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Sustainability in transportation is vital for lessening environmental effects and ensuring the long-term viability of the transportation industry. The aviation sector, a significant player in sustainable transport, has encountered obstacles in reducing its ecological footprint, especially in aircraft design and operation. Symmetry and asymmetry exist widely in the aviation sector, as in other industrial applications. Multicriteria decision making (MCDM) is an important research topic that is widely applied to practical decision-making problems and is considered a complex tool with which to balance the symmetry between goals and conflicting criteria. This research assesses different airplane models in order to pinpoint the most environmentally friendly options using the Bayesian best–worst (BWM) method for evaluating criteria and the consensus-based intuitionistic fuzzy evaluation based on distance from average solution (IF EDAS) method for assessing alternatives. The Bayesian BWM approach facilitates group decision making on criteria evaluation and systematically evaluates factors like fuel efficiency, emissions, and noise levels, offering a holistic view of each airplane model's sustainability performance. Conversely, consensus-based IF EDAS allows for the ranking of alternatives based on their distance to the average solution for a sustainable airplane model while concurrently considering multiple criteria, guiding decisionmakers to a consensus and eliminating the asymmetry between opinions before the final decision. This study reveals that technical features are the critical criteria for airplanes and significantly influence alternative rankings. The findings indicate that the chosen decision criteria greatly impact the selection of the most sustainable airplane model as part of a sustainable transportation system, potentially altering ranking orders. [ABSTRACT FROM AUTHOR]

Purpose: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue. Methods: A single triplanar CNN of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head. The 3D volumes were processed slice-wise across the three orientations and the predictions were merged using different voting strategies. This approach was developed using MRI datasets from 20 patients with 'classical' spinal amyotrophic lateral sclerosis (ALS) and 20 healthy controls and, in a pilot study, applied to the tongue volume quantification to 19 controls and 19 ALS patients with the variant progressive bulbar palsy (PBP). Results: Consensus models with softmax averaging and majority voting achieved highest segmentation accuracy and outperformed predictions on single orientations and consensus models with union and unanimous voting. At the group level, reduction in tongue volume was not observed in classical spinal ALS, but was significant in the PBP group, as compared to controls. Conclusion: Utilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume. [ABSTRACT FROM AUTHOR]

In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model. [ABSTRACT FROM AUTHOR]

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs' weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs' jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems. [ABSTRACT FROM AUTHOR]

Collaboration in design decision-making is a critical factor in enhancing the quality of built environments. However, several factors, such as the lack of clarity in the negotiation process, the diverse disciplinary backgrounds of stakeholders, and conformity bias, pose significant challenges, rendering the collaboration in built environment design time-consuming. To overcome these challenges and improve the efficiency of collaborative design, a novel solution that introduces a consensus model enhanced by virtual reality (VR) is proposed in this paper. The proposed model implements a structured decision-making process to foster fact-based discussions and minimize conflicts arising from personal biases. It ensures equal influence among all stakeholders, prevents the dominance of any single viewpoint, and offers personalized recommendations to guide stakeholders toward group consensus while respecting their initial preferences. The study leverages VR techniques to enhance communication and comprehension during design negotiations. The VR collaboration platform provides a robust visualization and user-friendly interface, empowering stakeholders to understand the decision-making process better and iteratively refine their preferences for optimal user satisfaction. By integrating VR, the burden traditionally placed on a moderator in group decision-making (GDM) is reduced, resulting in a more streamlined and efficient collaborative process. Additionally, stakeholders can conveniently communicate their preferences remotely through cloud services within the immersive VR collaboration environment, further enhancing the overall design collaboration experience. To the best knowledge of the authors, this study is the first attempt to combine a consensus model with VR to create a comprehensive solution that supports stakeholders in achieving consensus design solutions in the early design stage. This study contributes to the advancement of knowledge in the field of design collaboration by exploring the potential benefits of a VR-enhanced consensus model for GDM. [ABSTRACT FROM AUTHOR]

