Search

EnglishThe objective of this study is to elaborate a decision-making model for the Brazilian electricity sector, based on sectorial indicators of energy sustainability in the industrial segment. In public electricity management, the indicators help guide decision-making with the intention of consolidating the relevant role of electricity in development. In the intricacies of each sector of economic activity, electricity reflects in different ways the flow of jobs, consumption, concentration of income, gas emissions, among other variables. In this perspective, this article questions how the decision-making process in the Brazilian electricity sector could be strategically supported by sectorial indicators of energy sustainability. The methodology of this investigation built sectorial indicators of energy sustainability, from linear correlations verified between variables of energy input and development variables, whose results fed a decision structure supported by technology, norms and rules and decision-making style. In the methodology, the State of Para was adopted as the study site and the time span between 2010 and 2019. An analysis was used based on linear correlations between variables relevant to electricity and variables related to socioeconomic development, within the industrial sector. Then, a structure for feeding the decision-making process in the electricity sector was developed, based on the results of indicators calculated in the economic, social, environmental and political dimensions. The results found indicate the need to reread the decision-making process through a model of indicators that strategically guide the translation of electricity from production processes in the industrial sector. The study resulted in a proposed decision-making model, which suggested, among other recommendations, actions linked to increasing energy autonomy in Para, redirecting the industrial profile, including compensatory devices for environmental costs, directing investments to increase GDP in the reality of the industrial sector. portuguesO objetivo deste estudo e elaborar um modelo decisorio ao setor eletrico brasileiro, baseado em indicadores setoriais de sustentabilidade energetica no segmento industrial. Na gestao publica de energia eletrica, os indicadores favorecem a orientacao da tomada de decisao na intencao de consolidar o relevante papel da eletricidade junto ao desenvolvimento. Nos meandros de cada setor de atividade economica, a energia eletrica reflete de diferentes maneiras os fluxos de empregos, consumo, concentracao de renda, emissao de gases, entre outras variaveis. Nesta perspectiva, e que artigo questiona como o processo decisorio no setor eletrico brasileiro poderia ser apoiado estrategicamente por indicadores setoriais de sustentabilidade energetica. A metodologia dessa investigacao construiu indicadores setoriais de sustentabilidade energetica, a partir de correlacoes lineares verificadas entre variaveis do insumo energetico e variaveis do desenvolvimento, cujos resultados alimentaram uma estrutura decisoria apoiada na tecnologia, em normas e regras e no estilo decisorio. Na metodologia adotou-se o Estado do Para como local de estudo e o espaco temporal entre 2010 e 2019. Utilizou-se uma analise a partir de correlacoes lineares entre variaveis pertinentes a energia eletrica e variaveis relativas ao desenvolvimento socioeconomico, no âmbito do setor industrial. Em seguida, desenvolveu-se uma estrutura de alimentacao do processo decisorio no setor eletrico, a partir dos resultados dos indicadores apurados nas dimensoes economica, social, ambiental e politica. Os resultados encontrados indicam a necessidade de releitura do processo decisorio por meio de um modelo de indicadores que oriente estrategicamente a traducao da eletricidade a partir dos processos produtivos no setor industrial. O estudo resultou no modelo decisorio proposto, que sugeriu acoes vinculadas ao aumento de autonomia energetica no Para, redirecionamento do perfil industrial, inclusao de dispositivos compensatorios dos custos ambientais, direcionamento de investimentos para o aumento de PIB na realidade do setor industrial, entre outras recomendacoes.

Fault prognosis of discrete event systems (DES) is an active field of research and has become important due to the high demand on systems’ safety and reliability. The aim of this paper is to provide the state-of-the-art on fault prognosis of DES, as well as quick directions regarding many aspects of the research on this topic. This overview was carried out using a systematic approach, which allowed us to find and select papers with minimum bias. In total, 74 papers were selected and analyzed in this overview. A classification and an analysis of these papers are made regarding the modeling formalism and decision structure of prognosis of DES. Specific notions of prognosis are also presented. A survey on these papers was performed, regarding practical applications and related problems of prognosis. Results show that most papers present solutions based on languages & automata (56), and most papers consider the centralized decision structure (45). Also, we have found 17 different papers with examples of practical applications of prognosis of DES. We have identified research gaps, such as applications of distributed prognosis and distributed prognosis of Petri nets. Finally, the research activity on prognosis of DES is growing 16.14% by year on average, accordingly to the number of published papers on this topic between 2006 and 2020.

