Set Of Alternatives Research Articles

Lessons from enterprise systems competency centers in adopting digital transformation initiatives: An assemblage approach

Firms are increasingly adopting digital transformation as a strategic priority. However, the path to successful transformation remains uncertain for many organizations. This paper examines the establishment and evolution of competency centers in two case study organizations, historically used in enterprise systems, in addressing the complexity and challenges of digital transformation. The interactions within these competency centers are analyzed through assemblage theory to understand the emergent relations between heterogeneous parts (technology, people, and organization) and the dynamic processes of new configurations. The insights from this research show the critical role of the competency center in any enterprise system's success and how it could continue playing a central role in future digital transformation initiatives. By providing a new lens to examine these issues, the assemblage theory provided a new theoretical perspective to the IS field and a new alternative empirical setting to the organizational literature.

On a class of strategy-proof social choice correspondences with single-peaked utility functions

We consider the problem of constructing strategy-proof rules that choose sets of alternatives based on the preferences of voters, modelled as Social Choice Correspondences (SCCs) in the literature. We focus on two domain restrictions inspired by Barberà et al. (2001) in the context of single-peaked utility functions. We find that for the narrower domain, the set of tops-only, unanimous, and strategy-proof SCCs coincides with the class of unions of two min–max rules (Moulin, 1980). For the broader domain, the set of SCCs coincides with the class of unions of two ‘adjacent’ min–max rules, meaning the corresponding parameters for the two rules must be either the same or consecutive alternatives.

Measuring Countries' Performance in Ecological Security

The research aims to construct a novel secure regional development index focusing on ecological security aspects of eight Sustainable Development Goals (SDGs). The index building is based on the Sustainable Development (SD) Security Model that measures ecological threats' harm to the regional ecosystem. The ultimate goal is to construct a tool to manage facets of sustainable regional development. The research study is focused on G20 countries. The databank of the World Bank organisation was utilised to get secondary data of selected security indicators to measure security for the Sustainable Development Goals (SDGs) of the G20. The methodological approach relies on grouping ecological security indicators (Clustering using the K-Means method). It suggests a meaningful shortlist of indicators in each group, which would obtain a structured system of indicators suitable for constructing a novel Secure Regional Development Index using the multiple criteria decision-making TOPSIS method that selects the best alternative from a set of alternatives according to several criteria.

Skeleton-based shape similarity.

Many aspects of visual perception, including the classification of shapes into known categories and the induction of new shape categories from examples, are driven by shape similarity. But there is as yet no generally agreed, principled measure of the degree to which two shapes are "similar." Here, we derive a measure of shape similarity based on the Bayesian skeleton estimation framework of Feldman and Singh (2006). The new measure, called generative similarity, is based on the idea that shapes should be considered similar in proportion to the posterior probability that they were generated from a common skeletal model rather than from distinct skeletal models. We report a series of experiments in which subjects were shown a small number (1, 2, or 3) of 2D or 3D "nonsense" shapes (generated randomly in a manner designed to avoid known shape categories) and asked to select other members of the "same" shape class from a larger set of (random) alternatives. We then modeled subjects' choices using a variety of shape similarity measures drawn from the literature, including our new measure, skeletal cross-likelihood, a skeleton-based measure recently proposed by Ayzenberg and Lourenco (2019), a nonskeletal part-based similarity model proposed by Erdogan and Jacobs (2017), and a convolutional neural network model (Vedaldi & Lenc, 2015). We found that our new similarity measure generally predicted subjects' selections better than these competing proposals. These results help explain how the human visual system evaluates shape similarity and open the door to a broader view of the induction of shape categories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Creating technical criteria for the hierarchization of public works: Case study in Paraná state, Brazil