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition. [ABSTRACT FROM AUTHOR]

Sustainability in transportation is vital for lessening environmental effects and ensuring the long-term viability of the transportation industry. The aviation sector, a significant player in sustainable transport, has encountered obstacles in reducing its ecological footprint, especially in aircraft design and operation. Symmetry and asymmetry exist widely in the aviation sector, as in other industrial applications. Multicriteria decision making (MCDM) is an important research topic that is widely applied to practical decision-making problems and is considered a complex tool with which to balance the symmetry between goals and conflicting criteria. This research assesses different airplane models in order to pinpoint the most environmentally friendly options using the Bayesian best–worst (BWM) method for evaluating criteria and the consensus-based intuitionistic fuzzy evaluation based on distance from average solution (IF EDAS) method for assessing alternatives. The Bayesian BWM approach facilitates group decision making on criteria evaluation and systematically evaluates factors like fuel efficiency, emissions, and noise levels, offering a holistic view of each airplane model’s sustainability performance. Conversely, consensus-based IF EDAS allows for the ranking of alternatives based on their distance to the average solution for a sustainable airplane model while concurrently considering multiple criteria, guiding decisionmakers to a consensus and eliminating the asymmetry between opinions before the final decision. This study reveals that technical features are the critical criteria for airplanes and significantly influence alternative rankings. The findings indicate that the chosen decision criteria greatly impact the selection of the most sustainable airplane model as part of a sustainable transportation system, potentially altering ranking orders.

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Abstract Background The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features. Results The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design. Conclusion Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.

Abstract Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints. Graphical Abstract

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs’ weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs’ jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems.

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

Yalkowsky’s General Solubility Equation (GSE), with its three fixed constants, is popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible molecules. This review examines the findings of a series of studies, where we have sought to come up with a better prediction model, by comparing the performances of the GSE to Abraham’s Solvation Equation (ABSOLV), and Random Forest regression (RFR) machine-learning (ML) method. Large, well-curated aqueous intrinsic solubility databases are available. However, drugs may be sparsely distributed in chemical space, concentrated in clusters. Even a large database might overlook some regions. Test compounds from under-represented portions of space may be poorly predicted, as might be the case with the ‘loose’ set of 32 drugs in the Second Solubility Challenge (2020). There appears to be still a need for better coverage of drug space. Increasingly, current trends in predictions of solubility use calculated input descriptors, which may be an advantage for exploring properties of molecules yet to be synthesized. The risk may be that overall prediction approaches might be based on accumulated uncertainty. The increasing use of ML/AI methods can lead to accurate predictions, but such predictions may not readily suggest the strategies to pursue in selecting yet-to-be-synthesized compounds. Based on our latest findings, we recommend predictions based on both ‘grouped’ ABSOLV(GRP) and ‘Flexible Acceptor’ GSE(Φ,B) models with the provided best-fit parameters, where Φ is the Kier molecular flexibility index and B is the Abraham H-bond acceptor strength. For molecules with Φ < 11, the prudent choice is to pick the Consensus Model, the average of ABSOLV(GRP) and GSE(Φ,B). For more flexible molecules, GSE(Φ,B) is recommended.

With the development of the cloud computing era, the decision-making environment and algorithm models have become increasingly complex, and traditional decision-making methods have been unable to meet the needs of large group decision-making (LGDM) problems. Firstly, in order to solve this problem, the concept of double hierarchy interval hesitant fuzzy language (DHIHFL) is proposed. Compared with the traditional double hierarchy hesitant fuzzy language (DHHFL), it contains all elements from the lower limit to the upper limit and more comprehensively characterizes the hesitation of language information. Secondly, for LGDM problems, a self-confident double hierarchy interval hesitant fuzzy language (SC-DHIHFL) is developed, and the integration of self-confident degree can better enrich the evaluation information and promote the achievement of group consensus. Thirdly, a new two-stage LGDM method is proposed. The first stage is clustering and grouping and reaching consensus within the group, and the second stage is the integration of LGDM information. The two-stage method contains novel methods such as expert clustering algorithm, subjective and objective comprehensive weight, consensus degree, and deviation weight considering minority opinions. Finally, the proposed LGDM consensus method is applied to a practical LGDM problem, and the effectiveness is verified by comparative analysis with existing methods. [ABSTRACT FROM AUTHOR]