During new venture creation, entrepreneurs make decisions in a variety of areas from seeking funding to hiring employees. When and why entrepreneurs use effectual or causal logics to make such decisions is poorly understood. In this study, we integrate ecological rationality theory and effectuation theory to examine how the nature of decisions influences entrepreneurs’ use of decision logics. In a qualitative study with 41 entrepreneurs across 290 decisions, we explore how decision content (what the decision is about) and decision structure (what information about a decision is represented in the decision-maker’s mind) influence entrepreneurs’ use effectual or causal logics. We extend our findings in an experiment with 224 entrepreneurs where we manipulate decision structure. Our results suggest that decision content influences entrepreneurs’ mental representations of decision structure. In turn, the combination of two elements of decision structure — decision complexity and the perceived costs of implementing different options — drives entrepreneurs’ use of decision logics. We contribute to the effectuation literature by integrating it with ecological rationality theory, introducing the concept of decision fit as a driver of decision logics and developing our understanding of hybrid decision-making (the simultaneous use of effectuation and causation).

This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities. We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering (ML) increases the ratio of reported transactions to unreported transactions. If a representative money launderer makes an optimal portfolio choice, then this ratio increases further. Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks. We attribute this result to cryptocurrency exchanges’ inferior ML detection abilities and their proximity to the underground economy.

In Egypt, people are unable to determine the qualities of appropriate residence that achieves quality and occupant satisfaction, and contributes to sustainability of residential conglomerations. In general, developing countries lack housing information which can be used to enhance quality of residence. Also, the methods of assessing and identifying the appropriate criteria for future residence quality remain traditional ones that cannot address the multiple, conflicting, overlapping aspects to reach a good decision. This calls for using the Analytical Network Process (ANP), an effective tool for specifying the relative importance of all factors impacting a specific issue for making an appropriate residential decision. In addition, this method provides results for the decision element impacts network within the decision structure; thus contributing to more understanding of the mechanisms and requirements of residence selection. The proposed decision structure comprises a two-level network: main clusters, main elements, and sub-elements included in the demographic characteristics group, the residence criteria group, the demand parameters group, the supply parameters group, the residence specifications group, and the alternatives group which representing, in total, the decision and specifying the percentage needed for each housing level. Results of the model showed complete capacity in smoothly addressing complexities and overlapping in the decision structure. The decision structure showed that 52% chose luxury residence, 28% chose middle-class residence, and 19.5% chose the economic residence. Mechanisms of decision making were analyzed; particularly in terms of relationship to demographic characteristics and residence specifications. Also, the importance and impact of demand / supply parameters in reaching decision were analyzed.

We examine the impact of activated (salient) versus non-activated ethical norms on the aggressiveness of accounting decisions, in the presence of self-interest favoring aggressiveness. Using a case in which the accounting rules are ambiguous, we ask professional accountants to make an accounting decision as though they were in their own organization; we measure the ethical norms of their organization at the end of the experiment. Based on the focus theory of normative conduct, we argue that the general ethical norms of the participants’ organizations are activated when the decision structure is such that the participant receives a recommendation from a subordinate, whereas those norms are not activated when the participant is making the decision alone. We find that higher ethical norms decrease aggressiveness when the decision maker receives a recommendation, whereas higher ethical norms have no impact on aggressiveness when the decision maker makes the decision alone. Our results demonstrate that general ethical norms, known to impact decisions having clear ethical content, can also curb accounting aggressiveness when these norms are activated. Furthermore, firm practices such as decision structure can activate norms. These findings are of interest to practitioners and regulators who seek to temper aggressive accounting.

With the introduction of bitcoin, the world got not just a new currency, it also got evidence that a decentralized control structure could work in practice for institutional governance. This Commentary discusses the advantages and disadvantages of centralized and decentralized control structures by examining the features of the bitcoin payment system. We show that while the decentralized nature of the Bitcoin network "democratizes" payments, it is not obvious that the approach increases the equity or efficiency of markets or that the costs of the decentralized control structure won’t outweigh the benefits in the long run.

O presente artigo analisa a estrutura de decisão dos usuários embarcadores de transporte de carga de dois estados brasileiros: Rio Grande do Sul (RS) e Rio de Janeiro (RJ). Modelos de escolha discreta são estimados para analisar a escolha modal de embarcadores de carga a partir de dados de Preferência Declarada. Os resultados evidenciam e quantificam o impacto nas preferências das empresas embarcadoras de carga de minimizar tempos, custos e atraso nos envios de mercadoria. Adicionalmente, é quantificado o impacto de características de confiabilidade, disponibilidade do modo e tipo de serviço na escolha modal. Os resultados mostram a complementariedade dos dados de RJ e RS na estimação dos modelos. Características de custo são percebidas de forma similar por embarcadores de carga de ambos os estados, entretanto características de confiabilidade e de tempo de viagem mostraram ser percebidas de forma diferente. O valor do tempo estimado para RJ foi de R$/t. h 7,43 e para RS de R$/t. h 2,49.

