Model-based optimization of viral capsid protein production in fed-batch culture of recombinant <Emphasis Type="Italic">Escherichia coli</Emphasis>

An optimized fed-batch cultivation process for the production of the polyoma virus capsid protein VP1 in recombinant Escherichia coli BL21 bacteria is presented. The optimization procedure maximizing the amount of desired protein is based on a mathematical model. The model distinguishes an initial cell growth phase from a protein production phase initiated by inducer injection. A new approach to model the target protein formation rate was elaborated, where product formation is primarily dependent on the specific biomass growth rate. Lower growth rates led to higher specific protein concentrations. The model was identified from a series of fed-batch experiments designed for parameter identification purposes and possesses good prediction quality. Then the model was used to determine optimal open-loop control profiles by manipulating the substrate feed rates in both phases as well as the induction time. Feed-rate optimization has been solved using Pontryagin's maximum principle. The solution was validated experimentally. A significant improvement of the process performance index was achieved.     

Integrated Material Flow Analysis and Process Modeling to Increase Energy and Water Efficiency of Industrial Cooling Water Systems

Cooling water systems (CWS) are one of the main energy and water using operations in industry. Existing CWS in operation provide high improvement potentials in environmental and economic performance through optimized operation and system control. Industry often fails to realize these potentials, given that the efficiency measures as well as their technical, economic, and ecological impact are mostly unknown because of the lack of appropriate approaches. This article presents a holistic approach to the systematic identification and assessment of efficiency measures that support industry in improving the operation and system control of large‐scale CWS consisting of one or multiple cooling towers, heat exchangers, and pumps. Based on material flow analysis coupled with process modeling, a material and energy flow model of CWS is developed. The model enables the investigation of different adjustments in operation of CWS in order to identify and assess specific efficiency measures. The approach is applied to a CWS of a real manufacturing facility. The results show, first, high validity of the approach as compared to a real system. Second, the effectiveness of the approach, given that the model allows fast and simple identification and assessment of efficiency measures that save up to 16% energy and 24% water in the presented case study.     

A diagnostic model for green productivity assessment of manufacturing processes

Goal, Scope and Background  Green Productivity (GP) is a new paradigm in sustainable manufacturing where resource conservation and waste minimization constitute the strategy in simultaneously enhancing environmental performance and productivity. This productivity approach to the sustainability of industries requires the adoption of clean production technology and the development of appropriate indicators and instruments to measure environmental performance in a continuous improvement strategy that focuses on the manufacturing stage of the product life cycle. The analysis may be expanded to include the entire life cycle with increasing details on impacts, improvement strategies and indicators. Methods  The study proposes a methodology for GP assessment that integrates the essential components of life cycle assessment (LCA) and multicriteria decision analysis specifically the analytic hierarchy process (AHP). LCA provides a systematic and holistic perspective for GP analysis that spans inventory, impact and improvement assessment. The AHP is utilized as a decision framework and valuation tool for impact and improvement assessment to come up with priority weights. Indicators are derived and measured from a streamlined LCA focused on a number of parameters within the gate-to-gate analysis to demonstrate the GP concept in relation to resource utilization and waste minimization. An input-output approach using a suitable material balance in a scenario analysis provides the basis of GP performance measurement. Results and Conclusion  The diagnostic model is applied on a semiconductor assembly/packaging operation. From the streamlined life cycle inventory, impact factors were derived for water resource depletion (WRD), energy resource depletion (ERD), human toxicity-air (HTA), human toxicity-land (HTL), human toxicity-water (HTW), aquatic ecotoxicity (ETA) and terrestrial ecotoxicity (ETT). Valuation of impact factors using the AHP showed the high significance of ETT, HTL, WRD and ERD. This especially reflects the impact of the industry on the solid waste problem as a result of emissions to land associated with human toxicity and ecotoxicity effects and the intensive use of water and energy resources. Using scenario analysis, the effect of implementing a process-based improvement technique on a product-specific operation was determined and the highest values in GP are for energy utilization, water utilization and terrestrial ecotoxicity. Recommendation and Perspective  Expert system technology was explored in developing a diagnostic prototype that emulates how human experts diagnose green productivity of manufacturing processes. The aim was to investigate how such a diagnosis could be performed in an intelligent fashion that it is also easily accessible as a decision support for industries. The expert system model will provide flexibility in testing the relationships of environmental performance and productivity parameters as well as in preserving and disseminating valuable human expertise in GP program implementation. This is a continuing research effort that is building the knowledge base for GP assessment. It will include case studies over a wider range or level of detail regarding the impacts and improvement techniques and the other stages of the product life cycle.     