Sustainable freight transportation is aimed at reducing environmental emissions and social inequity along with economic inefficiency to drive the business. Lack of a continuous sustainability assessment and monitoring tool increases the reluctance of freight transporters to adopt world-class sustainability practices. This paper attempts to develop the performance index for a sustainable freight transportation system by innovatively integrating the Consensus Model (CM) with the Fuzzy Evidential Reasoning Algorithm (FERA). A CM has been used to determine the degree of importance of each Key Performance Indicators identified across three dimensions of sustainability. FERA has been used to aggregate subjective judgments with crisp quantitative values. This approach has a unique capability to handle various uncertainties related to impreciseness in decision-making. This study has demonstrated the use of an integrated approach for developing a performance index of the freight transportation system. Sensitivity Analysis of the model provides logical inferences and an understanding of the robustness of the model outputs. It can be interpreted from the results of the study that the firms with higher profitability in the market are focusing more on making their operations sustainable. A proportionate approach of firms towards the economic, environment, and societal well-being, may assist in achieving higher sustainability. [ABSTRACT FROM AUTHOR]

Over the past decade, the blockchain technology has gained a significant traction with the introduction of the "bitcoin", the first cryptocurrency back in 2008. The rapid growth of "bitcoin" as a currency and the Bitcoin as a protocol has served as a wake-up call to the IT community which began to realize the real potential of blockchain technology. Blockchain technology is capable of filling many gaps that traditional IT solutions struggle to address. In the other hand the increasing complexity of adjudicating health insurance claims is an urge to introduce more secure and efficient claims adjudication system. Blockchain technology has the potential to reshape the claims adjudication process not only due to its decentralized and tamper-proof features but to its consensus protocols and much more other characteristics. This article studies some of the most recent researches on blockchain solution for health insurance claim adjudication, evaluates the its feasibility and identifies areas where blockchain technology could be applied to improve efficiency and accuracy. This study will also examine the potential benefits and challenges of implementing a blockchain-based system, like reducing administrative costs and improving fraud detection. [ABSTRACT FROM AUTHOR]

Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 (http://lmmd.ecust.edu.cn/admetsar2/admetopt2/), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Background: National standards for nurse practitioner licensure require certification programs to conduct practice analyses to ensure that certified nurse practitioners possess the necessary knowledge for entry-level practice. The practice analysis for the American Association of Critical Care Nurses Certification Corporation (AACN Cert Corp) adult-gerontology acute care nurse practitioner (AGACNP) credential is performed every five years by the AACN Certification Corporation. Purpose: The AACN Cert Corp conducted a practice analysis to confirm that current AGACNP practice is reflected in the ACNPC-AG test plan, and the examination is congruent with 2008 consensus model guidelines. This work describes findings from the 2020 AACN Cert Corp practice analysis and changes in AGACNP practice and academic preparation based on the survey data. Method: : In 2020, AACN Cert Corp volunteer subject matter experts (SMEs) developed a survey of practice activities and competencies relevant to AGACNP practice. Patient care activities and competencies were rated by AGACNP respondents for criticality and frequency. AACN SMEs reviewed criticality and frequency ratings to determine the patient care problems, skills/procedures, and competencies to include in the updated AACN Cert Corp ACNPC-AG test plan. Results: The 2020 AGACNP practice analysis survey and subsequent review resulted in the retention of 33 skills and procedures, 165 patient care problems, and all national competencies in the final ACNPC-AG test plan. Conclusions and Implications: The 2020 AACN Cert Corp AGACNP practice analysis survey describes possible changes in AGACNP practice and academic preparation that have occurred since the 2016 survey, findings that may be associated with the ongoing COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Applying peer-to-peer (P2P) architecture to online video games has already attracted both academic and industrial interests, since it removes the need for expensive server maintenance. However, there are two major issues preventing the use of a P2P architecture, namely how to provide an effective distributed data storage solution, and how to tackle potential cheating behaviors. Inspired by emerging blockchain techniques, we propose a novel consensus model called Proof-of-Play (PoP) to provide a decentralized data storage system that incorporates an anti-cheating mechanism for P2P games, by rewarding players that interact with the game as intended, along with consideration of security measures to address the Nothing-at-stake Problem and the Long-range Attack. To validate our design, we utilize a game-theory model to show that under certain assumptions, the integrity of the PoP system would not be undermined due to the best interests of any user. Then, as a proof-of-concept, we developed a P2P game (Infinity Battle) to demonstrate how a game can be integrated with PoP in practice. Finally, experiments were conducted to study PoP in comparison with Proofof-Work (PoW) to show its advantages in various aspects. [ABSTRACT FROM AUTHOR]

