Multi-attribute Methods Research Articles

Blockchain-Based Joint Auction Model for Distributed Energy in Industrial Park Microgrids

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.

Improved Mass Accuracy and Precision for Multi-Attribute Methods Using a New Internally Calibrated High Resolution Orbitrap Mass Detector.

In the development of biotherapeutics, a thorough understanding of a molecule's product quality attributes (PQAs) and their effect on structure-function relationships and long-term stability is essential for ensuring the safety and efficacy of the product. First published in 2015, the multi-attribute method (MAM), based on LC-MS peptide mapping and automation principles, can be used to support biotherapeutic process and product development. The MAM provides simultaneous site-specific detection, identification, quantitation, and quality control monitoring of selected PQAs. In this article, a low-maintenance MAM-ready mass detector with a small footprint was evaluated for its ability to monitor PQAs on proteolytically digested proteins with high mass accuracy and precision. Optimized source parameters enable robust relative quantitation of attributes with high sensitivity and minimal in-source fragmentation. A combination of a built-in one-point mass calibration procedure prior to data acquisition and Scan-to-Scan on-the-fly mass correction allows monitoring of most peptides for at least 54 days with sub-1 ppm mass accuracies at high-resolution (180,000 at m/z 200). This enables the use of <3 ppm mass tolerances for peptide monitoring, supporting high method specificity and robustness. LC-MS based MAM data from this instrument compares well to data collected by earlier MAM systems and conventional HPLC profile-based drug substance release assays.

PREFERENSI KONSUMEN TERHADAP PEMBELIAN CABAI BESAR DI KABUPATEN SUKOHARJO

Chili consumption is projected to continue to increase every year along with the increasing population in Indonesia, this is an opportunity for farming. Farmers responded to this by increasing chili production. The purpose of the study was to determine the preferences and attributes that consumers consider the most towards the purchase of large chilies. The basic research method is descriptive analytic. The research locations were chosen purposively in three sub-districts in Sukoharjo Regency, namely Sukoharjo, Kartasura, and Grogol sub-districts. Sampling was done by accidental sampling totaling 100 respondents. Data analysis used Chi-Square and Fishbein Multi-attribute methods. The results showed that the large chilies that became the preference were large chilies, bright red in color because consumers considered red to be perfect ripeness, had a glossy skin surface that represented the level of freshness, had thick skin, had a very spicy level of spiciness, and had a low price. The attributes that were considered the most in succession were skin surface, skin thickness, price of chili, type of chili, skin color, and level of spiciness. Recommendations that can be given are the use of varieties that suit consumer tastes such as IPB CH3 and IPB CH6 and maintain the quality of red chilies to maintain consumer trust.

Compliance and regulatory considerations for the implementation of the multi-attribute-method by mass spectrometry in a quality control laboratory

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under the good manufacturing practice (GMP) regime, due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. This article is the second part of a two-tiered publication aiming at providing guidance for implementation of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) in a QC laboratory. The first part [1] focuses on technical considerations, while this second part provides considerations related to GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

Method for detecting rare differences between two LC-MS runs

LC-MS based multi-attribute methods (MAM) have drawn substantial attention due to their capability of simultaneously monitoring a large number of quality attributes of a biopharmaceutical product. For successful implementation of MAM, it is usually considered a requirement that the method is capable of detecting any new or missing peaks in the sample when compared to a control. Comparing a sample to a control for rare differences is also commonly practiced in many fields for investigational purpose. Because MS signal variability differs greatly between signals of different intensities, this type of comparison is often challenging, especially when the comparison is made without enough replicates. In this report we describe a statistical method for detecting rare differences between two very similar samples without replicate analyses. The method assumes that an overwhelming majority of components have equivalent abundance between the two samples, and signals with similar intensities have similar relative variability. By analyzing several monoclonal antibody peptide mapping datasets, we demonstrated that the method is suitable for new-peak detection for MAM as well as for other applications when rare differences between two samples need to be detected. The method greatly reduced false positive rate without a significant increase of false negative rate.