Application of fuzzy VIKOR for evaluation of green supply chain management practices

Environmentally sustainable activities have received an increasing interest among the firms to improve their practices in the supply chain. Although environmental regulations force firms consider these issues, but, green issues are new, evolving every day, and requires a continuous study in the field to gain a complete understanding of the problems. In this study, we illustrate the case of a laptop manufacturer in Malaysia that pursues to evaluate green supply chain management (GSCM) indicators among its practitioners. This paper develops a quantitative evaluation model to measure the uncertainty of GSCM activities and applies an approach based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method which is an extension of intuitionistic fuzzy environment aiming to solve the green multi-criteria decision making (GMCDM) problem. The triangular fuzzy numbers (TFNs) were used to handle imprecise numerical quantities. Then, a hierarchical multiple criteria decision making (MCDM) model was proposed based on fuzzy sets theory and VIKOR method to deal with the problem. The results show the alternative ranks of the four evaluated companies which was based on their performance in GSCM initiatives. The results also indicated that the main criteria of the research ranked as follows respectively: eco-design, green production, green purchasing, green recycling, green transportation and green warehousing. Finally, a comparative analysis of results by fuzzy VIKOR is presented. Additionally the scope for future studies is provided at the end of the paper.     

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.     

The New Quality Assurance Standard of the Joint Commission on Accreditation of Hospitals

The Joint Commission on Accreditation of Hospitals (JCAH) has continuously emphasized improvement in the quality of care provided in hospitals as the central purpose of the accreditation process. In striving to assure such improvement, the JCAH has stressed the need for, and the responsibility of, the medical and other professional staffs to provide continuing review and evaluation of patient care. In recent years, quality assessment activities have evolved, proliferated and matured to the extent that they require a purposeful integration if they are to effect sustained improvement in patient care and clinical performance. To assist hospitals in the coordination or integration of quality assessment activities, the Board of Commissioners of the JCAH has approved an important new quality assurance standard.Significant requirements of the new standard are a comprehensive quality assurance program, a written plan, a problem-focused approach to the review and evaluation of patient care and clinical performance, an annual reassessment of the program, and an improvement in patient care or clinical performance. The new standard reflects the JCAH belief that an integrated, problem-focused approach to quality assurance will significantly improve the quality of care provided throughout a hospital. Such an approach recognizes the interdependence of hospital departments and services in the provision of patient care, and, therefore, requires a purposeful integration or coordination of quality assessment data and activities. Consequently, quality assessment data may be utilized effectively and efficiently, and many potentially useless or duplicative quality assessment activities can be eliminated. The new standard affords hospitals considerable flexibility in the manner in which they implement and administer the program and encourages innovation.     

The effect of dilution rate variation on the performance of continuous fermentation

The effect of dilution rate variation on the performance of bioreactors used for continuous biomass producton is discussed. Calculations based on Powell's model of microbial dynamics show that such a mode of operation does not improve the performance of the process. Remarkable improvement of the process performance reported in literature is caused by incorrectness of the dynamic model of microbial growth which has been used in the calculations.     

Evidence-informed policy formulation and implementation: a comparative case study of two national policies for improving health and social care in Sweden

Background

Evidence has come to play a central role in health policymaking. However, policymakers tend to use other types of information besides research evidence. Most prior studies on evidence-informed policy have focused on the policy formulation phase without a systematic analysis of its implementation. It has been suggested that in order to fully understand the policy process, the analysis should include both policy formulation and implementation. The purpose of the study was to explore and compare two policies aiming to improve health and social care in Sweden and to empirically test a new conceptual model for evidence-informed policy formulation and implementation.