On December 24, 2019, was published Law No. 13.964/2019, known as "Anticrime Law". Result of Bill No. 882/2019, this law aims to provide more severe and effective treatment against corruption, organized crime and crimes involving the use of violence against a person, promoting significant changes in the Penal Code, in the Criminal Procedure Code, Criminal Enforcement Law and in special criminal laws. Among all the changes promoted, worth to mention the introduction of article 28-A in the Criminal Procedure Code, creating the Criminal Non-Prosecution Agreement ("ANPP"), an important consensus instrument that will bring important changes, especially by how criminal proceedings will be seen from now on. The objective of this article is, at first, to analyze the characteristics of this institute, and at the same time, to propose reflections to the possibility of the existence of consensus in the criminal proceedings, adopting the inductive method. What is intended is to enable the reader to get to know this new institute and its main characteristics and requirements, but also to create new approaches and constructive debates on the theme, mainly due to the novelty and importance of the subject. [ABSTRACT FROM AUTHOR]

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent "allowed list" for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results. [ABSTRACT FROM AUTHOR]

Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure–property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions. [ABSTRACT FROM AUTHOR]

After adoption of the 2008 Consensus Model for Advanced Practice Registered Nurse (APRN) Regulation, evolution of the nurse practitioner (NP) role and emergency NP (ENP) specialty has outpaced regulatory standards. Lack of uniformity among regulators, health insurance providers, and employers in acknowledging the ENP specialty has created barriers to practice and access to care. This article serves to identify potential outcomes if the ENP specialty was transitioned to a population within the Consensus Model of Regulation. A strengths, weaknesses, opportunities, and threats (SWOT) analysis methodology was used to assess ENP specialty current state, specifically focusing on the licensure, accreditation, certification, and education model for APRN regulation. The data were collected from peerreviewed literature, clinical subject-matter experts, and academic and advanced practice executive leaders. Variances in ENP licensure, recognition, and acknowledgment among State Boards of Nursing leave the ENP specialty in a precarious position. For each of the strengths and opportunities that could exist in recognizing the ENP at the population level of the Consensus Model, there are abundant weaknesses and threats. Although full acknowledgment of the ENP specialty is lacking, regulatory implications are highly variable, and employers' understanding of the APRN educational model is limited, our findings reveal that the ENP specialty is an evolving role that deserves regulatory legitimization. Although ideal placement for the ENP within the Consensus Model remains to be determined, challenges and opportunities exist with both specialty and population options. [ABSTRACT FROM AUTHOR]

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%-61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals. [ABSTRACT FROM AUTHOR]