Technical considerations for the implementation of the multi-attribute-method by mass spectrometry in a quality control laboratory

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under good manufacturing practice (GMP) due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. Here, current literature related to the development and application of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) is compiled with the aim of providing guidance for the implementation of MAM in a QC laboratory. This article, focusing on technical considerations, is the first part of a two-tiered publication, whereby the second part will focus on GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

Intact Mass Analysis–Based Multi-Attribute Methods (iMAMs) for Characterization of Biopharmaceuticals

Multi-attribute methods (MAMs) are becoming increasingly popular for their ability to analyze multiple critical quality attributes (CQA) in a single workflow. This capability becomes particularly attractive for a product class such as monoclonal antibodies, which are large and complex, and have many CQAs that need to be monitored and controlled during their manufacturing so as to deliver consistent product quality. In an earlier installment, we discussed the role of liquid chromatography and mass spectrometry in MAMs. In this article, we focus on intact mass analysis–based multi-attribute methods (iMAMs), a suitable alternative that can complement standard MAMs or be used when there is a need for rapid turnaround and monitoring of only a limited number of CQAs. Multiple case studies are presented to elucidate this concept.

TrustSys: Trusted Decision Making Scheme for Collaborative Artificial Intelligence of Things

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

Analysis of International Competitiveness of China’s Mobile Phone Industry Based on Data Mining Algorithm

In order to analyze the international competitiveness of China-made mobile phones, this paper combines data mining algorithms to analyze the international competitiveness of China’s mobile phone industry, improve the international market share of China-made mobile phones, and study the concept of multiattribute problems and related research methods. Moreover, the paper carries out the model description of the selected research methods combining principal component analysis, data envelopment analysis, and sorting method approaching the ideal solution. In addition, this paper expounds on the principles and models of PCA, DEA, and TOPSIS and selects an intelligent algorithm suitable for this model. Finally, this paper verifies the validity of the model proposed in this paper, conducts statistical analysis through the data mining model, evaluates the data mining effect through multiple simulation exercises, and verifies the validity of the system model.

Multi-attribute method based characterization of antibody drug conjugates (ADC) at the intact and subunit levels

Antibody–drug conjugates (ADCs) represent an important class of new biopharmaceutical modalities. ADCs are highly complex and heterogeneous molecules, potentially containing numerous product-related structures, that can contribute to the quality, efficacy and safety of the product. To keep up with product life cycle related changes, wide-range and targeted characterization of product quality attributes (PQA) are of high demand. Multi-attribute methods (MAM) can screen numerous PQAs in a parallel fashion including product properties as well as product and process-related impurities. MAM is usually based on a bottom-up approach relying on the enzymatic digestion of the protein into peptides prior to mass spectrometry (MS). However, this processing workflow can result in considerable information loss, such as the drug distribution profile of an antibody-drug conjugate. Therefore, complementary MAM approaches, based on subunit and intact mass analyses, are necessary approaches offering the advantage of product identity confirmation, quantification of the different conjugated species and monitoring the drug-to-antibody ratio at the same time. In this work we introduce a high throughput MS based attribute tracking method for ADC characterization at the intact and subunit levels by simultaneously monitoring multiple PQAs. The workflow includes sample preparation and MS instrument suitability testing for heterogeneous lysine-linked ADCs, software solutions for routine PQAs tracking, method repeatability and an easy data review fitting perfectly into high throughput analyses. As methionine oxidation is one of the modifications that should be closely monitored at any step of process development, an important application to oxidative stress evaluation using forced degradation demonstrated the applicability of the workflow.

Corporate Social Responsibility and Profitability: The Moderating Role of Firm Type in Chinese Appliance Listed Companies

Corporate social responsibility (CSR) is among the dominant multi-attribute methods of comprehensively representing the competitiveness of a company. A large number of studies have commonly found that profitability can positively affect CSR. However, positivity depends on firm type and the economy, and there is little research in this area. The objective of this paper is to study and verify whether the profitability of different types of companies has a comparable impact on CSR measures in Chinese appliance listed companies. A specific multi-attribute AHP (analytic hierarchy process) model was proposed to determine the CSR for the conditions of Chines appliance listed companies. The interactive regression model serves to analyse the impact of a firm type. The specific multi-attribute AHP model was verified as a suitable tool for CSR evaluation of Chines appliance listed companies. The regression results show that for family firms, the impact of profitability on CSR is significant, while for non-family firms, the impact was not confirmed. Thus, evidence that family firms fulfil better CSR than non-family firms in the investigated Chinese sector is offered. The findings provide proof that it is essential to distinguish firm types, and the generalised findings are simplified and not valid.

Multi-dimensional LC-MS: the next generation characterization of antibody-based therapeutics by unified online bottom-up, middle-up and intact approaches.

