Crucial Decisions Research Articles

Management of carcinoid heart disease

Carcinoid Heart Disease (CaHD) is defined as the constellation of all cardiac manifestations that occur in patients with carcinoid tumors. Cardiac manifestations are generally due to the paraneoplastic effects of vasoactive substances secreted by carcinoid tumors. These primarily cause cardiac valve dysfunction and resultant heart failure. Successful management of patients with CaHD requires a multidisciplinary team to address both the classical manifestations of carcinoid syndrome, as well as the additional manifestations of cardiac dysfunction. While the cornerstone of medical management for carcinoid syndrome are somatostatin analogs (SSAs), there is no evidence to suggest that the usage of SSAs influences the development or progression of CaHD. Additionally, while liver-directed therapies provide a survival benefit to symptomatic carcinoid syndrome patients with resectable disease, there are conflicting data on the survival benefit of hepatic resection among patients with CaHD. Cardiac surgery in patients with CaHD is a complex undertaking, and is the only definitive treatment for symptom management in CaHD with significant survival benefit for patients in advanced disease states. Two crucial surgical decisions to be made are determining which valve(s) should be replaced, and what prosthetic should be utilized. While challenging in this often medically frail population, cardiac surgery confers a survival benefit and should be pursued in cases of symptomatic CaHD or progressive right ventricular dysfunction.

Managing career plateaus for mid‐career professionals: A process‐driven practice intervention using life‐design counseling

AbstractThis qualitative study explores the impact of life‐design counseling (LDC) on mid‐career professionals facing career plateauing. Seven mid‐career professionals underwent counselling sessions focused on overcoming barriers, evaluating career beliefs, and exploring options. Thematic analysis delves into their experiences, attitudes, and unresolved mid‐career dilemmas. Results indicate a shift from problem‐focused to solution‐based thinking. A two‐wave longitudinal study spanning 4 years confirms counseling's effectiveness in assisting individuals in making crucial career decisions, guiding positive career redirection amidst mid‐career plateauing. Our study specifically explored and analyzed the practical application of LDC intervention in the real‐world counseling scenario for overcoming career plateauing.

Modeling the START transition in the budding yeast cell cycle.

Budding yeast, Saccharomyces cerevisiae, is widely used as a model organism to study the genetics underlying eukaryotic cellular processes and growth critical to cancer development, such as cell division and cell cycle progression. The budding yeast cell cycle is also one of the best-studied dynamical systems owing to its thoroughly resolved genetics. However, the dynamics underlying the crucial cell cycle decision point called the START transition, at which the cell commits to a new round of DNA replication and cell division, are under-studied. The START machinery involves a central cyclin-dependent kinase; cyclins responsible for starting the transition, bud formation, and initiating DNA synthesis; and their transcriptional regulators. However, evidence has shown that the mechanism is more complicated than a simple irreversible transition switch. Activating a key transcription regulator SBF requires the phosphorylation of its inhibitor, Whi5, or an SBF/MBF monomeric component, Swi6, but not necessarily both. Also, the timing and mechanism of the inhibitor Whi5's nuclear export, while important, are not critical for the timing and execution of START. Therefore, there is a need for a consolidated model for the budding yeast START transition, reconciling regulatory and spatial dynamics. We built a detailed mathematical model (START-BYCC) for the START transition in the budding yeast cell cycle based on established molecular interactions and experimental phenotypes. START-BYCC recapitulates the underlying dynamics and correctly emulates key phenotypic traits of ~150 known START mutants, including regulation of size control, localization of inhibitor/transcription factor complexes, and the nutritional effects on size control. Such a detailed mechanistic understanding of the underlying dynamics gets us closer towards deconvoluting the aberrant cellular development in cancer.