Blockchain technology ensures the security of cross-organizational data sharing in the process of collaborative innovation. It drives the development of collaborative innovation in discrete manufacturing to intelligent innovation. However, collaborative innovation is a multi-role, networked, and open resource-sharing process. Therefore, it is easy to form information barriers and increase the risk of cooperation between organizations. In this paper, we firstly analyze the blockchain- based information management models in the traditional discrete manufacturing collaborative innovation process. Then, we found that in the process of industry-university-research (IUR) collaborative innovation, consensus servers maintain too many connections due to the high latency between them, which leads to lower consensus performance and efficiency. To solve this problem, we proposed the dependency analysis (DA) model, which separates and assembles transactional data and non-transactional data into different blocks by analyzing read_set and write_set of transactions. Besides, we propose the out-of-order Raft (OORaft) model to allow non-transactional blocks to be copied in parallel, which can also prioritize transactional blocks. Finally, we implement the blockchain model in the discrete manufacturing scenario based on Hyperledger Fabric. The experimental results show that our model improves the transactions per second (TPS) performance of 1.9X-3.7X and reduces transactional data committing latency by more than 40% in the target scenario. [ABSTRACT FROM AUTHOR]

With the development of the cloud computing era, the decision-making environment and algorithm models have become increasingly complex, and traditional decision-making methods have been unable to meet the needs of large group decision-making (LGDM) problems. Firstly, in order to solve this problem, the concept of double hierarchy interval hesitant fuzzy language (DHIHFL) is proposed. Compared with the traditional double hierarchy hesitant fuzzy language (DHHFL), it contains all elements from the lower limit to the upper limit and more comprehensively characterizes the hesitation of language information. Secondly, for LGDM problems, a self-confident double hierarchy interval hesitant fuzzy language (SC-DHIHFL) is developed, and the integration of self-confident degree can better enrich the evaluation information and promote the achievement of group consensus. Thirdly, a new two-stage LGDM method is proposed. The first stage is clustering and grouping and reaching consensus within the group, and the second stage is the integration of LGDM information. The two-stage method contains novel methods such as expert clustering algorithm, subjective and objective comprehensive weight, consensus degree, and deviation weight considering minority opinions. Finally, the proposed LGDM consensus method is applied to a practical LGDM problem, and the effectiveness is verified by comparative analysis with existing methods.

• Among nurse practitioners practicing acute care with adults, 44.5% held acute care certification. • Odds of certification were higher when states restricted practice by certification. • Job satisfaction, role perception, and earnings did not differ by certification. • Groups that endorsed the Consensus Model can make changes to enforce it. The Consensus Model specifies distinct education preparations for acute care and primary care nurse practitioners (NP), but incomplete implementation and employer hiring practices risk misalignment of certification and practice. Report the prevalence of acute care certification among NPs working in acute care, investigate the factors associated with alignment, and explore the impact of alignment on nurse outcomes. Using the 2018 National Sample Survey of Registered Nurses, we selected NPs practicing acute care and compared employment, education, and nurse outcomes by certification alignment. A minority of NPs (44.5%) held acute care certification. Controlling for NP characteristics, those practicing in states that restrict a NP's practice to their area of certification had 47% higher odds of acute care certification. Understanding patterns of alignment in the NP workforce and the factors that produce them is critical to an appropriate regulatory framework for advanced practice nursing. [ABSTRACT FROM AUTHOR]

