In the digital economy, the relationship between digital transformation and a company's total factor productivity has profound implications for high-quality business development. Heavy polluters are ...given more environmental responsibility because of their high pollution and emission characteristics. This paper analyses the theoretical framework for the impact of digital transformation on the total factor productivity of heavily polluting firms. Using a sample of Shanghai and Shenzhen A-share heavy polluters from 2010 to 2020, we explore how the digital transformation of heavy polluters affects the total factor productivity of firms. The study found that the digital transformation of heavily polluting companies can effectively improve total factor productivity, internally by increasing their level of green technology innovation and externally by increasing their willingness and capacity for corporate social responsibility. At the same time, digital transformation can improve total factor productivity by reducing cost stickiness, revealing the "black box" in which digital transformation affects the total factor productivity of an enterprise. It was further found that the digital transformation of companies with high levels of environmental investment, large enterprises, those in non-manufacturing industries, and heavy polluters of a state-owned nature had a more significant impact on total factor productivity. The findings of the study provide empirical evidence for the digital transformation of heavily polluting companies to improve productivity and the green transformation of the economy for companies under the low carbon goal.
•An optimization-based framework for molecular and mixture product design is developed.•An MINLP model for CAMD is established to design different kinds of chemical products simultaneously.•A ...versatile tool for chemical product design and evaluation “OptCAMD” is developed and integrated in ProCAPD.•Case studies highlighting different aspects of OptCAMD involving the design of various types of chemical products are presented.
Chemical product design determines the structure and constitution of products that satisfies all desired properties and functions. Molecular products are usually employed as the main active ingredient, or manipulated to obtain a specific function for chemical-based products, while mixtures are one of the most widely used chemical products. Therefore, the design of molecular and mixture products is the foundation of all chemical product design problems. In this paper, the development of an optimization-based framework for molecular and mixture product design is presented. The design work-flow consists of three steps involving preliminary design, CAMD (Computer Aided Molecular-Mixture Design), as well as product evaluation and verification. In the preliminary design step, the product attributes are collected and converted into a set of desired physico-chemical properties with associated targets to formulate the CAMD problem. In the CAMD step, an optimization-based mathematical programming model is established and solved to generate feasible molecules and/or mixtures together with optimal product candidates. In the product evaluation and verification step, final selection of the optimal chemical product is made based on evaluation of in-use product performance attributes and additional properties not included in the CAMD step. The three steps have been implemented within a molecular-mixture design toolbox called “OptCAMD”, which is integrated in ProCAPD, a versatile tool for chemical product design and evaluation. Case studies highlighting different aspects of OptCAMD involving the design of various types of chemical products are presented.
Glutamate plays a crucial role in the treatment of depression by interacting with the metabotropic glutamate receptor subtype 5 (mGluR5), whose negative allosteric modulators (NAMs) are thus ...promising antidepressants. At present, to explore the structural features of 106 newly synthesized aryl benzamide series molecules as mGluR5 NAMs, a set of ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were firstly carried out applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. In addition, receptor-based analysis, namely molecular docking and molecular dynamics (MD) simulations, were performed to further elucidate the binding modes of mGluR5 NAMs. As a result, the optimal CoMSIA model obtained shows that cross-validated correlation coefficient
= 0.70, non-cross-validated correlation coefficient
= 0.89, predicted correlation coefficient
= 0.87. Moreover, we found that aryl benzamide series molecules bind as mGluR5 NAMs at Site 1, which consists of amino acids Pro655, Tyr659, Ile625, Ile651, Ile944, Ser658, Ser654, Ser969, Ser965, Ala970, Ala973, Trp945, Phe948, Pro903, Asn907, Val966, Leu904, and Met962. This site is the same as that of other types of NAMs; mGluR5 NAMs are stabilized in the "linear" and "arc" configurations mainly through the H-bonds interactions, π-π stacking interaction with Trp945, and hydrophobic contacts. We hope that the models and information obtained will help understand the interaction mechanism of NAMs and design and optimize NAMs as new types of antidepressants.
The aim of this study is to evaluate different renewable energy investments alternatives. Within this framework, six different criteria are chosen to represent financial and non-financial dimensions. ...Additionally, five renewable energy investment alternatives (biomass, hydropower, geothermal, wind and solar) are selected. Fuzzy AHP and fuzzy DEMATEL methods are considered to weight these criteria whereas alternatives are ranked by using fuzzy TOPSIS and fuzzy VIKOR approaches. The findings show that fuzzy AHP and fuzzy DEMATEL methods also give coherent results. It is concluded that environmental effects and earnings are the most significant criteria. Moreover, wind and solar are the most attractive renewable energy investment alternatives. Therefore, it is recommended that governmental incentives should be widely used for effective location selection of both energy alternatives. This situation could be also attractive for foreign investors in renewable energy market. In addition, large-scale investments should be handled by merger and acquisition to increase overall performance. Hence, it can be possible to raise earnings, improve capital adequacy and enhance organizational capacity with the extensive investments. Furthermore, easy access to the sources and good contract conditions should also be provided for this purpose so that it can be much easier to attract the attention of these investors. Also, customer expectations should be understood effectively with a detailed analysis. With the help of this issue, appropriate products can be presented according to customer needs and it significantly contributes to the success in renewable energy investment.