A Prospective Comparative Study of Ray Resection versus Amputation through Proximal Phalanx for Nonviable Digits of Upper Limb

Abstract Background Finger amputations aim at preserving function and optimizing cosmesis. The crucial decision here is whether to preserve a stump or to do a ray amputation. The present study aimed to compare the functional outcome and postoperative quality of life after ray amputation or amputation through the proximal phalanx. Methods A prospective study was conducted from January 2019 to June 2020 in patients requiring single-finger amputation through the proximal phalanx or metacarpal. Patients were divided into two groups; Group A: amputation through the proximal phalanx and Group B: ray amputation of the finger. The following functional parameters were assessed: grip strength, hand circumference, palmar volume, and webspace span. The Michigan Hand Outcomes Questionnaire (MHQ) score was employed to score hand function and aesthesis at 6 months in both hands. Results Thirty patients were enrolled. Findings in 26 patients (52 hands) were subjected to further analysis, 12 in Group A and 14 in Group B. Patients in both groups lost grip strength significantly compared with their contralateral normal hands (29.22 ± 14.88 [Group A] and 34.57 ± 19.12 [Group B]); however, it was statistically nonsignificant between the two groups. There was reduced mean palmar circumference in both groups' involved hands, but the decrease in circumference was statistically significant, only for Group B. Group B patients scored better in all the six subscales of the MHQ; however, only the Aesthetics score was significantly superior. The operated hand's webspace span increased significantly with respect to the normal contralateral web by a mean of 4.55 mm. Conclusion The study concluded that both the surgical options for the level of amputation should be discussed with the patients in detail, taking into account their occupational and personal requirements. Our study findings will help to objectively counsel the patients regarding expectations in functional and aesthetic outcomes following either technique.

Depth Image Assisted Aiming for Scoring Goal in Wheeled Soccer Robot

Wheeled soccer robot, as an automatic robot, required to have an advanced decision making system based on information it grasp from its surrounding. One of the most crucial decision making ability is to determine aiming angle when it is scoring goal.This research will enhance the aiming ability for scoring goal by predicting unguarded area of goal. Combination of depth image and RGB image information will be used to predict the position of unguarded space in goal. This position will be converted into aiming angle for the robot. Intel Realsense D435i depth camera will be used to get RGB and depth image simultaneouslyBy using this method, the system is capable to predict unguarded area in all of 60 test points, with 1.3% average error for the predicted coordinate.

Measuring the Impact of Written Corrective Feedback (WCF): The Methodological ins and outs

The interest in the study of written corrective feedback (WCF) has led to the identification of two controversial areas: the impact of the different types of WCF and the methods used in the studies. The present study aims to contribute to the clarification of the latter by examining the methods section of recently published research articles. Its objective is to review the current state of the experimental research designs in WCF studies. For this, eleven published studies were randomly selected and analyzed considering as inclusion criteria that they were empirical studies published in journals indexed in Web of Science and/or Scopus during the last eight years. The findings have been grouped into two categories: research designs and data analysis procedures. Regarding research design, there is a growing concern for ecological validity, the inclusion of delayed post-tests, and the implementation of training on feedback types for students. Concerning data analysis, the choice of a specific formula for error quantification was identified as a crucial decision researchers interested in WCF must make. It is hoped that this study will guide future research, ensuring a solid methodological design in line with current publications in prestigious journals. By following the trends identified in recent prestigious research, it is more likely that research can address the concerns surrounding the effectiveness of different types of WCF.