Public works in general play an important role because of their expressive value and considerable social relevance. In this respect, it is necessary to establish criteria/indicators for defining the hierarchy of works that must be conducted by a public entity, mainly for works considered ordinary, that is, those that do not stand out from a set of alternatives by degree of emergency or importance. Specifically, the hierarchization of engineering works to be carried out by the Public Administration in Brazil usually adopts the political approach to decision making without the application of rational technical criteria. Therefore, the aim of this article is to present a methodology to define the hierarchy of engineering works with the judiciary branch of power through the creation of technical criteria/indicators, followed by the application of the Fuzzy-Delphi technique to define the indices/weights that comprise the indicators. The technical criteria were created based on Resolution 114 of the country's National Council of Justice (Conselho Nacional de Justiça; CNJ), whose weights were obtained through the aforementioned method, which was applied to a panel of experts, in two stages, with the adoption of triangular fuzzy numbers. The results, which were clear and objective, provide the General Hierarchization Index (GHI), with the ordering/ranking of the works included in the process. The methodology was validated in a case study applied to 162 alternatives (districts), containing 192 buildings of the Court of Justice of Paraná State (Tribunal de Justiça do Paraná; TJPR), proving to be an important tool to support decision making. The optimization of resources through the determination of priorities converges to the sustainable hiring applied to public contracts. Institutions of the other Powers of the Republic could and should also be encouraged by public authorities to conduct technical studies similar to the proposal presented here, always clarifying the need for a clear and objective definition of the criteria and weights to be applied in the development of solutions for multicriteria decision making.

Matrix element corrections in the Pythia8 parton shower in the context of matched simulations at next-to-leading order

We discuss the role of matrix element corrections (MEC) to parton showers in the context of MC@NLO-type matchings for processes that feature unstable resonances, where MEC are liable to result in double-counting issues, and are thus generally not employed. By working with Pythia8, we show that disabling all MEC is actually unnecessary in computations based on the narrow-width approximation, and we propose alternative MEC settings which, while still avoiding double counting, allow one to include hard-recoil effects in the simulations of resonance decays. We illustrate our findings by considering tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}$$\\end{document} production at the LHC, and by comparing MadGraph5-aMC@NLO predictions with those of POWHEG-BOX and standalone Pythia8.

Using intervention mapping to design and implement a multicomponent intervention to improve antibiotic and NSAID prescribing.

Successfully changing prescribing behavior to reduce inappropriate antibiotic and nonsteroidal anti-inflammatory drug (NSAID) prescriptions often requires combining components into a multicomponent intervention. However, multicomponent interventions often fail because of development and implementation complexity. To increase the likelihood of successfully changing prescribing behavior, we applied a systematic process to design and implement a multicomponent intervention. We used Intervention Mapping to create a roadmap for a multicomponent intervention in unscheduled outpatient care settings in the Veterans Health Administration. Intervention Mapping is a systematic process consisting of six steps that we grouped into three phases: (i) understand behavioral determinants and barriers to implementation, (ii) develop the intervention, and (iii) define evaluation plan and implementation strategies. A targeted literature review, combined with 25 prescriber and 25 stakeholder interviews, helped identify key behavioral determinants to inappropriate prescribing (e.g. perceived social pressure from patients to prescribe). We targeted three desired prescriber behaviors: (i) review guideline-concordant prescribing and patient outcomes, (ii) manage diagnostic and treatment uncertainty, and (iii) educate patients and caregivers. The intervention consisted of components for academic detailing, prescribing feedback, and alternative prescription order sets. Implementation strategies consisted of preparing clinical champions, conducting readiness assessments, and incentivizing use of the intervention. We chose a mixed-method study design with a commonly used evaluation framework to assess effectiveness and implementation outcomes in a subsequent trial. This study furthers knowledge about causes of inappropriate antibiotic and NSAID prescribing and demonstrates how theoretical, empirical, and practical information can be systematically applied to develop a multicomponent intervention to help address these causes.

