Decision Logic Research Articles

Development and Implementation of Clinical Decision Algorithms for Oncology.

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format.

Predicting the Performance of Ensemble Classification Using Conditional Joint Probability

In many machine learning applications, there are many scenarios when performance is not satisfactory by single classifiers. In this case, an ensemble classification is constructed using several weak base learners to achieve satisfactory performance. Unluckily, the construction of the ensemble classification is empirical, i.e., to try an ensemble classification and if performance is not satisfactory then discard it. In this paper, a challenging analytical problem of the estimation of ensemble classification using the prediction performance of the base learners is considered. The proposed formulation is aimed at estimating the performance of ensemble classification without physically developing it, and it is derived from the perspective of probability theory by manipulating the decision probabilities of the base learners. For this purpose, the output of a base learner (which is either true positive, true negative, false positive, or false negative) is considered as a random variable. Then, the effects of logical disjunction-based and majority voting-based decision combination strategies are analyzed from the perspective of conditional joint probability. To evaluate the forecasted performance of ensemble classifier by the proposed methodology, publicly available standard datasets have been employed. The results show the effectiveness of the derived formulations to estimate the performance of ensemble classification. In addition to this, the theoretical and experimental results show that the logical disjunction-based decision outperforms majority voting in imbalanced datasets and cost-sensitive scenarios.

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues similar to classical DL systems. There is an urgent need for ways to test their correctness and security. However, QNN systems differ significantly from traditional quantum software and classical DL systems, posing critical challenges for QNN testing. These challenges include the inapplicability of traditional quantum software testing methods to QNN systems due to differences in programming paradigms and decision logic representations, the dependence of quantum test sample generation on perturbation operators, and the absence of effective information in quantum neurons. In this paper, we propose QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems. We design a quantum entanglement adequacy criterion to quantify the entanglement acquired by the input quantum states from the QNN system, along with two similarity metrics to measure the proximity of generated quantum adversarial examples to the original inputs. Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement adequacy and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples. Experimental results demonstrate that QuanTest possesses the capability to capture erroneous behaviors in QNN systems (generating 67.48%-96.05% more high-quality test samples than the random noise under the same perturbation size constraints). The entanglement-guided approach proves effective in adversarial testing, generating more adversarial examples (maximum increase reached 21.32%).

Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

Growth Configuration of Invention-based Entrepreneurial Start-up

This research studies an important question of configurational change of entrepreneurial growth. More precisely, it explores the question of post-start-up configurations in invention-based ventures and aims to improve our understanding of how and why aspects of start-ups transition from one stage to another. A case study method enables us to see what happens when a start-up tries to move from product invention to its commercialization stage, why specific actions occur, and how they impact the venture. Our findings show the importance of enabling the organizational emergence process through two complementary mechanisms, changes in entrepreneurs’ sense- and decision-making logic and using temporal or similar pressure as a motivational tool.

Right, Wrong, Logic and Mentality

This paper explores the nuanced relationship between right, wrong, logic, and mentality in ethical decision-making. It argues that ethical judgments are deeply subjective and context-dependent, influenced by individual and collective experiences, cultures, and cognitive frameworks. The paper introduces the metaphor of mentality as the fabric of the mind and logic as Lego pieces, illustrating how cognitive flexibility and logical structure interact to shape our moral understanding. While advocating for a fluid and adaptable approach to morality, the paper asserts the absoluteness of wrongs when actions cause significant harm. The "Twelve Gates of Wrong" framework is proposed to systematically evaluate ethical decisions, emphasizing harm avoidance. This model integrates traditional ethical theoriesvirtue ethics, deontology, and consequentialismwhile addressing the complexities of modern moral dilemmas. By balancing cognitive flexibility with logical integrity, the paper offers a comprehensive roadmap for ethical reflection and decision-making, urging a principled and empathetic approach to minimize harm and foster societal well-being.