From gavels to algorithms: The Vidhii Partners GenAI evolution

Learning outcomes This case study exposes students to the process of decision-making given the market uncertainty, evolving shareholder expectations and other variables that executives and managers must consider ensuring business continuity and growth and helps students develop an appreciation of the impact emerging technologies are having on organizational digital transformation. This case study evaluates the strategic considerations for whitespace digital solutions into existing organizational operations, balancing innovation with core operating principles; exposes the students to the drivers of fostering a culture of innovation and employee buy-in during strategic digital transformation initiatives; and evaluates and appreciates the role of key stakeholders, such as customers, employees and executive management, in shaping a successful digital transformation strategy and promoting sustained business growth. Case overview/synopsis Vidhii Partners, a leading Indian law firm, grapples with the burgeoning demand for legal services in a rapidly evolving market. Traditional methods may struggle to keep pace, prompting Vikram Wadehra, a partner, to champion the adoption of Generative artificial intelligence (GenAI) technology. Vidhii Partners embarks on the development of two GenAI tools: VidAI, an AI-powered chatbot designed to democratize access to legal information, and VidAI Pro, a business-to-business offering aimed at streamlining legal research and drafting. Wadehra envisions these tools not only enhancing efficiency but also fostering a culture of legal awareness and generating new business opportunities. However, crucial decisions remain. Can Vidhii Partners bridge the potential gap between innovation and the firm’s established practices? How can they effectively integrate GenAI into their operations while ensuring user trust and employee buy-in? This case study presents a rich opportunity to explore the challenges and opportunities associated with digital transformation in the legal sector. Students will grapple with themes such as navigating market uncertainty, fostering a culture of innovation within a traditional organization and developing effective strategies for stakeholder management during change initiatives. Through the lens of Vidhii Partners’ GenAI journey, students gain valuable insights into the complexities of embracing disruptive technologies within a dynamic business landscape. Complexity academic level This case study is designed for masters level (MBA). Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Male attractiveness is subjective to exposure to males of different attractiveness in fruit flies

Mate choice is a crucial decision in any animal. In terms of fitness, the best mate is the one that leads to the most abundant and productive offspring. Pairing with a low-quality mate would reduce fitness, generating selection for accurate and subtle mate choice in all animal species. Hence, mate choice is expected to be highly context dependent, and should depend on other potential options. For instance, a medium-quality male can constitute the best option when all other males are in poorer condition, but not when there are better-quality males available. Therefore, animals are predicted to gather information about their social context and adapt their mate choice to it. Here, we report on experiments in which we manipulated the social environment of females of Drosophila melanogaster and found that after encountering a high or a low-quality male, they take more or less time to accept copulation with another male, suggesting that females adapt their mating strategy to their social context. We also report on a similar effect in D. biarmiceps. Thus, male attractiveness appears to depend on the quality of recently met males, suggesting that male attractiveness is subjective, indicating plastic and context dependent mate choice.

Selection of Optimal Bending Types in Die Process of L-Shaped Panels Considering the Springback Behaviours

In die designing, one of the most crucial decisions is selecting the appropriate die process required to produce panels. The challenge for die designer is to comprehend the behaviour of the panel material in relation to the die processes. If the die process is not correctly determined before die fabrication, severe panel spring back may occur, which can become difficult and require additional time and effort to rectify. This paper presents research aimed at understanding the behaviour of different types of bending process combinations concerning the spring back occurrence during the stamping process of an L-shaped design intent panel. Using simulation and experiment, three types of materials with different tensile properties of 270 MPa, 440 MPa and 590 MPa, but the same thickness of 1.8 mm, were selected. The results were compared, analysed and optimized by using Response Surface Methodology (RSM) method. The results demonstrate that one-process bending with full bending (compress type) approach, is the most suitable for the three materials type L-shaped panel. The study provides guidelines for die design engineers, industrial practitioners and researchers to decide on the best approach for die process decisions, when dealing with specific panels properties, shape and thickness.

Cerebrospinal fluid cytology: A theragnostic and prognostic tool- Experience from a tertiary cancer care centre in central India