A fuzzy interval based multi-criteria homogeneous group decision making technique: An application to airports ranking problem

This paper aims to develop and introduce a fuzzy Interval based Multi-criteria homogeneous Group Decision making technique (IMGD) to make appropriate decision under fuzzy environment. In fuzzy multi-criteria group decision making process, a group of decision makers often considers several subjective criteria for ranking a set of alternatives. Due to vague and imprecise information, decision makers generally utilize linguistic variables which are mandatorily converted into triangular or trapezoidal fuzzy numbers. The total process then becomes very complex and time consuming. The current investigation advocates fuzzy intervals instead of triangular or trapezoidal fuzzy numbers for simplification of the complex situation and ease of calculation. In this method, fuzzy intervals of performance ratings and weights assessed by homogeneous group decision makers under subjective criteria are converted into first mean fuzzy intervals then into normalized crisp numbers. The normalized crisp performance ratings and normalized crisp weights are combined together to determine initially individual contribution and then into aggregate contribution to each alternative for final ranking and selection of the alternative. The new model is demonstrated with an application to airports ranking and selection problem for better clarification and verification. The outcome of the proposed is validated with the results obtained by well-known existing MCDM techniques. The analysis shows that the proposed method is applicable, useful and effective for appropriate decision making under fuzzy MCDM environment.

Classification of patients with chronic disease by activation level using machine learning methods.

Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. [Formula: see text] of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.

An integrated intelligent decision support framework for the development of photovoltaic solar power

An emerging question for photovoltaic (PV) solar power development is how to ascertain the optimal choice from a finite set of available alternatives under numerous conflicting criteria as well as high levels of imprecise, vague, and uncertain information. For the first time, we investigate the prioritization of the alternatives for the development of PV solar power via the interval-valued intuitionistic fuzzy sets (IVIFSs), which show great power in capturing ambiguous, uncertain, and vague information, and mitigating information loss. This paper presents a novel integrated intelligent decision support system comprising of Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Hierarchy Process (AHP), and Combined Compromise Solution (CoCoSo) within an interval-valued intuitionistic fuzzy (IVIF) framework. Four alternatives are considered. To rank these alternatives, twelve criteria are defined under four aspects of SWOT analysis based on literature review and discussion with decision-makers. Subsequently, the IVIF-AHP method is utilized to determine the weights assigned to each criterion and sub-criterion. Finally, the IVIF-CoCoSo method is employed to rank four alternatives. The results showed that giving proactive attention to mitigating potential adverse environmental impacts of PV solar systems is the most highly prioritized strategy for PV solar power development. The results of the comparative and sensitivity analyses showed that the proposed method generates highly robust outcomes. The formulated integrated intelligent decision support system can help energy policy authorities with a valuable resource to craft optimal techniques for developing PV solar power.

Median-Joining Networks and Bayesian Phylogenies Often Do Not Tell the Same Story

Inferring phylogenies among intraspecific individuals often yields unresolved relationships (i.e., polytomies). Consequently, methods that compute distance-based abstract networks, like Median-Joining Networks (MJNs), are thought to be more appropriate tools for reconstructing such relationships than traditional trees. Median-Joining Networks visualize all routes of relationships in the form of cycles, if needed, when traditional approaches cannot resolve them. However, the MJN method is a distance-based phenetic approach that does not involve character transformations and makes no reference to ancestor–descendant relationships. Although philosophical and theoretical arguments challenging the implication that MJNs reflect phylogenetic signal in the traditional sense have been presented elsewhere, an empirical comparison with a character-based approach is needed given the increasing popularity of MJN analysis in evolutionary biology. Here, we use the conservative Approximately Unbiased (AU) test to compare 85 cases of branching patterns of cycle-free MJNs and Bayesian Inference (BI) phylogenies using datasets from 55 empirical studies. By rooting the MJN analyses to provide directionality, we report substantial disagreement between computed MJNs and posterior distributions on BI phylogenies. The branching patterns in MJNs and BI phylogenies show significantly different relationships in 37.6% of cases. Among the relationships that do not significantly differ, 96.2% show alternative sets of relationships. Our results indicate that the two methods provide different measures of relatedness in a phylogenetic sense. Finally, our analyses also support previous observations of the statistical hypothesis testing by reconfirming the over-conservativeness of the Shimodaira-Hasegawa test versus the AU test.