Entrepreneur's social identity, decision logic, and resource bootstrapping behaviors: An empirical study of the fsQCA method

While resource bootstrapping is widely employed by novice entrepreneurs,and social identities and decision logic are studied as important antecedents of entrepreneurs' adoption of resource bootstrapping behaviors, both scholars and practitioners have but a hazy understanding on the configuration effect of entrepreneur's social identities and decision logic on resource bootstrapping behaviors. In this paper, we combined the necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) to explore the configuration effects of entrepreneur's social identity and decision logic on resource bootstrapping behaviors. Analysis of 411 questionnaire date acquired from novice entrepreneurs, we found that none of single antecedent condition of both the entrepreneur's social identity and decision logic constitute the necessary condition of novice entrepreneurs' high resource bootstrapping behaviors. We also found a substitutive relationship between Darwinian social identity and communitary social identity that will lead the novice entrepreneurs with high dual effectual-causal decision logic to choose high joint utilization bootstrapping, and a complementary relationship between the three social identities of novice entrepreneurs such that those with high dual effectual-causal decision logic prefer different resource bootstrapping behaviors because of different social identities.Furthermore, we found that the core and edge conditions in the decision logic play different roles in the resource bootstrapping behaviors of novice entrepreneurs, with the core decision logic conditions having a greater impact on the resource bootstrapping behaviors. These findings deepen our understanding of the causal complexity among entrepreneur's social identity, decision logic and resource bootstrapping behaviors. It also provides theoretical guidance for effective resource integration for novice entrepreneurs facing resource constraints.

Towards Achieving Substantive Justice: The Importance of Extending the Time Limit for Resolving Presidential Election Disputes

Presidential election dispute resolution (PHPU) is complicated because its scope covers the entire territory of Indonesia and its application to Indonesian citizens in various parts of the world. The PHPU dispute resolution process must also guarantee maximum protection of constitutional rights. This research uses normative research methods using secondary data sources. This aims to assess the urgency of extending the deadline for resolving presidential election disputes, which is currently 14 working days from the date of submission. Research sculpture approach and conceptual approach. These findings illustrate the need for changes to legislation to extend the time limit for resolving PHPU disputes beyond the existing 14-day limit. In practice, this time period does not provide maximum opportunity for the presentation of evidence. Historical analysis shows that from the 2004 presidential election to 2024, not a single PHPU demand has been granted, mainly because the demand cannot be proven. The author emphasizes that a time limit that is too early does not guarantee substantive justice, which includes not only fair and accurate decision-making, so rational considerations are needed that produce rational and logical decisions and a process that allows each party to present their arguments comprehensively and obtain fair treatment in the judicial process.

A Decision Logic for the Reliability Assessment and Interpretation of Vitellogenin Measurements.

A Decision Logic for the Reliability Assessment and Interpretation of Vitellogenin Measurements.

Drought, water management, and agricultural livelihoods: Understanding human-ecological system management and livelihood strategies of farmer's in rural California

Mitigating impact between agricultural livelihoods and water conservation efforts in the face of significant drought requires a sophisticated understanding of policy efforts enacted to manage water supply and the logic of human livelihood decision-making. This case study extends literature on human-water decision-making in agricultural areas by using a political ecology framework to understand how and why farmers facing significant water shortages make livelihood decisions, and how such decisions are affected by broader socio-political contexts. Specifically, we focus on farmers' livelihood strategies in the Central San Joaquin Valley of California. Here, the effects of extreme and persistent drought have resulted in a resource management governance structure that forces farmer decision-making within the narrow bounds of the newly emerging California water policy. We argue that government public policy intervention has created a potential system of “big winners and big losers,” leading to three divergent strategies farmers adopt: Nimbility, Abandonment, and Policy Engagement. These findings raise questions about how policy interventions shape livelihood strategies of farmers in the Tulare Lake Basin and by extension other areas where public policy intervention is emerging to respond to decreased water supply resources.