Methods

Two concurrent national policies were studied during the entire policy process using a longitudinal, comparative case study approach. Data was collected through interviews, observations, and documents. A Conceptual Model for Evidence-Informed Policy Formulation and Implementation was developed based on prior frameworks for evidence-informed policymaking and policy dissemination and implementation. The conceptual model was used to organize and analyze the data.

Results

The policies differed regarding the use of evidence in the policy formulation and the extent to which the policy formulation and implementation phases overlapped. Similarities between the cases were an emphasis on capacity assessment, modified activities based on the assessment, and a highly active implementation approach relying on networks of stakeholders. The Conceptual Model for Evidence-Informed Policy Formulation and Implementation was empirically useful to organize the data.

Conclusions

The policy actors’ roles and functions were found to have a great influence on the choices of strategies and collaborators in all policy phases. The Conceptual Model for Evidence-Informed Policy Formulation and Implementation was found to be useful. However, it provided insufficient guidance for analyzing actors involved in the policy process, capacity-building strategies, and overlapping policy phases. A revised version of the model that includes these aspects is suggested.

     

At‐line process analytical technology (PAT) for more efficient scale up of biopharmaceutical microfiltration unit operations

Tangential flow microfiltration (MF) is a cost‐effective and robust bioprocess separation technique, but successful full scale implementation is hindered by the empirical, trial‐and‐error nature of scale‐up. We present an integrated approach leveraging at‐line process analytical technology (PAT) and mass balance based modeling to de‐risk MF scale‐up. Chromatography‐based PAT was employed to improve the consistency of an MF step that had been a bottleneck in the process used to manufacture a therapeutic protein. A 10‐min reverse phase ultra high performance liquid chromatography (RP‐UPLC) assay was developed to provide at‐line monitoring of protein concentration. The method was successfully validated and method performance was comparable to previously validated methods. The PAT tool revealed areas of divergence from a mass balance‐based model, highlighting specific opportunities for process improvement. Adjustment of appropriate process controls led to improved operability and significantly increased yield, providing a successful example of PAT deployment in the downstream purification of a therapeutic protein. The general approach presented here should be broadly applicable to reduce risk during scale‐up of filtration processes and should be suitable for feed‐forward and feed‐back process control. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:108–115, 2016     

Risk analysis and management decisions for weed biological control agents: Ecological theory and modeling results

Biologically based control methods offer many advantages for the control of invasive plant species; however, these methods are not without risks to native species. Thus, there is a need for more effective and efficient methods of risk analysis for biological control agents. We show how the process of ecological risk assessment established by the United States’ Environmental Protection Agency may be adapted to improve assessment of the risks of proposed biological control agents. We discuss the risks posed by weed biological control agents, and present a simple individual-based model of herbivorous insect movement and oviposition on two species of host plant, a target invasive plant species and a non-target native species, in simulated landscapes. The model shows that risks of non-target impacts may be influenced by the details of the movement behavior of biological control agents in heterogeneous landscapes. The specific details of insect movement that appear to be relevant are readily measured in field trials and the general modeling approach is readily adapted to real landscapes. Current biological control risk assessments typically emphasize effects analysis at the expense of exposure analysis; the modeling approach presented here provides a simple and feasible way to incorporate exposure analyses. We conclude that models such as ours should be given serious consideration as part of a comprehensive strategy of risk assessment for proposed weed biological control agents.     

Risk analysis and management decisions for weed biological control agents: Ecological theory and modeling results

Biologically based control methods offer many advantages for the control of invasive plant species; however, these methods are not without risks to native species. Thus, there is a need for more effective and efficient methods of risk analysis for biological control agents. We show how the process of ecological risk assessment established by the United States’ Environmental Protection Agency may be adapted to improve assessment of the risks of proposed biological control agents. We discuss the risks posed by weed biological control agents, and present a simple individual-based model of herbivorous insect movement and oviposition on two species of host plant, a target invasive plant species and a non-target native species, in simulated landscapes. The model shows that risks of non-target impacts may be influenced by the details of the movement behavior of biological control agents in heterogeneous landscapes. The specific details of insect movement that appear to be relevant are readily measured in field trials and the general modeling approach is readily adapted to real landscapes. Current biological control risk assessments typically emphasize effects analysis at the expense of exposure analysis; the modeling approach presented here provides a simple and feasible way to incorporate exposure analyses. We conclude that models such as ours should be given serious consideration as part of a comprehensive strategy of risk assessment for proposed weed biological control agents.     