Convex economic model predictive control for blade loads mitigation on wind turbines

AbstractEconomic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade‐off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real‐time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC‐IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid‐fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.

Does control of corruption play its role in enhancing the innovation? Empirical evidence from Asian region

Purpose In the current competitive era of industrialization, a significant level of innovation is necessary to meet the growing competition. There are many economic forces that determine the pace of innovation within a country. Among others, this study aims to focus on exploring the relevant role of corruption control (CC) in determining the innovation level. Design/methodology/approach For empirical analysis, the authors sample the 24 years of data (1996–2019) of Asian economies and use the fully modified ordinary least square (OLS) and dynamic OLS models to check the regression among variables. The selection of both techniques is based upon the empirical suggestions offered by unit root testing and the Johansen cointegration test. Findings The empirical findings infer the positive and statistically significant role of CC in boosting innovation. Strengthening the corruption-free environment encourages innovation activities within the country. In addition, foreign direct investment has a negative relationship with CC while financial development, economic growth, export volume and government subsidies positively determine the innovation level. Practical implications Based on empirical analysis, it is suggested that the policy officials should do more focus on CC to enhance the competitiveness of the country through more innovation. Originality/value The empirical analysis robust the findings of existing literature in an alternative data set and offers innovative views regarding the role of other factors in boosting the innovation level.

Excessive concentrations of kinase inhibitors in translational studies impede effective drug repurposing

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in invitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.

Privacy-Preserving Personalized Revenue Management

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer’s vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm’s pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost “for free.” That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) “Investissements d’Avenir” [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925 .

Spirituality as a Protective Factor

Adults who were separated from parental care as children and resided in alternative care settings often experience difficulties throughout their lifespan. Thus, it is important to explore factors that lead to better outcomes. Spirituality is one protective factor that has received attention in academic literature in recent years. However, little work has examined spirituality in this population or the specific ways it impacts wellbeing in adulthood. Using thematic analysis, the current study explored the specific ways spirituality serves as a protective factor in a multinational sample of 267 individuals with care experience. Data revealed six spirituality-related themes: 1) relationship with God, 2) spiritual community support, 3) spirituality as a component of identity development, 4) a belief in a higher purpose, 5) spirituality-motivated servanthood, and 6) access to spirituality-based teachings and teachers. Based on these findings, we propose data-informed recommendations to improve services for children separated from parental care.

Children's lead exposure in the U.S.: Application of a national-scale, probabilistic aggregate model with a focus on residential soil and dust lead (Pb) scenarios

Lead (Pb) exposures from soil and dust ingestion contribute to children's blood lead levels (BLLs) in the United States. The U.S. Environmental Protection Agency (EPA)'s Strategy to Reduce Lead Exposures and Disparities in U.S. Communities and the Federal Action Plan to Reduce Childhood Lead Exposure describe multi-pronged collaborative approaches. These include reducing multi-media lead exposures nationally using analytical tools such as EPA's Stochastic Human Exposure and Dose Simulation model for lead [SHEDS-Pb; formerly known as SHEDS-IEUBK (Integrated Exposure Uptake Biokinetic model)], which was initially developed and applied with a focus on children's drinking water exposures. In this study we applied SHEDS-Pb to determine what residential soil Pb and dust Pb concentrations (individually and their sum) can keep BLLs of potentially exposed young children in the general U.S. population below specified values, considering aggregate exposures from water, soil, dust, food, and air. We considered two age groups (1 to <2 years and 2 to <6 years), two BLL values (5 μg/dL and 3.5 μg/dL), and two population percentiles (95th and 97.5th). Sensitivity analyses were conducted using several alternative model inputs and data sets, yielding 15 scenarios summarized in the paper. Of those scenarios, we focused on ones with the most recent science and available data. Modeled soil Pb concentrations by age group, population percentile and reference BLL scenarios for the focus scenarios ranged from 70 ppm to 220 ppm; and modeled dust Pb concentrations ranged from 110 ppm to 240 ppm. These results are consistent with current soil and dust Pb concentrations in the U.S. general population and are lower than most of the current U.S. Federal standards. Estimated BLLs compared well with measured BLLs from CDC's NHANES 2009–2016 (0–27 % relative error for focus scenarios). This analysis can be used to inform EPA and other federal Pb efforts.