Accelerated development of new therapeutics in an increasingly competitive landscape requires the use of high throughput analytical platforms. In addition, the complexity of novel biotherapeutic formats (e.g. fusion proteins, protein-polymer conjugates, co-formulations, etc.) reinforces the need to improve the selectivity and resolution of conventional one-dimensional (1D) liquid chromatography (LC). Liquid chromatography-mass spectrometry (LC-MS)-based technologies such as native LC-MS for intact mass analysis or peptide mapping (also called bottom-up approach)-based multi-attribute methods (MAM) have already demonstrated their potential to complement the conventional analytical toolbox for monoclonal antibody (mAb) characterization. Two-dimensional liquid-chromatography (2D-LC-MS) methods have emerged in the last ten years as promising approaches to address the increasing analytical challenges faced with novel antibody formats. However, off-line sample preparation procedures are still required for conventional 1D and 2D-LC-MS methods for the in-depth variant characterization at the peptide level. Multi-dimensional LC-MS (mD-LC-MS) combine sample preparation and multi-level (i.e. intact, reduced, middle-up and peptide) analysis within the same chromatographic set-up. This review presents an overview of the benefits and limitations of mD-LC-MS approaches in comparison to conventional chromatographic methods (i.e. 1D-LC-UV methods at intact protein level and 1D-LC-MS methods at peptide level). The current analytical trends in antibody characterization by mD-LC-MS approaches, beyond the 2D-LC-MS workhorse, are also reviewed, and our vision on a more integrated multi-level mD-LC-MS characterization platform is shared.

Covid-19 Economic Vulnerability Index: EU Evidence

The COVID-19 pandemic outbreak caused many negative effects on both the global and national economies. To implement effective policies to mitigate the negative impact of a pandemic, it is necessary to identify particularly vulnerable areas. The objective of this paper is to rank the EU countries in terms of the level of vulnerability of their economies to the impact of the pandemic. For this purpose, the COVID-19 Economic Vulnerability Index (CEVI) was constructed. It replaces the 15-dimensional set of characteristics of the countries with one aggregate, synthetic indicator estimated for 27 EU member states. In the study multivariate statistical methods, including agglomerative clustering and multi-attribute methods of object assessment were used to analyse the effects of the pandemic. The research shows that EU countries have different levels of economic vulnerability to the impact of the COVID-19 pandemic. The southern European countries (Spain, Croatia, Greece and Italy), where the tourism sector plays an important role in GDP composition, are the most fragile. Germany and the Scandinavian countries proved to be the least sensitive to the negative impact of the pandemic. The CEVI can be an important part of the decision support system. It enables the identification of countries that show greater vulnerability to the economic impact of the COVID-19 pandemic and may help support countries that need help the most. The proposed index also indicates certain areas in the country’s economy that make it more vulnerable. The CEVI in combination with other instruments can be a very useful tool to improve the economy’s resilience and help it recover faster in the event of a pandemic shock.

A Multiattribute Performance Measurement for Marine Energy Saving and Environmental Protection Enterprises

Fang, F., 2020. A multiattribute performance measurement for marine energy saving and environmental protection enterprises. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 396–401. Coconut Creek (Florida), ISSN 0749-0208.With the rapid development of modern technology and industries, an industry revolution is approaching. Many countries have adjusted their own strategic development steps by raising emerging industries that are based on significant technical breakthroughs and development demand. The Chinese government has adopted the “blue economy” as the direction for high-quality development. Energy Saving and Environmental Protection (ESEP) industries have been chosen for the nature of their intensive knowledge and technology, their low consumption of material resources, their large growth potential, and the good overall benefit. China's listed ESEP enterprises, as an example, set performance evaluation indicators based on the path of their corporate value formation and use multiattribute evaluation methods to obtain stable evaluation results. They also comprehensively evaluate marine ESEP safeguard measures to protect the marine environment, to contribute toward transforming traditional development methods, and to accelerate the high-quality development of the marine economy.

Determination and Verification of the Key Assessment Indicators for the Insurance Market by Applying the Decomposition Multi-attribute Methods and Regression Analysis

The aim of the article is to determine and verify the key assessment indicators for the insurance market by applying the decomposition multi-attribute methods and regression analysis. The assessed specific indicators are qualitative indicators (insurance penetration, claim ratio) and quantitative indicators (gross premium, insurance benefit, number of insurance contracts, number of settled insurance claims, number of employees, number of commercial insurance companies, concentration of the insurance market, and more). The decomposition multi-criteria AHP method (analytic hierarchy process) and ANP method (analytic network process) based on the Saaty pair comparison approach are described, including the computation procedure. The described methods are then applied to determine the preferences of the indicators for the insurance market. Subsequently, a particular regression model is created. Our findings reveal the resulting preferences of individual indicators of the insurance market evaluation and key assessment indicators.