Dynamic fuzzy model based predictive controller for a biochemical reactor

The kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In this study, a dynamic fuzzy model based predictive controller (DFMBPC) is presented for the control of a biochemical reactor. The DFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. The DFMBPC has the flexibility to opt with various types of fuzzy models whose choice also lead to improve the control performance. The performance of DFMBPC is evaluated by comparing with a fuzzy model based predictive controller (FMBPC) with no model adaptation and a conventional PI controller. The results show that DFMBPC provides better performance for tracking setpoint changes and rejecting unmeasured disturbances in the biochemical reactor.     

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.     

绿色发展背景下区域旅游生态效率影响因素——以西部地区为例

旅游生态效率是评估区域绿色全要素生产率和可持续发展水平的绩效依据。基于西部各省（市、自治区）2000-2017年的面板数据,用"自下而上"法测算西部地区旅游业碳排放量并运用比值法计算旅游生态效率,分析旅游生态效率的时空演变特征及影响因素。首先构建由旅游生态效率、规模效应、结构效应、技术效应共同组成的PVAR模型,探究3种效应对旅游生态效率的影响。然后在考虑各地区能源消费结构差异的基础上构建面板门槛模型,对旅游业发展水平与旅游生态效率的非线性关系进行实证检验。研究结果表明：（1）西部地区旅游生态效率自2000年西部大开发战略实施以来呈逐步提高的趋势,绿色发展水平持续提高。（2）旅游生态效率受自身滞后因素以及技术效应因素的影响较大,游客规模的扩大、产业结构优化以及技术水平的提高均有利于旅游生态效率的提高。（3）旅游业发展水平对旅游生态效率的影响存在门槛效应,经济发展水平、规模效应、结构效应对旅游生态效率有显著的正向作用,城镇化对旅游生态效率有显著的负向作用。最后根据实证分析的结果,提出西部地区实现旅游业绿色、低碳发展的相关对策。     

Assessing an unknown evolutionary process: effect of increasing site-specific knowledge through taxon addition

Assessment of the evolutionary process is crucial for understanding the effect of protein structure and function on sequence evolution and for many other analyses in molecular evolution. Here, we used simulations to study how taxon sampling affects accuracy of parameter estimation and topological inference in the absence of branch length asymmetry. With maximum-likelihood analysis, we find that adding taxa dramatically improves both support for the evolutionary model and accurate assessment of its parameters when compared with increasing the sequence length. Using a method we call "doppelg?nger trees," we distinguish the contributions of two sources of improved topological inference: greater knowledge about internal nodes and greater knowledge of site-specific rate parameters. Surprisingly, highly significant support for the correct general model does not lead directly to improved topological inference. Instead, substantial improvement occurs only with accurate assessment of the evolutionary process at individual sites. Although these results are based on a simplified model of the evolutionary process, they indicate that in general, assuming processes are not independent and identically distributed among sites, more extensive sampling of taxonomic biodiversity will greatly improve analytical results in many current sequence data sets with moderate sequence lengths.     

A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network

Recently, governmental legislations, limitation of natural resources and adverse effects of End-of-Life products on ecological system have spurred researchers to design closed-loop supply chains (CLSCs). Accordingly, designing green supply chains (SCs) that manage greenhouse gas emissions and prevent air pollution can be helpful for companies to heighten profitability and customer loyalty, besides, uncertainty of parameters and disruption strikes could adversely affect performance of SCs and lower quality of output decisions. Effective planning prevents great losses and increases reliability of manager's decisions against uncertainties. Therefore, this paper is proceeding to design a reliable bi-objective green CLSC that minimizes total costs of network aside with minimizing harmful gas emissions. The proposed model is capable of controlling adverse effects of disruptions via applying scenario-based stochastic programming approach. Also, an effective hybrid robust fuzzy stochastic programming method is extended to effectively control uncertainty of parameters and risk-aversion level of output decisions. Extended model analyzing is based on lead-acid battery SC case study that output results approve applicability and effectiveness of model.     

