subjected to user feedback and market testing,with products that are highly accepted by users as the final output.The research results show that the use of AIGC can not only promote innovation in cultural and creative education products,improve design efficiency and product diversity,but also inspire more creative inspiration for designers.The advantage of data analysis further enhances the accuracy of product development and market response speed,achieving effective transformation of the design process.Moreover,this research provides valuable references for educational management in terms of resource allocation and curriculum design.

Optimization of Ti-5Al-2.5Sn Chemical Milling Process Parameters Using Taguchi Design for Enhanced Performance

One of the nontraditional manufacturing processes is the chemical machining that deals with the removal of material substances using acidic or alkaline chemical solutions. This study aimed to determine the maximum material removal rate(MRR), and minimum surface roughness(SRa) of Ti-5Al-2.5Sn alloy during chemical milling that is possible to achieve by varying the etching chemical milling parameters in terms of time, concentration of the chemical solution [hydrofluoric acid(HF) and nitric acid(HNO_(3))], and chemical milling temperature. The Taguchi method based on a statistical design of experiments(DOE) technique with an L_(16) orthogonal array is efficiently used to obtain the objective of this study and to detect optimal chemical milling parameters for the Ti-5Al-2.5Sn alloy. The experimental results were analyzed using ANOVA analysis to determine the importance of each system parameter on the response variables(MRR and SRa). The optimal process parameters were found to be at a chemical solution concentration of 22.5% HF and 17% HNO_(3), a temperature of 45 ℃, and a time of 60 min. These parameters resulted in a maximum MRR of 0.0842 mg/min and a minimum SRa of 0.30 μm. The ANOVA result signalized that the concentration of the etching acids has the most impact on both responses with contribution percentages of 81% and 67% respectively. This takes a look at the efficacy of the Taguchi technique in optimizing chemical milling procedures and offers precious insights for selecting process parameters to attain favored results.

Dendritic spine degeneration:a primary mechanism in the aging process

Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

An Unprecedented Efficiency with Approaching 21%Enabled by Additive‑Assisted Layer‑by‑Layer Processing in Organic Solar Cells

Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.

Design and Research on Virtual Display of Whole Process Mechanization for Rice Production in Cold Region

With the development of society,the virtual reality technology and multimedia technology have been applied to modern large-scale agriculture. The paper describes the design ideas for animation display of mechanized rice production in cold region,and elaborates the design of major part of each scene. By animation display,we can know about the whole process of rice production,as well as the benefits of mechanized operation.

Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys

In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector module was presented.A high-precision forward prediction model based on deep neural networks was developed to implement these two parts.Of utmost importance,domain knowledge-guided inverse design networks(DKIDNs)and regular inverse design networks(RIDNs)were also developed.The forward prediction model possesses a coefficient of determination(R^(2))of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set.Furthermore,the DKIDNs model exhibits superior performance compared to the RIDNs model.It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.

Processing,microstructure,and mechanical properties of wire arc additively-manufactured AZ91 magnesium alloy using cold arc process

Thin walls of an AZ91 magnesium alloy with fine equiaxed grains were fabricated via cold arc-based wire arc additive manufacturing(CA-WAAM),and the droplet transfer behaviours,microstructures,and mechanical properties were investigated.The results showed that the cold arc process reduced splashing at the moment of liquid bridge breakage and effectively shortened the droplet transfer period.The microstructures of the deposited samples exhibited layered characteristics with alternating distributions of coarse and fine grains.During layer-by-layer deposition,the β-phase precipitated and grew preferentially along grain boundaries,while the fineη-Al_(8)Mn_(5)phase was dispersed in the α-Mg matrix.The mechanical properties of the CA-WAAM deposited sample showed isotropic characteristics.The ultimate tensile strength and elongation in the building direction(BD)were 282.7 MPa and 14.2%,respectively.The microhardness values of the deposited parts were relatively uniform,with an average value of HV 69.6.

Design Strategies Applied to Classroom's Daylight Design Optimization of Classrooms Design

This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virtual models of classrooms were evaluated using the Radiance software.We used a methodology that allowed us to determine the luminous conditions under different types of skies,seasons of the year and times of the day.The evaluation of the typologies was performed based on three defined criteria,in order to achieve the stated design objectives.We defined the optimal solutions for each orientation and,finally,we stated design recommendations for daylit classrooms to ensure the visual comfort of the students.These recommendations link all that found in the initial analysis with that found in the optimization stage.