As an important theoretical computation method in computer-aided drug design, molecular docking has significantly shifted the paradigm of drug discovery. As one of the open-source docking software, ...Autodock Vina (Vina) is widely popular, but the lack of relevant experience and inappropriate docking parameters make it unable to perform optimally in practical application scenarios, which leads to potential failure risks in the early stage of drug discovery. In order to simplify the docking steps and determine the most appropriate docking parameters, a universal solution for rigid receptor docking using Vina has been proposed in this paper, and a user-friendly software for the entire process of molecular docking using Vina is developed. The case studies show that our docking solution is able to be applied to different docking scenarios to facilitate a more accurate, faster, and more convenient new drug discovery process.
•An accurate and universal batch docking solution using Autodock Vina is proposed.•The optimal parameters for batch docking with Vina are determined.•A user-friendly software interface is developed for docking with Vina.
Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemical-based products. The life cycles of ...chemical products involve the procedures of conceptual product designs, experimental investigations, sustainable manufactures through appropriate chemical processes and waste disposals. During these periods, one of the most important keys is the molecular property prediction models associating molecular structures with product properties. In this paper, a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions, such as activity coefficient, and so forth. The workflow of framework consists of three steps. In the first step, a database is created for collections of basic molecular information; in the second step, quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties (pseudo experimental data), which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step. The whole framework has been carried out within a molecular property prediction toolbox. Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.
Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands ...of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.
•New model for warehouse performance evaluation has been proposed.•An integrated PCA-DEA-IFR PIPRECIA-IFR MARCOS Model was developed.•New approach IFR PIPRECIA was developed and presented in literature for first time.•The model enables more accurate and precise decision-making in logistics.
This study aims to evaluate the innovation performance of the Turkish banking industry. For this purpose, eight different financial and nonfinancial criteria are identified as a result of literature ...review. Moreover, five biggest Turkish deposit banks are selected as alternatives. Interval type 2 fuzzy decision-making trial and evaluation laboratory (IT2 FDEMATEL) is taken into account to weight the dimensions. On the other side, to rank the alternatives, interval type 2 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (IT2 FVIKOR) approach is considered. The findings show that market share and return on investment are the most important factors in the innovation performance of retail banking services. This situation gives information that the when banks are more successful in the market, they can make investment to the innovation more effectively. The main reason is that these banks have enough capacity to make this kind of investments. Thus, it is recommended that Turkish banks should first make an efficiency analysis to increase their profit margin so that their capacities can be improved. For this purpose, many different factors should be taken into account by Turkish banks, such as personnel competency and technological development. Within this scope, these banks should make financial analysis of the innovation effectively by generating a qualified team. However, necessary research and development activities should be conducted to reach this objective. Hence, it can be possible to minimize ineffective innovation for the banks.
The industrial application of lithium metal anode requires less side reaction between active lithium and electrolyte, which demands the sustainability of the electrolyte‐induced solid‐electrolyte ...interface. Here, through a new diluted lithium difluoro(oxalato)borate‐based (LiDFOB) high concentration electrolyte system, it is found that the oxidation behavior of aggregated LiDFOB salt has a great impact on solid‐electrolyte interphase (SEI) formation and Li reversibility. Under the operation window of Cu/LiNi0.8Co0.1Mn0.1O2 full cells (rather than Li/Cu configuration), a polyether/coordinated borate containing solid‐electrolyte interphase with inner Li2O crystalline can be observed with the increasing concentration of salt, which can be ascribed to the reaction between aggregated electron‐deficient borate species and electron‐rich alkoxide SEI components. The high Li reversibility (99.34%) and near‐theoretical lithium deposition enable the stable cycling of LiNi0.8Co0.1Mn0.1O2/Li cells (N/P < 2, 350 Wh kg−1) under high cutoff voltage condition of 4.6 V and lean electrolyte condition (E/C ≈ 3.2 g Ah−1).
High‐efficacy and polymeric solid‐electrolyte interphase is in situ formed on lithium metal anode by using a new diluted lithium difluoro(oxalato)borate‐based (LiDFOB) high‐concentration electrolyte. The outer SEI layer is an amorphous polyether/tri‐coordinated borate polymeric organic phase, while the inner layer contains robust Li2O inorganic crystalline. As‐fabricated cells deliver a high Li reversibility of 99.34% and long full‐cell lifetime under ≈350 Wh kg−1.
Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency. Nevertheless, the current in silico techniques are insufficient in providing accurate and robust ...predictions for binding kinetic properties. To this end, this work develops a variety of binding kinetic models for predicting a critical binding kinetic property, dissociation rate constant, using eight machine learning (ML) methods (Bayesian Neural Network (BNN), partial least squares regression, Bayesian ridge, Gaussian process regression, principal component regression, random forest, support vector machine, extreme gradient boosting) and the descriptors of the van der Waals/electrostatic interaction energies. These eight models are applied to two case studies involving the HSP90 and RIP1 kinase inhibitors. Both regression results of two case studies indicate that the BNN model has the state‐of‐the‐art prediction accuracy (HSP90: Rtest2=0.947 ${R}_{\text{test}}^{2}=0.947$, MAEtest = 0.184, rtest = 0.976, RMSEtest = 0.220; RIP1 kinase: Rtest2=0.745 ${R}_{\text{test}}^{2}=0.745$, MAEtest = 0.188, rtest = 0.961, RMSEtest = 0.290) in comparison with other seven ML models.
This paper develops a variety of binding kinetic models for predicting dissociation rate constants using eight machine learning methods, which are tested by two case studies involving the HSP90 and RIP1 kinase inhibitors. Both regression results of the two case studies indicate that the Bayesian neural network model has the state‐of‐the‐art prediction accuracy in comparison with the other seven machine learning algorithms.
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