In order to optimize traffic flows and power consumption of network components, various green routing algorithms and protocols have been proposed. These algorithms and protocols apply different techniques to attain their own goals. One of the most important techniques is the sleep-scheduling technique that switches the status of the network components, nodes or links, into active/inactive modes. There are four characteristics affecting the power and performance of communication networks which distinguish green routing algorithms and protocols, namely the sleep-scheduled component, decision structure, network traffic awareness, and quality of service awareness. In this paper, a method is proposed to model, evaluate, and compare the power and performance of the green routing algorithms that use the sleep-scheduling technique. We apply stochastic activity networks to model and analyze the routing algorithms with respect to the network topology. The results obtained from the comparison of the algorithms, validated with the OMNeT++ simulator, can be used by network administrators to make the right decisions.

We formulate and solve a mixed-integer linear program to model an agribusiness two-echelon supply chain between an agro-industrial producer and a wholesaler. The presented model is an equivalent reformulation of a bilevel linear program, using the well known KKT conditions and a disjunction approach on the complementary constraints. It includes a hierarchical decision structure that contemplates an agro-industrial producer in the upper level, who maximizes its profit by delineating the field into management zones and assigning to each one a specific crop rotation plan. In turn, in the lower level is the wholesaler who minimizes the penalization cost of lost sales that could experiment in order to satisfy the demand requirement according to the units provided by the producer. We present a case study to show the relevance of the proposed methodology.

This paper presents a novel methodology for solving multi-objective Computer Aided Molecular Design (CAMD) problems. One of the major challenges in multi-objective CAMD problems is the subjectivity involved in assigning the weighting factors to each property that is optimised. It is difficult to define the relative importance of each property in CAMD problems as target properties that belong to different categories cannot be compared on a common scale. It is crucial to solve this issue as distinct solutions will be generated due to different weighting factors of each property. In this work, a systematic framework that combines CAMD and Analytic Hierarchy Process (AHP) is developed to deal with the ambiguities involved in assessing the relative importance weightings of target properties in multi-objective molecular design problem. Through AHP, molecular design problems can be approached as a hierarchical decision structure and different categories of target properties are combined in the same analysis. Besides, AHP has the ability to generate numerical priorities from the subjective knowledge in evaluating the relative importance of properties using pairwise comparison. A case study on solvent design for oil extraction from palm pressed fibre is solved to illustrate the proposed methodology.

The decision making in the government’s IT project can be problematic to degrade the performance of a team. A leader who confidence in their IT competence likely to make their own decision (hierarchical) and a leader who lacks competencies tends to allow the specialized follower to make a decision. Based on the garbage can model, the present research address how government IT project adopt industry 4.0’s knowledge-based leadership that founded on authentic leadership as measurement. Samples were drawn from 444 IT project participants in Semarang municipal government, Indonesia. The path analysis of the sequential mediated variable indicated team performance has positively affected by authentic leadership, employee (follower) competence, and decision structure. Practically, this study as a pathway to set out industry 4.0’s knowledge-based leadership to the government’s IT project. Our findings contribute to the theoretical discourse of garbage can by augmenting authentic leadership and employee competence as an antecedent predictor of decision structure.

e16273 Background: There is a critical need to develop fast, reliable, and cost-effective methods for the detection of pancreatic cancer (PC) at the earliest stage to maximize the impact of treatment. To-date, early detection of PC is close to impossible due to the location of the pancreas and the absence of characteristic symptoms in early cancer stages. Methods: Our team of clinicians and scientists has established a fast and reliable nanobiosensor technology that comprises iron/iron oxide nanoparticles attached to a protease or arginase activatable FRET pair (tetrakis (4- carboxyphenyl) porphyrin (TCPP) /cyanine 5.5). Arginase and seven proteases (MMP1, 3, and 9, cathepsin B, and E, urokinase plasminogen activator, and neutrophil elastase) were identified using the Gene Expression Omnibus (GEO) web tool based on their different expression pattern in pancreatic cancer patients, pancreatitis and healthy control subjects. Protease/arginase activities were measured in serum after 1h of incubation. Based on this data, a novel engineering approach to improved early stage detection of pancreatic cancer is reported here. This study was funded by American Cancer Society Institutional Research Grant (IRG‐16‐194‐07), awarded to the University of Kansas Medical Center. Results: In our study, 159 patients were enrolled at KU Cancer Center from 2000-2019, 47 with metastatic PC, 36 with localized PC, 26 pancreatitis and 50 healthy controls using KUCC Biospecimen Repository. The problem of early stage detection of pancreatic cancer can be modeled as a multi-class classification problem. Conventional classification approaches provide at most 77% accuracy for the dataset under consideration. A new hierarchical decision structure with specific feature engineering at each step is introduced here to improve the performance of the classifier. The fundamental premise of this information fusion-based framework involves tailoring the statistically most significant features with appropriate weights to execute an efficient binary classification task at each hierarchical step. An overall accuracy of 95% was achieved for the detection of patients with early pancreatic cancer (see table). Conclusions: Because of the dire survival statistics of pancreatic cancer, detection at the earliest possible time by means of a liquid biopsy will offer the greatest benefit. Novel nanobiosensor based protease biomarkers achieved high accuracy in early detection of pancreatic cancers by applying hierarchical decision structure. Our results need validation in a larger cohort. Predicted true class considering the following combination of classification methods: Step1 – kNN*, step2 – kNN*, step3 – RFC* (Accuracy = 94.97%).[Table: see text]