Design strategies and insights of flexible infrared optoelectronic sensors

Infrared optoelectronic sensing is the core of many critical applications such as night vision,health and medication,military,space exploration,etc.Further including mechanical flexibility as a new dimension enables novel features of adaptability and conformability,promising for developing next-generation optoelectronic sensory applications toward reduced size,weight,price,power consumption,and enhanced performance(SWaP^(3)).However,in this emerging research frontier,challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated systems.Therefore,we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors,including the fundamentals of infrared photodetectors,selection of materials and device architectures,fabrication techniques and design strategies,and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image sensing.Finally,this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials,covering challenges in materials development and device micro-/nanofabrication.Benchmarks for scaling these techniques across fabrication,performance,and integration are presented,alongside perspectives on potential applications in medication and health,biomimetic vision,and neuromorphic sensory systems,etc.

Influence of material characteristics on the failure mode and process of landslide dam

Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the stability of landslide dams and the potential for dam breaches.To explore the failure mechanisms and evolutionary processes of landslide dams with varying soil properties,this study conducted a series of flume experiments,considering different grain compositions and material densities.The results demonstrated that grading characteristics significantly influence landslide dam stability,affecting failure patterns,breach processes,and final breach morphologies.Fine-graded materials exhibited a sequence of surface erosion,head-cut erosion,and subsequent surface erosion during the breach process,while well-graded materials typically experienced head-cut erosion followed by surface erosion.In coarse-graded dams,the high permeability of coarse particles allowed the dam to remain stable,as inflows matched outflows.The dam breach model experiments also showed that increasing material density effectively delayed the breach and reduced peak breach flow discharge.Furthermore,higher fine particle content led to a reduction in the residual dam height and the base slope of the final breach profile,although the relationship between peak breach discharge and the content of fine and coarse particles was nonlinear.To better understand breach morphology evolution under different soil characteristics and hydraulic conditions,three key points were identified—erosion point,control point,and scouring point.This study,by examining the evolution of failure patterns,breach processes,and breach flow discharges under various grading and density conditions,offers valuable insights into the mechanisms behind landslide dam failures.

Seismic isolation design and resilience improvement of railway station considering the influence of near-fault pulse-like ground motions

To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The seismic resilience of the no-isolation railway stations(NIRS)and the isolation railway stations(IRS)were compared to provide a numerical result of the improvement in resilience.The results show that in the station isolation design,the station's functional requirements and structural characteristics should be considered and the appropriate placement of isolation bearings is under the waiting room.Under the action of a rare earthquake,the repair cost,repair time,rate of harm and death of the IRS were decreased by 8.04 million,18.30 days,6.93×10^(-3)and 1.21×10^(-3),respectively,when compared to the NIRS.The IRS received a seismic resilience grade of three-stars and the NIRS only one-star,indicating that rational isolation design improves the seismic resilience of stations.Thus,for the design of stations close to earthquake faults,it is suggested to utilize appropriate isolation techniques to improve their seismic resilience.

采用Process Designer的白车身焊装生产线工艺规划研究

白车身焊装生产线工艺规划是决定汽车整车生产周期的重要环节。以先进的数字化工厂技术软件——ProcessDesigner为平台,通过创建制造信息模型和工艺过程模型,实现对白车身焊装生产线的生产信息管理和工艺过程规划,构建了白车身焊装生产线工艺规划系统,并详细描述了其工艺规划实现的具体方法和步骤,最后以某型白车身底板焊装线为例,生成了基于规划系统的数字化三维工厂,对规划结果进行了验证。结果表明,利用该系统进行焊装工艺规划能显著缩短工艺规划时间,提高工艺规划的准确性。

Ultra-processed food,obesity,and colon cancer:A systematic review and meta-analysis

BACKGROUND Recently,there has been a significant increase in the consumption of ultraprocessed foods worldwide.However,the association between the consumption of ultra-processed food,obesity,and the prevalence of colon cancer remains controversial.AIM To find out the association between the consumption of ultra-processed food,obesity,and the prevalence of colon cancer.METHODS A comprehensive systematic literature search of PubMed,Scopus,Web of Science,and Google Scholar for grey literature was done for articles published before 8th March 2023.The search was done to retrieve potential peer-reviewed articles that explored the association between the consumption of ultra-processed food,obesity,and the prevalence of colon cancer.RESULTS Of the 246 potential articles assessed,17 met the inclusion criteria.Meta-analysis results demonstrated that high consumption of ultra-processed food is associated with an increased risk of obesity[odds ratio(OR):1.65;95%CI:1.07-2.45;P<0.05].Consequently,there is a positive association between obesity and an increased risk of colon cancer(OR 1.48;95%CI:0.77-2.87;P>0.05).CONCLUSION Consuming ultra-processed foods increases the risk of obesity and colon cancer.