The Influence of Intuitive-Analytical Cognitive Styles on Entrepreneurial Decision-Making Logic: Moderated by Environmental Uncertainty

The Influence of Intuitive-Analytical Cognitive Styles on Entrepreneurial Decision-Making Logic: Moderated by Environmental Uncertainty

Striking the balance: Configurations of causation and effectuation principles for SME performance

Causation and effectuation are two fundamental decision-making logics that managers use for crucial firm strategic decisions. However, existing research has yet to agree on the relationship between the two logics, supporting both the substitution and complementarity of causation and effectuation in influencing firm performance. This leaves us with a puzzle: How do causation and effectuation combine in balance to improve firm performance? To address the gap, we utilize a fuzzy set qualitative comparative analysis (fsQCA) with data collected from 344 small to medium-sized enterprises (SMEs) in China to uncover the dynamic relationships between the two logics. Our findings indicate that causation or effectuation alone is insufficient to achieve superior firm performance. By distinguishing between four dimensions of effectuation, we identify three types of configurations for high performance: (1) causation with promotion-focused effectuation principles; (2) causation with prevention-focused effectuation principles; (3) causation with hybrid-focused effectuation principles. More importantly, we find that the effectiveness of the configurations depends on the firm development stage. Our findings provide SMEs with practical insights into how to effectively choose their decision-making logic when faced with different firm growth challenges.

Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials.

A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials. A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians. The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types. The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.

Специфика перевода рекламных слоганов в Рунете

Marketing of goods and services includes active advertising activities, which are implemented primarily in the web space. One of his pragmatically effective tools is an advertising slogan, a formulaic means of marketing communication, which is studied in the paradigm of modern linguistics in various aspects. This article is devoted to solving a translatological problem, i.e. identifying the translation specifics of advertising slogans in the web space. The material of this study is the texts of advertising slogans in English, functioning in the Internet space, and their translation into Russian. During the analysis, advertising slogans involved in the global advertising discourse in the period from 1993 to 2023 were used. The authors use methods of pragmalinguistic analysis, as well as techniques of interpretative analysis with elements of content and intent analysis. The peculiarities of the linguistic composition of the original slogans in English are revealed, due to the correlation of the slogan with a group of goods. The main translation solutions in the translation of slogans are transcription, adaptation, and lexical transformation. The pragmatic effect of the texts of the original slogans and their translations in the focus of marketing communication is determined and described. The logic of translation decisions and the factors influencing the preservation of slogans in the original language in the target linguaculture are established.

Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study

ABSTRACT The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news, rumors surrounding events or companies, market sentiments, and governmental policies, can significantly impact stock prices. Nevertheless, the precision of current methods remained insufficient until the adoption of artificial neural network architectures like long short-term memory (LSTM). The aim of this study is to create a precise AI-driven platform tailored for both the Indian and international stock markets. This platform is designed to assist retail investors in navigating digital environments by employing various LSTM algorithms. Its primary goals include predicting stock price fluctuations, pinpointing potential investment prospects, and refining trading strategies. The application aims to leverage advanced LSTM algorithms to analyze historical market data, recognize patterns, and provide real-time insights. It will take past price and process it through LSTM algorithms to take a logical decision. In the quest to broaden retail participation in the capital markets, the effort is to develop an application for novice investors who either have no time in research or are the victims of financial mis-selling and enable them to leverage the technology to their advantage.

Fuzzy Methods in Entrepreneurship Research

The use of fuzzy theory has grown across many academic disciplines, including business, management and economics. In entrepreneurship research, methods such as Fuzzy-set Qualitative Comparative Analysis (fsQCA), fuzzy analytic hierarchy process (fAHP), fuzzy Delphi, fuzzy logic and fuzzy Decision Making Trial and Evaluation Laboratory are increasingly used. The emerging nature of these methods necessitates a synthesis of extant research in this field. Therefore, this research study aims to provide a comprehensive review of the growth of fuzzy methods in entrepreneurship via a bibliometric analysis. Using Scopus as the main data source, a systematic approach for data retrieval, screening and analysis was used. The results include a temporal analysis of citations, and analyses of influence and productivity. Furthermore, using a scientific mapping approach, the main thematic clusters and emerging themes are unpacked. Dominant themes include fsQCA, entrepreneurial orientation, female entrepreneurship, sustainability, entrepreneurial intention and innovation. Emerging themes include COVID-19, sports, ecosystems and the configurational approach. This article concludes with suggestions for future research.