: CSF examination is the gold standard for diagnosing leptomeningeal metastases. Malignant cells may access the subarachnoid space by hematogenous dissemination, spreading directly from parenchymal brain lesions, or spreading along the spinal cord or cranial nerves. Cytological detection of malignant cells in CSF is still a crucial decision point for systemic and intrathecal chemotherapy, and it has prognostic significance even with improvements in biochemical analysis and CNS imaging. Over the course of two years, a total of 397 patients—regardless of gender and age—were investigated. There were sixty-four cases with involvement of the central nervous system documented, comprising 42 hematologic malignancies and 22 solid tumors. Cell centrifugation (Cytospin) method were used to prepare smears and cell blocks where prepared, as feasible and indicated. : A cytological analysis of the cerebrospinal fluid showed that metastatic tumors were much more prevalent than primary central nervous system tumours. Lung tumors were the most frequent primary site in our analysis, with breast, gastrointestinal, and female genital tract malignancies following closely behind. CNS involvement has been reported in patients with Lymphoma (DLBCL) and Leukemia (most commonly ALL, but also AML and JMML) among hematological malignancies. Two cases of Retinoblastoma and one each of Medulloblastoma and an Atypical teratoid/rhabdoid tumor were observed in the pediatric age group.: Cerebrospinal fluid cytology is a simple and useful method, used as a primary diagnostic method in the evaluation of leptomeningeal metastases. This helps in the early detection of secondary CNS lesions and allows for timely intervention, prognosis, and prediction of overall survival.

Identifying and hierarchizing factors that affect the choice of transport routes in land ferry transport chains

Motives: The choice of a transport route has long been the subject of research because it is regarded as one of the most crucial decisions in transport organizations. However, there is a lack of studies addressing this issue in the context of land-ferry supply chains, where two transport modes have to be integrated. Therefore, analyses of multimodal transport should involve a holistic approach to account not only for seaborne transport, but also for the land leg before and after the ferry trip. This knowledge gap should be urgently addressed due to the importance of land-ferry supply chains in the transport system. Aim: The aim of this study was to identify and hierarchize factors that determine the choice of a multimodal transport route combining ferry and road transport. Results: The factors that determine the choice of ferry routes were identified and hierarchized according to their importance as primary, supplementary, and additional routes. The study demonstrated that the cost and time of transport are the main determinants in the decision-making process, with time being marginally more important than cost.

Human Resource Management Factors in Financial Analysis of Securities Companies

The outcomes of financial analysis serve as the basis for crucial decision-making in business operations. However, many small-scale securities companies tend to overlook this aspect. This study aims to investigate the relationship between financial analysis activities and business outcomes of small-scale securities companies in Vietnam. Data were collected from 92 respondents working in 27 small-scale securities companies (with owner equity of less than 250 billion Vietnamese Dong), holding positions such as management (directors, assistant directors, financial directors, chief accountants) and staff (financial and accounting personnel) involved in financial and accounting operations of Vietnamese securities companies. Sampling was conducted selectively from September 2023 to December 2023. Utilizing quantitative research, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method was employed using SPSS 20 and AMOS 20 software. Results indicate that Financial Analysis Method (MAF) and Financial Analysis Content (variable FAC) positively influence financial analysis activities of small securities companies. Recommendations to enhance securities companies' business outcomes include (i) employing the EVA technique in financial analysis, (ii) improving management capabilities in financial report analysis, (iii) enhancing the skills of accounting and financial staff, and (iv) applying and operating professional ethical principles.

Traffic Sign Board Recognition and Voice Alert System using CNN

Street to guarantee a secure and efficient flow of traffic. Street accidents sometimes occur on account of carelessness in reading traffic signs incorrectly. The suggested framework aids in recognizing the stop sign and giving a voice warning to the motorist for the speaker to make their point, and crucial decisions. The proposed framework is prepared Using a Convolutional Neural Network (CNN), which aids with the recognition and arranging of rush hour congestion sign pictures. To increase precision, a number are of classes generated and characterized on a particular dataset. Utilized was the German Traffic Sign Benchmarks Dataset, which includes 51,900 pictures of road signage in 43 classifications. Around 98.52 percent during execution was precise. After the framework recognizes the sign, the driver is informed through a voice alarm issued through the speaker. The suggested framework also includes a section where drivers are warned about nearby traffic signs so they can keep track of which laws to follow while on a highway. The system’s goal is to protect the driver, passengers, and pedestrians from harm.