Modelling decision knowledge for the evaluation of water management investment projects

Water resources are facing increased stress owing to population growth and environmental pollution. Water utility companies are concerned about this, and they are increasingly utilizing decision support systems to achieve higher transparency in utility investment project selection and to improve the economic management of water supply systems and resource exploitation. However, utility investment project decisions involve a diverse set of quantitative and qualitative criteria. This makes the selection of an appropriate decision method difficult. We have developed a novel hybrid multi-criteria decision method that integrates quantitative and qualitative criteria value domains in the same decision-making model and applied it to a real-world case involving a large multi-utility operator. The method is based on the decision expert and multi-attribute utility theory methods, which were modified to facilitate conversion between criteria domains. Our innovative approach allows for the use of the expressive power and comprehensibility of the qualitative method while maintaining the precision of the quantitative method, enabling decision makers to differentiate more easily between worthwhile and less feasible utility investment projects in the field of safe drinking water supply. The proposed method is applicable to a wide range of decision problems involving a diverse set of decision criteria.

Subunit mass analysis for monitoring multiple attributes of monoclonal antibodies

Multi-Attribute Methods (MAMs) are appealing due to their ability to provide data on multiple molecular attributes from a single assay. If fully realized, such tests could reduce the number of assays required to support a product control strategy while providing equivalent or greater product understanding relative to the conventional approach. In doing so, MAMs have the potential to decrease development and manufacturing costs by reducing the number of tests in a release panel. In this work, we report a MAM which is based on subunit mass analysis. The MAM assay is shown to be suitable for use as a combined method for identity testing, glycan profiling, and protein ratio determination for co-formulated monoclonal antibody (mAb) drugs. This is achieved by taking advantage of the high mass accuracy and relative quantification capabilities of intact mass analysis using quadrupole time-of-flight mass spectrometry (Q-TOF MS). Protein identification is achieved by comparing the measured masses of light chain (LC) and heavy chain (HC) mAbs against their theoretical values. Specificity is based on instrument mass accuracy. Glycan profiling and relative protein ratios are determined by the relative peak intensities of the protein HC glycoforms and LC glycoforms, respectively. Results for these relative quantifications agree well with those obtained by the conventional hydrophilic interaction liquid chromatography (HILIC) and reversed-phase LC methods. The suitability of this MAM for use in a quality control setting is demonstrated through assessment specificity for mAb identity, and accuracy, precision, linearity and robustness for glycan profiling and ratio determination. Results from this study indicate that a MAM with subunit mass analysis has the potential to replace three conventional methods widely used for mAb release testing including identification assay, glycosylation profiling, and ratio determination for co-formulated mAbs.

Heronian Mean Operator of Linguistic Neutrosophic Cubic Numbers and Their Multiple Attribute Decision-Making Methods

Many aggregation operators in multiattribute decisions assume that attributes are independent of each other; this leads to an unreasonable situation in information aggregation and decision-making. Heronian mean is the aggregation operator that can embody the interaction between attributes. In this paper, we merge the linguistic neutrosophic cubic number (LNCN) and the Heronian mean operator together to develop a LNCN generalized weighted Heronian mean (LNCNGWHM) operator and a LNCN three-parameter weighted Heronian mean (LNCNTPWHM) operator and then discuss their properties. Further, two multiattribute decision methods based on the proposed LNCNGWHM or LNCNTPWHM operator are introduced under LNCN environment. Finally, an example is used to indicate the effectiveness of the developed methods.

Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade

Considering the fuzzy multi-attribute problems of components' failure information, a fault diagnosis model of a Bayesian network based on the interval triangular fuzzy multi-attribute decision method was built. First, the fault multi-attribute hierarchies were represented as a Bayesian network structure by Bayesian network causal dependencies, which reduced the complexity of the multi-attribute hierarchy. Second, in the condition that weight information is completely unknown, grey correlation optimal models were constructed based on the grey correlation analysis method. Furthermore, faulty components were sorted and the component with the highest value was optimised, and the calculation method of interval triangle fuzzy multi-attribute decision based on the grey correlation was given, which realised the fault diagnosis of the system fuzzy multiple-attribute problem. Finally, the method was applied in an analysis of a case of fault diagnosis on a CNC machine tool servo system with a low voltage alarm, which verified the effectiveness of the method.

Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade

Research on interval triangular fuzzy multi-attribute fault diagnosis methods based on the grey relation grade