The high efficiency video coding (HEVC) is superior to the previous video coding standards in compression performance, while the computational complexity is introduced simultaneously. The complexity increases mainly due to the novel flexible partitioning scheme that allows the inter-prediction mode partition split via exhaustive rate-distortion optimization (RDO). In this paper, a fast inter-prediction mode decision algorithm is proposed, which contains adaptive threshold determination based on quantization parameter and fast inter-prediction mode partition decision. The proposed algorithm utilizes the edge information of the partition to simplify the RDO and then accelerates coding time of inter-prediction mode decision structure for the original HEVC encoder. The experimental results show that the proposed algorithm can achieve 39.5 % coding time reduction with just 1.97 % bitrate increase on average under random access condition and 35.2 % coding time reduction with just 1.89 % bitrate increase on average under low-delay B condition, compared to the original HEVC encoder.

We formulate the capacity expansion planning as a bilevel optimization to model the hierarchical decision structure involving industrial producers and consumers. The formulation is a mixed-integer bilevel linear program in which the upper level maximizes the profit of a producer and the lower level minimizes the cost paid by markets. The upper-level problem includes mixed-integer variables that establish the expansion plan; the lower level problem is an LP that decides demands assignments. We reformulate the bilevel optimization as a single-level problem using two different approaches: KKT reformulation and duality-based reformulation. We analyze the performance of these reformulations and compare their results with the expansion plans obtained from the traditional single-level formulation. For the solution of large-scale problems, we propose improvements on the duality-based reformulation that allows reducing the number of variables and constraints. The formulations and the solution methods are illustrated with examples from the air separation industry.

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation., Chemical engineering, Optimal selection; Simple additive weighting; Clean technologies; Induction; Decision analysis; P-graph.

Survey-based contingent valuation (CV) techniques are commonly used to value the potential effects of a policy change when market-based valuation of those effects is not possible. The results of these analyses are often intended to inform policy decisions, which are made within the context of formal policymaking institutions. These institutions are typically designed to reduce the large number of potential options for addressing any given policy problem to a binary choice between the continuation of current policy and a single, specified alternative. In this research we develop an approach for conducting CV exercises in a manner consistent with the decision structure typically faced by policymakers. The data generated from this approach allow for an estimate of willingness to pay (WTP) for a defined policy alternative, relative to leaving policy unchanged, which we argue is of direct interest to policymakers. We illustrate our approach within the context of policy governing the storage of used nuclear fuel in the United States. We value the policy option of constructing an interim storage facility relative to continuation of current policy, wherein used nuclear fuel is stored on-site at or near commercial nuclear generating plants. We close the paper with a discussion of the implications for future research and the role of CV in the policymaking process.

International audience; We study the use of a bipolar decision structure called BLF (bipolar leveled framework) in the context of collective decision making where the vote consists in giving factual information about a candidate which the group should accept or reject. A BLF defines the set of possible decision principles that may be used in order to evaluate the admissibility of a given candidate. A decision principle is a rule that relates some observations about the candidate to a given goal that the selection of this candidate may achieve or miss. The decision principles are ordered accordingly to the importance of the goal they support. Oppositions to decision principles are also described in the BLF under the form of observations that contradict the realization of the decision principles. We show how the use of a common BLF may reduce the impact of manipulation strategies in the context of group decision making.

The paper proposes a classification model for human behavioral patterns recognition in which the decisions are provided based on several Support Vector Machines classifiers within a multi-level decision structure. SVMs are suitable for applications in which the input data feature spaces are very large, involving many features. The human behavior recognition is a relevant example of such application. On the other hand, the model proposes several kernels to be applied for SVMs in order to achieve improved performances for the real applications in which the human behavioral patterns recognition is required, as in the case of tele-assistance services.

This paper concerns income- and production taxation in a decentralized fiscal federalism model where the ability to commit differs among member countries. It is assumed that the federal government dictates environmental targets to be implemented at the national level, where the horizontal leader has commitment power vis-a-vis the other country (the follower). The results show how incentives underlying the horizontal leader’s tax policies are influenced by the decision structure.