Multi-Objective Optimization Methods for Train Load Planning in Seaport Container Terminals

In this paper we study the planning problem of loading trains in seaport container terminals, which consists in assigning containers to the wagon slots. The train load can be realized considering different objectives, in this work we focus on two very important objectives for terminal managers, that are the minimization of the number of reshuffles in the storage area and the minimization of the total distance covered by the handling equipment between the storage area and the wagons. To address this problem, some multi-objective methods are investigated in the paper, based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint approach and the lexicographic algorithm, allowing to find different sets of efficient solutions to be provided to train planners. The experimental tests, devised to compare the proposed multi-objective methods, show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint method integrated with a lexicographic approach is the most efficient choice since it allows to obtain a higher number of efficient solutions in shorter computational times. This method can be applied in decision support systems for train planners in order to allow them to decide the train load in an efficient way. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The automation and the process optimization in seaport container terminals is a relevant issue for terminal managers in order to face the strong competitiveness in the logistics and shipping sector and the new emerging problems due to the presence of mega vessels. Among the different operations to be optimally managed in a terminal, the loading of trains is becoming more and more significant, also due to the increase of freight traffic by rail, seen as a cheaper and more sustainable option compared with road transport. The optimal assignment of containers present in a storage area to the wagon slots is a crucial decision in order to reduce operational times and costs in a container terminal. In this paper, we address the train load planning problem considering two important objectives to be minimized, i.e. the number of reshuffles and the distance covered by the handling equipment. The multi-objective methods proposed in the paper are devised to find different efficient solutions in short computational times. The proposed algorithms are intended to be implemented within a decision support tool in which the solutions found are shown to the train planner who chooses the preferred option depending on personal evaluations and on the basis of information coming in real time.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

Mate choice in the brain: species differ in how male traits 'turn on' gene expression in female brains.

Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioural data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioural context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic neurogenomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting evolutionary tinkering with these conserved molecular processes to generatedivergent mate preferences. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.

Modelling Asymmetric Volatility in the Crypto Currency and its Dynamic Relationship with Stock Market

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

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.

Enhancing multi-facility manufacturing resilience through integrated product and supply chain network design

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework’s capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience.

Uncertainty assessment in unsupervised machine-learning methods for deepwater channel seismic facies using outcrop-derived 3D models and synthetic seismic data

Unsupervised machine-learning (ML) techniques have been widely applied to analyze seismic reflection data, including the identification of seismic facies and structural features. However, interpreting the resulting clusters often relies on geoscientists’ expertise, necessitating a robustness assessment of these methods. To evaluate their reliability, synthetic data generated from an actual outcrop model are used to demonstrate how two unsupervised methods, self-organizing maps (SOMs) and generative topographic maps (GTMs), cluster deepwater channel-related seismic facies and then measure the associated error. Six seismic attributes, comprising root-mean-square amplitude, instantaneous envelope, peak magnitude, and spectral decomposition frequencies at 20, 40, and 55 Hz, served as input variables. Geobodies are assigned to each cluster formed, and the error in facies clustering is quantified by comparing the actual 3D model with the facies grouped by ML methods on a voxel-by-voxel basis. This allowed for error quantification and the computation of metrics such as F1 score and accuracy through correlation matrices. Key findings reveal that (1) GTM and SOM exhibit similar performance, with a clustering configuration of 81 for GTM slightly outperforming others. (2) Error rates are approximately 2% for the predominant facies (background shale) but significantly higher for individual channel-related facies, suggesting that channel clusters might represent multiple facies. (3) Resolution and imbalanced data distribution impact seismic facies predictability, resulting in nonuniqueness in cluster generation. (4) Using synthetic seismic data proved valuable for experimenting with different unsupervised MLs, highlighting the need for assessing uncertainty in these methods, given their implications for crucial economic decisions reliant on reservoir interpretation, modeling, and volumetric estimations.