Application of Fuzzy Decision Support Systems in Risk Assessment of Southeast Asian Labor Market

The labor market in Southeast Asia has several complicated issues, including the quick changes in the economy, the various behaviors of employees, and the shifting regulatory settings. To foster sustainable regional development and make decisions based on accurate information, a comprehensive risk assessment is important. Conventional methodologies, on the other hand, often fail to reflect the multifaceted personality of issues about the labor market. In light of recent economic changes and the complexity of applicable policies, this paper recommends a fuzzy decision support system to enhance risk assessment (FDSS-ERA) that could enhance risk assessment in Southeast Asian labor markets. The model methodically assesses hazards, using a mixture of fuzzy logic sense and decision assistance, incorporating changes in employment trends and demographics. The results highlight fuzzy logic’s role in decision-making for effective risk management and policy interventions, showing improved risk comprehension. While educated policy choice making can lead to equitable development in Southeast Asia’s labor work marketplace, FDSS is a potential method. In conclusion, to overcome the many obstacles that stand in the way of chance assessment in the labor market in Southeast Asia, FDSS provides a powerful strategy. Because it offers more advanced analytical tools, this research assists decision-makers in the region in developing more effective guidelines, proactively reducing risks, and achieving projects that promote sustainable development.

Unveiling the synergy: entrepreneurial effectuation and alliance-driven SME diversification

PurposeInternationalisation is a complex and uncertain process for small and medium-sized enterprises (SMEs), characterised by challenges such as limited resources, lack of international experience and uncertainty in foreign markets. This study aims to explore the impact of entrepreneurial effectuation on the diversification of products and market expansion. Additionally, it examines the mediating role of alliance capabilities in this relationship.Design/methodology/approachThis study developed five hypotheses based on an extensive and relevant literature review. Data were collected using nonprobability judgemental and snowball sampling techniques from 202 software exporting firms in Bangladesh to measure the relationship. Data collected from the survey were then analysed using partial least squares-structural equation modelling.FindingsThe results of this study show that all five hypotheses developed in this study are supported. This study found a positive impact of entrepreneurial effectuation on product and market diversification. Study results also show that alliance capabilities mediate the relationship between entrepreneurial effectuation and product and market diversification.Research limitations/implicationsIn the face of internationalisation uncertainties, SMEs use effectual decision-making logic and form strategic alliances to enhance their competitive positions. This research contributes to understanding how SMEs use effectuation in navigating international markets and expanding their product portfolios and market reach.Originality/valueThis study pioneers a mediation approach to explore the relationship between entrepreneurial effectuation, alliance capabilities and diversification in SME internationalisation. Examining the interplay of decision-making logic and collaborative ventures, this study offers insights into the complexities of SME internationalisation in uncertain environments.

Evaluating The Efficiency of Contractor's Guidelines in Improving the Success of Construction Projects

In the construction industry, the guidelines of contractors play a crucial role in improving the success of construction projects. Contractors have significant responsibilities in a construction project, including measurement, recording, and supervision of dimensions and work quality. In general terms, a contractor is responsible for planning, leading, executing, supervising and inspecting a building construction project. The responsibility extends from the beginning to the end of the project, regardless of its scope. Overall contractors are responsible for fulfilling the contract Which makes them liable. These specialties enable contractors to gather necessary information accurately and with high precision, contributing to sound decision-making in the project. Suitable guidelines for contractors involve standard methods and processes for measurements, data recording, reporting, and communication with other project team members. These guidelines enhance efficiency, quality, and project execution speed, facilitating coordination among team members. By adhering to appropriate guidelines, contractors will be able to collect and analyze required information accurately and make logical and effective decisions to improve project performance. Ultimately, these improvements will lead to cost reduction, time saving, and enhancement of the final project quality. Factors such as sales history, quality policies or guidelines, adequacy of human resources and factory resources, completion of past project waste disposal, and company image are the most influential factos in project success.

Computational modeling of aging-related gene networks: a review

The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity.