Innovative Framework Research Articles

Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow

The identification of emerging technologies (ETs) is pivotal for advancing technological innovation. However, current methods fail to sufficiently clarify ETs' innovation mechanisms and lack a consistent perspective to integrate the five attributes proposed by Rotolo. This paper presents an innovative term-level framework to identify and comprehend ETs through the perspective of technological knowledge flow (TKF). By dissecting TKF comprehensively, encompassing aspects of knowledge absorption, growth, and diffusion, we construct and explicate multidimensional indicators reflective of ETs' attributes, including relatively rapid growth, radical novelty, coherence, prominent impact, as well as uncertainty and ambiguity. Through the analysis of digital medical patent dataset, our framework proves effective in assessing emergent scores and pinpointing ETs with specificity at the term level, clarifying their technological components and efficacy. This is beneficial for technology developers to overcome technical difficulties and strategic decision makers to manage IP for competitive advantage.

A Data-Driven Innovation Model of Big Data Digital Learning and Its Empirical Study

Digital learning is the use of telecommunication technology to deliver information for education and training. As the increased acceleration of the propagation speed of the web, a lot of data collected by automated or semi-automated way. The 4s (Volume, Velocity, Variety and Veracity) of big data increase the challenge to extract useful value via systemic framework. This study aims to construct the data model of big data digital learning. Based on the digital learning data, data-driven innovation framework was proposed to identify data form and collect data. Bayesian network was proposed to capture learning model to extract user experience of students to enhance learning efficiency. Empirical study was conducted on a university to validate the proposed approach. The results have been implemented to support the strategies to improve student learning outcomes and competitiveness.

Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework’s integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy.

A novel hypoglycemia alarm framework for type 2 diabetes with high glycemic variability.

In patients with type 2 diabetes (T2D), accurate prediction of hypoglycemic events is crucial for maintaining glycemic control and reducing their frequency. However, individuals with high blood glucose variability experience significant fluctuations over time, posing a challenge for early warning models that rely on static features. This article proposes a novel hypoglycemia early alarm framework based on dynamic feature selection. The framework incorporates domain knowledge and introduces multi-scale blood glucose features, including predicted values, essential for early warnings. To address the complexity of the feature matrix, a dynamic feature selection mechanism (Relief-SVM-RFE) is designed to effectively eliminate redundancy. Furthermore, the framework employs online updates for the random forest model, enhancing the learning of more relevant features. The effectiveness of the framework was evaluated using a clinical dataset. For T2D patients with a high coefficient of variation (CV), the framework achieved a sensitivity of 81.15% and specificity of 98.14%, accurately predicting most hypoglycemic events. Notably, the proposed method outperformed other existing approaches. These results indicate the feasibility of anticipating hypoglycemic events in T2D patients with high CV using this innovative framework.

Unclaimed Land Shares: Trends in the Development of Legislative Regulation

The article discusses current aspects of recognizing land shares from agricultural lands as unclaimed and turning them into municipal property. The importance of the topic is due to the importance of agricultural lands, which act as a guarantee for the development of agriculture in the country, as one of the strategic directions of the state in the field of food security in the Russian Federation. Recognizing land shares as unclaimed and registering them as municipal property is one of the important legally established ways of putting agricultural land into circulation against the backdrop of a decrease in the number of arable land areas in the state. Over the last decade, the state has been concerned about this situation and has been taking various measures aimed at preserving and increasing agricultural land. At the same time, with state regulation and the desire to preserve agricultural lands, it is necessary to respect the rights and legitimate interests of land shareholders as the weakest party in such land legal relations, take into account the vulnerability of such citizens due to their legal ignorance and lack of education on the part of local governments in the matter registration of rights to land shares. At the same time, local government bodies should strictly comply with the requirements of the law regulating the procedure for recognizing land shares as unclaimed, in order to guarantee constitutional protection of private property rights to land shares, as well as reduce the number of litigations in which the courts establish gross violations of the procedure for approving lists of land plots. It is especially important that municipalities comply with this procedure within the framework of the legal innovations which provided local governments with the right to acquire municipal property after January 1, 2025.

