The Feasibility and Method of News Mass Customization (NMC)

Mass Customization (MC) is a new production mode developed from the combination of Mass Production (MP) and single customization. News businesses putting the News Mass Customization (NMC) in force has its inherent advantages and NMC is a new way to get competence. The paper firstly analyzes two problems notation of MC and NewsML which have strong relation with NMC. Secondly, Compared with the MC rules in manufacturing industry, we draw the feasibility of NMC in News Businesses. Finally,according to the usage of Petri nets, we describe the NMC process and provide a detailed implement method

The potential for material circularity and independence in the U.S. steel sector

Achieving a U.S. circular economy would reduce environmental impacts and increase material independence. This article calculates maximum recycled contents (RCs) and recycling rates (RRs) in an independent U.S. steel sector, and estimates the potential to displace current imports with recycled scrap that is currently destined for landfill, hibernating stocks, or export (LHSE). A U.S. dynamic material flow analysis (1880–2100) is conducted to estimate annual steel consumption and scrap generation. The results are coupled with a linear optimization model that minimizes primary steel demand while satisfying the volumetric and compositional demands of new consumption. The compositional analysis examines only copper content because it is of greatest concern to recyclers.The best estimate is that the maximum independent RR is already constrained by copper contamination. Without interventions, this maximum RR will gradually decline throughout the century. The annual consumption to scrap availability ratio (C2SR) will decrease from around 1.4 today. Concurrently, the maximum RC rises but then plateaus below 75% as the RR falls. This highlights a conflict in the conditions for a circular economy: a C2SR approaching unity is a necessary condition for a high RC but leads to fewer opportunities for scrap contaminant dilution, which decreases the RR. Improved product design for recycling and deployment of scrap refining technologies will be needed to reach higher RCs. In 2017, the mass of U.S. scrap destined for LHSE exceeded direct steel imports. Domestic recycling of scrap exports alone could have displaced 36% of direct steel imports, reducing the U.S. deficit by $5.5 billion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156151/3/jiec12971-sup-0001-SuppMatS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156151/2/jiec12971_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156151/1/jiec12971.pd

Carbon Abatement Options for U.S. Transport and Industry

Annual anthropogenic greenhouse gas emissions must be cut by 40-70% by 2050 to limit global warming this century to 2o C above the pre-industrial temperature and avoid the worst consequences of climate change. This cut in global emissions is likely infeasible without U.S. decarbonization efforts equaling the global target. The transport and industry sectors account for 57% of U.S. GHG emissions. These two sectors must decarbonize and match the target if the U.S. is to achieve the necessary cut in emissions. Emissions from U.S. transport and industry are coupled with advanced transport technologies (e.g., battery electric vehicles (BEVs) with Li-ion batteries) typically requiring emissions-intensive manufacturing. Previous studies have largely ignored the transport-industry emissions nexus. Instead, this thesis presents a parametric fleet-scale production-use-disposal model that combines life cycle assessment with macro-level consumption parameters to calculate consumption based cumulative emissions and global temperature changes attributable to U.S. light duty vehicles (LDVs). Future pathways account for emerging powertrain technologies, electricity decarbonization, transport demand, recycling rates, and vehicle lifespans. Only 3% of the 1,512 modeled pathways meet the emissions target. Without aggressive actions, U.S. LDVs will likely exceed the cumulative emissions budget by 2039. Cumulative emissions are most sensitive to transport demand and the speed of fleet electrification and electricity decarbonization. Increasing production of BEVs to 100% of sales by 2040 (at the latest) is necessary, and early retirement of internal combustion engine vehicles is beneficial. Rapid electricity decarbonization minimizes emissions from BEV use and increasingly energy-intensive vehicle production. Deploying high fuel economy vehicles can increase emissions from the production of BEV batteries and lightweight materials. Increased recycling has only a small effect on these emissions because over the time period there are few batteries and lightweight materials available for recycling. A quarter of U.S. industry emissions are from the steel and aluminum sectors. Previous studies have shown that there are limited opportunities for further energy efficiency improvements in these upstream industries; however, increased material efficiency might prove fruitful, where services are delivered using less emissions-intensive materials produced from natural resources. Detailed material flow analyses (MFAs) are needed to identify the opportunities for material efficiency and to model the supply chain emissions. MFA construction is time consuming and fraught with missing and contradictory data. This thesis presents an easily updatable nonlinear least squares data reconciliation framework for MFA that is then applied to the annual U.S. steel flow. The MFA reveals key opportunities for U.S. steel material efficiency: increased manufacturing process yields and domestic recycling of landfilled and exported scrap. To understand the barriers to increased recycling, an optimal reverse supply chain model is derived using linear programming (LP). It shows that U.S. domestic steel and aluminum recycling is already constrained by compositional mismatches between the scrap streams and industry demand. The LP model is coupled with a dynamic material flow analysis to show that the increasing volumes of high-quality wrought aluminum being used in U.S. vehicles are likely to be downcycled or landfilled at vehicle end-of-life. The LP model is revised to show the potential for using emerging scrap separation and refining technologies to increase closed-loop recycling rates towards 90%. The technical assessments presented here highlight the scope for change. In future work, socioeconomic analyses could be coupled with these models to further assess the viability of the material efficiency strategies highlighted throughout.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/171347/1/yxzhu_1.pd