• EC 50 values for 21 TWP-derived compounds on C. vulgaris growth were 0.04‒8.60 mg/L. • A QSAR model, employing GA and MLR, was constructed with 112 compounds. • The consensus and q-RASAR models were developed to enhance prediction reliability. • The q-RASAR model was selected to predict the toxicity of 128 emerging compounds. • Predicted higher toxicity of diphenylamines linked to SpMax2_Bhm and LogBCF. Tire wear particles (TWPs) enter aquatic ecosystems through various pathways, such as rainwater and urban runoff. Additives in TWPs can harm aquatic organisms in these ecosystems. Therefore, it is essential to investigate their toxicity to aquatic organisms. In our study, we initially recorded the median effective concentrations of 21 TWP-derived compounds on Chlorella vulgaris growth, ranging from 0.04 to 8.60 mg/L. Subsequently, through an extensive review of the literature, we incorporated 112 compounds with specific toxicity endpoints to construct the QSAR model using genetic algorithm and multiple linear regression techniques, followed by the construction of the consensus model and the quantitative read-across structure-activity relationship (q-RASAR) model. Meanwhile, we employed rigorous internal and external validation measures to assess the performance of the model. The results indicated that the developed q-RASAR model exhibited strong adaptation, robustness, and reliable prediction, with q-RASAR indicators of Q2 LOO = 0.7673, R2 tr = 0.8079, R2 test = 0.8610, Q2 Fn = 0.8285−0.8614, and CCC test = 0.9222. Based on an external dataset containing 128 emerging TWP-derived compounds, the model's applicability domain coverage was 90.6 %. The q-RASAR model predicted that the structure of diphenylamine was associated with higher toxicity, possibly liked to the SpMax2_Bhm and LogBCF descriptors. The established model reliably provides prediction and fills a critical data gap. These findings highlight the potential risks posed by emerging TWP-derived compounds to aquatic organisms. [Display omitted] [ABSTRACT FROM AUTHOR]

In the consensus-reaching process ( CRP), the actual utility of decision-makers (DMs) is often influenced by the psychological behavior of regret aversion. However, the influence of regret aversion on DMs' utilities is rarely taken into account in the existing consensus models. The consensus-reaching problem of DMs with regret aversion is explored to maximize their perceived utilities under a limited budget. Firstly, three basic types of perceived utility functions are constructed based on the regret theory to describe the perceived utility of DMs with various preferences. Then, considering the limited budget and individual regret aversion, the maximum perceived utility consensus models based on types of left-skewed, right- skewed, middle-skewed, and heterogeneous utility preferences are proposed to achieve the consensus that maximizes the group perceived utility. After that, an example of land-transfer price negotiation in China is given to illustrate the validity of the proposed models. Finally, the model comparison and the sensitivity analysis are presented to reveal the influence of DMs' regret aversion on the CRP. The results suggest that the DMs' regret aversion will not affect left-skewed and right-skewed groups, but will affect the consensus results of middle-skewed and heterogeneous groups. [ABSTRACT FROM AUTHOR]

To represent qualitative aspect of uncertainty and imprecise information, linguistic preference relation (LPR) is a powerful tool for experts expressing their opinions in group decision-making (GDM) according to linguistic variables (LVs). Since for an LV, it generally means that membership degree is one, and non-membership and hesitation degrees of the experts cannot be expressed. Pythagorean linguistic numbers/values (PLNs/PLVs) are novel choice to address this issue. The aim of this paper which we propose a GDM problem involved a large number of the experts is called large-scale GDM (LSGDM) based on Pythagorean linguistic preference relation (PLPR) with a consensus model. Sometimes, the experts do not modify their opinions to achieve consensus. Therefore, the experts' proper opinions' management with their non-cooperative behaviors (NCBs) is necessary to establish a consensus model. At the same time, it is essential to ensure the proper adjustment of the credibility information. The proposed model using grey clustering method is divided with the experts' similar evaluations into a subgroup. Then, we aggregate the experts' evaluations in each cluster. A cluster consensus index (CCI) and a group consensus index (GCI) are presented to measure consensus level among the clusters. Then, we provide a mechanism for managing the NCBs of the clusters, which contain two parts: (1) NCB degree is defined using CCI and GCI for identifying the NCBs of the clusters; (2) implemented the weight punishment mechanism of the NCBs clusters to consensus improvement. Finally, an example is offered for usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) is a system activity that identifies, evaluates and eliminates potential failure modes (FMs) in a system/process to enhance the quality and reliability of a product. In order to improve the implementation efficiency of FMEA, this study proposes a consensus-based FMEA method to derive the ordinal classifications of FMs, in which the FMEA team members employ linguistic distribution to convey their preferences. In the proposed FMEA method, a multi-stage consensus optimization model with bounded confidences is designed to help the FMEA team reach a consensus. In the consensus reaching process, a maximum consensus optimization model based on bounded confidences is provided to obtain the adjustment suggestions by maximizing the level of consensus among the FMEA team. If the predetermined level of consensus cannot be reached, the adjustment suggestions obtained by the maximum consensus optimization model are adopted to guide the preference-modification of the FMEA team members. Otherwise, a two-stage consensus optimization model based on bounded confidences is designed to derive the adjustment suggestions for the preference-modification of the FMEA team members. Finally, a case study of marine diesel engine crankcase explosion, a sensitivity analysis and a comparative analysis are proposed to illustrate the feasibility and effectiveness of the proposed FMEA method. [ABSTRACT FROM AUTHOR]