Innovation Framework for Financial Excellence: Banks, FinTech and the Regulators

Financial innovations like peer-to-peer payment and digital currency, mostly introduced by FinTech companies, are perceived as disruptions to Financial Institutions (FIs). For banks, transformation to digital banking helps initially but quickly intensifies the challenge to change with speed and scale given the diversity of such disruptions. Who are banks competing with? What will banking be competing on? Interesting questions like these, which were not even relevant a decade ago, are pushing banks to innovate about the products, services, branches, and operations, if not the very core model of banking. For example, applying pay cuts into the profit margin but emerging innovation of Blockchain raises the questions of whether banks should accept Bitcoin or re-invent the trade finance service. Consumers do seem to benefit initially as competition encourages banks to lower the service fees and design new products in the quest of enhancing customer experience. Nevertheless, all come with a price, for instance digital frontier becomes a fertile ground for fraud and Regulators may jump in at calculated moment. This helps to level the competition or curtail intrusion into the space of customer privacy. Reluctantly, however, regulators, FinTech companies and banks now enter the impossible trinity, or what economists call the trilemma, which is the core issue our research focuses on. This paper develops an empirical framework for banks to embrace disruptive innovation from FinTech start-ups and associated legal or regulatory changes and to create competitive advantage through strategic use of enterprise data that are originated in the bank or acquired by the bank. Lab works in multiple North American banks have been first anonymized to protect the interest of all engaged parties and then harmonized to mature the empirical models based on first-hand evidence in three directions: Customer Experience Enhancement, Digital Product Design and Fraud Management. Foundation of the framework, however, builds on research works in digital banking, social engineering, data science, decision modeling, fraud detection algorithms, digital currency, and regulations adaptive to societal transformations enabled by application of artificial intelligence and Internet of Things. Rather than locking in rigid industry trend, often heavily depend on hence limited by technology, the framework focuses on translating theory or strategy into product and actionable steps that have direct and measurable impact. Data is the key enabler. Data monetization is a good example. Results from Banking Labs work are the proof, and the value will be even more pronounced as expected results come by to validate and optimize the framework and its implementation.

Teaching Innovation and Practice of Basic Computer Courses in Local Colleges and Universities

The rapid advancement of information technology poses significant pedagogical challenges for basic computer courses in local colleges and universities. This study proposes a comprehensive framework for instructional innovation, encompassing the restructuring of course content, enrichment of teaching resources, reformulation of instructional methods, and the establishment of feedback channels, as well as a comprehensive framework for optimizing assessment mechanisms. The implementation of this framework has yielded remarkable outcomes, including heightened classroom engagement and academic rigor as well as an overall enhancement in students’ critical thinking and practical skills. These transformative changes have propelled basic computer courses in local higher education institutions to a new stage while offering valuable insights for future pedagogical reforms.

Outlines of the Science and Technology System: A Look into the Past for Forming Policy for the Development of the Next Generation Ecosystem. Part 1

Overcoming stagnation in the scientific and technological system or, more broadly, in the national innovation ecosystem, which can be described as “the right researchers in a faulty system”, implies, as a start, an analysis of the existing structure, status and roles of its key elements. Strategic and framework planning as a way to allocate resources and determine actions to achieve national or regional goals can be undermined if national policies on the legal and regulatory environment, improvements in physical and institutional infrastructure, support programs and funding mechanisms do not provide a push in the right direction or, moreover, it does not develop and implement a comprehensive framework for innovation. By analyzing the traditional aspects of the state and recent changes in the country’s scientific and educational system and relying on independent opinions and a survey of primary sources conducted in 2022, in the context of international experience, we identify its current values and the models that produce these values in a mixed economy.

Ecological risk assessment of a coastal area using multi-source remote sensing images and in-situ sample data

Healthy coastal area have high ecological service value. However, because of the uncontrolled utilization of marine resources and the discharge of pollutants, the ecological environments of many coastal areas are faced with ecological risks, such as oil spills and Enteromorpha blooms. Therefore, it is of great significance to monitor and assess the ecological risks in coastal areas. The remote sensing technique can be used to obtain frequent and wide-coverage images of the areas of interest, and has become an effective tool to monitor the ecological risks occurring in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative ecological risk assessment framework was established using multi-source remote sensing images and in-situ sample data, which fully considers the potential harm of oil spills and Enteromorpha blooms on the ecology of the bay. Specifically, the oil spill risk for Jiaozhou Bay from 2017 to 2019 was identified by a deep learning based method from synthetic aperture radar (SAR) images, and the Enteromorpha bloom risk for the corresponding years was identified by a floating algae index (FAI) threshold method from multi-source optical images. Then, using habitat information and ground observation data about the marine species in the bay, environmental vulnerability maps were generated. Finally, a disaster-causing factors and disaster-bearing factors weighted risk assessment model was developed. The risk source monitoring results show that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, while the outbreak intensity of Enteromorpha blooms generally became higher. The ecological risk assessment results reveal that the high risk areas of Jiaozhou Bay are mainly around the transportation regions and the ports, with a higher risk degree in the eastern and southern parts. To support our work, the well-trained oil spill detection network model and some information about the oil spill events that have occurred in Jiaozhou Bay are provided in this paper.