A Rapid Automatic Life Cycle Assessment Tool for Eco-Design

Increasing concerns about global warming, resource depletion, and ecosystem degradation are pushing manufacturing enterprises to consider environmental impacts of the products they make. Tools such as Life Cycle Assessment (LCA) has been developed to quantify environmental performance of a product, yet the implementation of LCA requires a significant amount of time/resources and its potential in assisting eco-design has been limited. Research has been done to conduct automatic LCA using the simplified database for electronics or to investigate the environmental impact of electricity consumption in a manufacturing process. However, a comprehensive and automated approach is in need to perform LCA analysis for a product considering all related materials and manufacturing processes. In this research, a framework for automating LCA analysis for eco-friendly product design has been developed and implemented with a computer program. Two case studies have been conducted using the proposed automatic LCA tool to conduct life cycle analysis in the design process. The result of the tool is able to, with minimal time required, provide detailed distribution of life cycle impact indicators among direct inputs and assist in making design decisions to reduce the environmental footprints

Understanding and Fine Tuning the Propensity of ATP-Driven Liquid-Liquid Phase Separation with Oligolysine

Liquid-Liquid Phase Separation (LLPS) plays pivotal roles in the organization and functionality of living cells. It is imperative to understand the underlying driving forces behind LLPS and to control its occurrence. In this study, we employed coarse-grained (CG) simulations as a research tool to investigate systems comprising oligolysine and adenosine triphosphate (ATP) under conditions of various ionic concentrations and oligolysine lengths. Consistent with experimental observations, our CG simulations captured the formation of LLPS upon the addition of ATP and tendency of dissociating under high ionic concentration. The primary driving force behind this phenomenon is the electrostatic interaction between oligolysine and ATP. An in-depth analysis on the structural properties of LLPS was conducted, where the oligolysine structure remained unchanged with increased ionic concentration and the addition of ATP led to a more pronounced curvature, aligning with the observed enhancement of $\alpha$-helical secondary structure in experiments. In terms of the dynamic properties, the introduction of ATP led to a significant reduction in translational and vibrational degrees of freedom but not rotational degrees of freedom. Through keeping the total number of charged residues constant and varying their entropic effects, we constructed two systems of similar biochemical significance and further validated the entropy effects on the LLPS formation. These findings provide a deeper understanding of LLPS formation and shed lights on the development of novel bioreactor and primitive artificial cells for synthesizing key chemicals for certain diseases

Study of Information Supply Chain and Artificial Neural Network’s Related Application

Users become less and less patient with huge useless data today. One of the great challenges now most net searching engines meet is how to get valuable information from lots of data sets. Aiming to satisfy every user’s special demand, we need to integrate and optimize the whole course of data searching, including adjusting the users’ input keywords, searching original results from network, and further processing of these results. Learning from the idea of Supply Chain Management, we put forward the concept of Information Supply Chain (ISC) in this paper to generalize the course above .For ISC’s optimization, artificial neural network is chosen as a tool to find out the relationships between different keywords and paper categories, which are summarized and stored in knowledge base. Based on it, the process of selecting proper keywords and searching news information could be more efficient. A pruning method named MW-OBS is illustrated to train ANN as well. Some details about the framework and components are also mentioned, especially on how an individual step in ISC works, what’s the relationship between them, and how they coordinate to meet every personal demand. ISC, an integrated information processing in the interests of users’ individual need, has great advantages over simple searching from network with original keywords

iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC.

Terminator is a DNA sequence that gives the RNA polymerase the transcriptional termination signal. Identifying terminators correctly can optimize the genome annotation, more importantly, it has considerable application value in disease diagnosis and therapies. However, accurate prediction methods are deficient and in urgent need. Therefore, we proposed a prediction method "iterb-PPse" for terminators by incorporating 47 nucleotide properties into PseKNC-Ⅰ and PseKNC-Ⅱ and utilizing Extreme Gradient Boosting to predict terminators based on Escherichia coli and Bacillus subtilis. Combing with the preceding methods, we employed three new feature extraction methods K-pwm, Base-content, Nucleotidepro to formulate raw samples. The two-step method was applied to select features. When identifying terminators based on optimized features, we compared five single models as well as 16 ensemble models. As a result, the accuracy of our method on benchmark dataset achieved 99.88%, higher than the existing state-of-the-art predictor iTerm-PseKNC in 100 times five-fold cross-validation test. Its prediction accuracy for two independent datasets reached 94.24% and 99.45% respectively. For the convenience of users, we developed a software on the basis of "iterb-PPse" with the same name. The open software and source code of "iterb-PPse" are available at https://github.com/Sarahyouzi/iterb-PPse

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds.

Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (Grid-robust Efficient Neural Interface for Universal Solvent-Excluded Surface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance

An soft-sensor method for the biochemical reaction process based on LSTM and transfer learning

Due to significant differences in data distribution under different working conditions during Pichia pastoris biochemical reaction process, traditional soft-sensor model suffer from the model failure and deterioration, this paper propose a soft-sensor modeling method combing long short-term memory network (LSTM) and balanced distribution adaptation method (BDA). Firstly, the source domain data is used to establish an accurate source domain LSTM prediction model, and the structure and parameters of the first layer of LSTM are fixed to migrate to the target domain prediction model. Then use the balanced distribution adaptation method to shrink the distribution differences between different domains of data. Finally, data that has been modeled with balanced and adaptive distribution assist the real-time data to train the remaining layer of the network, and the accurate target domain prediction model is finally obtained. The simulation results show that the mentioned method has the preponderance of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method. This method solves the problem of soft-sensor modeling under unknown modes of multiple operating conditions in Pichia pastoris biochemical reaction process, achieving the prediction of key parameters under different operating conditions, which can be widely applied, and also providing a new method for soft-sensor modeling of other non system systems