IV Diuresis in Alternative Treatment Settings for the Management of Heart Failure: Implications for Mortality, Hospitalizations and Cost

Recent advances in heart failure (HF) care have sought to shift management from inpatient to outpatient and observation settings. We evaluated the association among HF treatment in the (1) inpatient; (2) observation; (3) emergency department (ED); and (4) outpatient settings with 30-day mortality, hospitalizations and cost. Using 100% Medicare inpatient, outpatient and Part B files from 2011-2018, 1,534,708 unique patient encounters in which intravenous (IV) diuretics were received for a primary diagnosis of HF were identified. Encounters were sorted into mutually exclusive settings: (1) inpatient; (2) observation; (3) ED; or (4) outpatient IV diuretic clinic. The primary outcome was 30-day all-cause mortality. Secondary outcomes included 30-day hospitalization and total 30-day costs. Multivariable logistic and linear regression were used to examine the association between treatment location and the primary and secondary outcomes. Patients treated in observation and outpatient settings had lower 30-day mortality rates (observation OR 0.67, 95% CI 0.66-0.69; P < 0.001; outpatient OR 0.53, 95% CI 0.51-0.55; P < 0.001) compared to those treated in inpatient settings. Observation and outpatient treatment were also associated with decreased 30-day total cost compared to inpatient treatment. Observation relative cost -$5528.77, 95% CI -$5613.63 to -$5443.92; outpatient relative cost -$7005.95; 95% CI -$7103.94 to -$6907.96). Patients treated in the emergency department and discharged had increased mortality rates (OR 1.15, 95% CI 1.13-1.17; P < 0.001) and increased rates of hospitalization (OR 1.72, 95% CI 1.70-1.73; P < 0.001) compared to patients treated as inpatients. Medicare beneficiaries who received IV diuresis for acute HF in the outpatient and observation settings had lower mortality rates and decreased costs of care compared to patients treated as inpatients. Outpatient and observation management of acute decompensated HF, when available, is a safe and cost-effective strategy in certain populations of patients with HF.

Sandwich-structured covalent organic framework membranes for selective sodium ion transport

Reverse electrodialysis (RED) is a promising renewable energy technology for salinity gradient power generation, while its key component (i.e., ion exchange membranes) faces the challenge of the lower energy density caused by the uphill transport of divalent ions (e.g., Mg2+). In this study, we carried out a series of molecular dynamics (MD) simulations to investigate the permselective transport of Na+ and Mg2+ through a novel sandwich-stacked hybrid covalent organic framework (COF) membrane. The alternate set of TpHz and TpPa-SO3H constructed reduced effective pore size ranging from 5.6 to 7.5 Å, producing an offset-block effect on the ion transport. Besides, the tunable interlayer spacing of the hybrid COF membrane could manipulate the rehydration/dehydration behavior of the confined Na+ ions. A suitable set of the interlayer spacing could achieve an adorable Na+/Mg2+ permselectivity and Na+ permeance by the synergistic effect of aperture sieving and the electrostatic affinity. This work provides useful insights into the Na+/Mg2+ permselective mechanism and further helps to design COF-based membranes for realizing a high energy efficiency for the application of RED.

The Application of Fuzzy Weak Autocatalytic Set in Robots Kinematic Structures Evaluation

Pairwise comparison of alternatives is common in decision-making procedures. A fuzzy weak autocatalytic set (FWACS) is a technique used in solving multi-criteria decision-making problems. A FWACS represented the comparison of a set of alternatives in form of a directed graph with fuzzy edges. This study discusses the implementation of ranking by FWACS on the evaluation of robots’ kinematic structures. A comparison of results generated by FWACS is made with the results using Potential Method (PM). Sensitivity analysis verifies that the decision made by FWACS is stable and consistent, thus comparable to PM.