Novel Ergonomic Postural Assessment Method (NERPA) Using Product-Process Computer Aided Engineering for Ergonomic Workplace Design

Background

Musculoskeletal disorders (MSDs) that result from poor ergonomic design are one of the occupational disorders of greatest concern in the industrial sector. A key advantage in the primary design phase is to focus on a method of assessment that detects and evaluates the potential risks experienced by the operative when faced with these types of physical injuries. The method of assessment will improve the process design identifying potential ergonomic improvements from various design alternatives or activities undertaken as part of the cycle of continuous improvement throughout the differing phases of the product life cycle.

Methodology/Principal Findings

This paper presents a novel postural assessment method (NERPA) fit for product-process design, which was developed with the help of a digital human model together with a 3D CAD tool, which is widely used in the aeronautic and automotive industries. The power of 3D visualization and the possibility of studying the actual assembly sequence in a virtual environment can allow the functional performance of the parts to be addressed. Such tools can also provide us with an ergonomic workstation design, together with a competitive advantage in the assembly process.

Conclusions

The method developed was used in the design of six production lines, studying 240 manual assembly operations and improving 21 of them. This study demonstrated the proposed method’s usefulness and found statistically significant differences in the evaluations of the proposed method and the widely used Rapid Upper Limb Assessment (RULA) method.     

Action versus result-oriented schemes in a grassland agroecosystem: a dynamic modelling approach

Effects of agri-environment schemes (AES) on biodiversity remain controversial. While most AES are action-oriented, result-oriented and habitat-oriented schemes have recently been proposed as a solution to improve AES efficiency. The objective of this study was to compare action-oriented, habitat-oriented and result-oriented schemes in terms of ecological and productive performance as well as in terms of management flexibility. We developed a dynamic modelling approach based on the viable control framework to carry out a long term assessment of the three schemes in a grassland agroecosystem. The model explicitly links grazed grassland dynamics to bird population dynamics. It is applied to lapwing conservation in wet grasslands in France. We ran the model to assess the three AES scenarios. The model revealed the grazing strategies respecting ecological and productive constraints specific to each scheme. Grazing strategies were assessed by both their ecological and productive performance. The viable control approach made it possible to obtain the whole set of viable grazing strategies and therefore to quantify the management flexibility of the grassland agroecosystem. Our results showed that habitat and result-oriented scenarios led to much higher ecological performance than the action-oriented one. Differences in both ecological and productive performance between the habitat and result-oriented scenarios were limited. Flexibility of the grassland agroecosystem in the result-oriented scenario was much higher than in that of habitat-oriented scenario. Our model confirms the higher flexibility as well as the better ecological and productive performance of result-oriented schemes. A larger use of result-oriented schemes in conservation may also allow farmers to adapt their management to local conditions and to climatic variations.     

Using Life-Cycle Assessment in Process Design

This article presents the application of life-cycle assessment in early phases of process design in the context of technology that employs a bio-based material. The goal is to identify hot spots in the process chains with regard to environmental impacts by performing a dominance analysis. By focusing his activities on the hot spots identified, the designer is given the opportunity to efficiently improve environmental performance. This approach is illustrated for the case of supercritical water gasification, a novel technology for the treatment of organic feedstock with high moisture content. In the reactor under supercritical conditions, organic components are converted into a high-caloric synthesis gas, with hydrogen, methane, and carbon dioxide as the main products. The data used for the assessment are obtained from laboratory tests and the literature, completed by assumptions for missing data. The scope of assessment ranges from the extraction of raw materials to the product, that is, hydrogen (cradle to gate) with sewage sludge of a municipal wastewater treatment plant used as feedstock. The assessment identifies the main sources of environmental impacts. The predominant process step in terms of global warming potential is the supply of the gasification process with additional heat. The production of a blending agent in the dewatering step is the main source of the impact category of acidification, whereas the wastewater treatment plant is the origin of emissions that lead to eutrophication. The revealed sources are analyzed further and options for reducing the environmental impacts are discussed.     

Modeling Healthcare Processes Using Commitments: An Empirical Evaluation

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7—each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student’s t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.