A certain flexibility of decision makers (DMs) is usually requisite for reaching the consensus in group decision making (GDM). When the judgments of DMs are expressed as fuzzy-valued quantities, the required flexibility has been shown. In this paper, we report a quantification approach to flexibility degrees (FDs) of fuzzy numbers and a consensus model in GDM with fuzzy-valued judgments. First, an axiomatic definition of FDs is proposed for fuzzy numbers. Based on a bounded positive scale, the novel formulae are constructed for quantifying the FDs of interval numbers, triangular fuzzy numbers (TFNs), and trapezoidal fuzzy numbers (TPFNs), respectively. The effects of the prototypical values of TFNs and TPFNs on the FDs are addressed. Second, by considering confidence levels and multiplicative reciprocity, the FD of triangular fuzzy multiplicative reciprocal preference relations (TFMRPRs) is calculated. A flexibility-degree-driven operator is provided to aggregate individual TFMRPRs. Third, the consensus degree of DMs is defined and a nonlinear optimization model is constructed to achieve the largest consensus degree. A new algorithm for the consensus model in GDM is elaborated on and the case study is made to illustrate the proposed method. Finally, the sensitivity of the centroid of TFNs on the final solution of a GDM problem is analyzed by numerical examples. The observations reveal that the position of the centroid plays a great role on the FD of TFNs. [ABSTRACT FROM AUTHOR]

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development. [ABSTRACT FROM AUTHOR]

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a consensus deep-learning solution that uses two deep neural networks to deal with sparse and noisy social media data. In the first step, high accuracy is achieved by implementing a deep neural network that has more balanced home location candidates, using batch normalization, and duplicating home location records. We obtained over 92% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

People's demand for the decision-making space of opinion expression is getting higher, and the methods to determine the threshold value of current consensus still remain elusive. To deal with large and diverse information of users and discuss deeply the threshold in social networks, we establish a new consistency model with a new preference structure. In this paper, the Pythagorean fuzzy numbers (PFNs) are introduced into social network group decision-making for the expression of decision-makers' preference (DMs) and the concepts definition of the distance measurements, consensus index, and threshold indifference curves, respectively. In addition, we establish a Pythagorean fuzzy group consensus model with minimum adjustment through determining the setting rule of threshold value before reaching the consensus. Finally, we use the proposed model to solve the selection of square cabin hospitals. [ABSTRACT FROM AUTHOR]

Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

Aims: The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors., Background: The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase Ⅱclinical trials; thus, it is necessary to develop novel POLQ inhibitors., Objectives: To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations., Methods: In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were Collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results., Results: The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations., Conclusion: We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network. [ABSTRACT FROM AUTHOR]

Drug‐induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug‐induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non‐ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5‐fold cross‐validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug‐induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment. [ABSTRACT FROM AUTHOR]

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149, respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota–Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment. [ABSTRACT FROM AUTHOR]