Oil and community development in Gabon: The case of Gamba

The article examines the role of an oil company in community development in Gamba in Gabon, Africa's fifth-largest oil producer. It aims to contribute to the literature on the impact of oil corporations’ social initiatives in developing countries. The study builds an innovative theoretical framework from on-the-ground data using the grounded theory method to explain and evaluate the scope of the social investments undertaken by the oil company. The research revealed three main dimensions around which the oil company contributed to community development in Gamba: infrastructure, health and education. The data show that the extent to which it invested in community initiatives was determined by the needs of business operation and implementing initiatives that supported the business. Furthermore, it is apparent that initiatives were directed by the notion that its intervention would not shift the social power balance from the State to itself due to increasing public expectations. To eradicate community dependency, it also suspended some of the services that it provided, primarily engaging in cooperative partnerships with the State when the State was unable to finance community projects that were crucial for operations. Further research should examine other host oil communities and their interactions with their respective oil companies.

Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation

Objective. Training neural networks for pixel-wise or voxel-wise image segmentation is a challenging task that requires a considerable amount of training samples with highly accurate and densely delineated ground truth maps. This challenge becomes especially prominent in the medical imaging domain, where obtaining reliable annotations for training samples is a difficult, time-consuming, and expert-dependent process. Therefore, developing models that can perform well under the conditions of limited annotated training data is desirable. Approach. In this study, we propose an innovative framework called the extremely sparse annotation neural network (ESA-Net) that learns with only the single central slice label for 3D volumetric segmentation which explores both intra-slice pixel dependencies and inter-slice image correlations with uncertainty estimation. Specifically, ESA-Net consists of four specially designed distinct components: (1) an intra-slice pixel dependency-guided pseudo-label generation module that exploits uncertainty in network predictions while generating pseudo-labels for unlabeled slices with temporal ensembling; (2) an inter-slice image correlation-constrained pseudo-label propagation module which propagates labels from the labeled central slice to unlabeled slices by self-supervised registration with rotation ensembling; (3) a pseudo-label fusion module that fuses the two sets of generated pseudo-labels with voxel-wise uncertainty guidance; and (4) a final segmentation network optimization module to make final predictions with scoring-based label quantification. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image segmentation tasks and compared to five state-of-the-art methods. Significance. Results demonstrate that our proposed ESA-Net can consistently achieve better segmentation performances even under the extremely sparse annotation setting, highlighting its effectiveness in exploiting information from unlabeled data.

BioDynGrap: Biomedical event prediction via interpretable learning framework for heterogeneous dynamic graphs

Model dynamic graphs with entity-specific heterogeneity has achieved remarkable success across various domains. By utilizing a novel dynamic graph encoding mechanism that employs rich temporal reasoning, we introduce BioDynGraph — a versatile and innovative framework for biomedical event prediction that bridges the gap between historical search and future reasoning. Existing studies tend to overlook clinical relationships between diseases during a patient’s visit by treating diagnoses as independent illnesses. Many machine learning techniques assume that disease progression within each time period is static and stationary, attenuating the information of all previous time steps using a homogeneous approach. However, in practice, disease progression is non-stationary, necessitating better use of combinational disease information to learn more about the dynamics of diseases. To address this problem, we use event clue searching and event temporal reasoning to infuse entity and relation information into dynamic subgraphs efficiently. BioDynGraph learns a global disease co-occurrence graph through event clue searching and acquires dynamic subgraphs for each patient’s visit using event temporal reasoning. Experimental results demonstrate that BioDynGraph exhibits superior performance compared to state-of-the-art (SOTA) baseline methods on biomedical event prediction using three electronic medical records datasets. Additionally, the clues found by BioDynGraph provide interpretability of the event prediction, showcasing its potential as an effective general-purpose interpretability algorithm for biomedical graph data.

Innovative Approaches in Citizenship Education Research: Conceptual Perspectives and Ethical Cautions

This article is devoted to the analysis of innovative approaches in citizenship education research taking into account conceptual perspectives and ethical cautions. The conceptual, axiological, and ethical basis of such innovative frameworks as digital citizenship education and artificial intelligence application for the development of responsible citizenship is elaborated. These innovations revolutionized the theory and practice of citizenship education, causing the use of new approaches and pedagogical methods as well as change in learning environments simultaneously adding new modalities and domains into objectives of citizenship education.

HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine

The strain on healthcare systems is growing dramatically due to the significant rise in heterogeneous chronic disease incidence. Even though frequent patient monitoring is complex and expensive, analyzing chronic illness transitions and predicting patients at risk of developing ailments is crucial to healthcare. This paper proposes an innovative prediction framework for heterogeneous chronic disease prediction (HCDP), including hypertension (HT), heart disease (HD), and chronic kidney disease (CKD), and develops a patient symptom-weighted network (PSWN) by projecting the bipartite graph. Hence, PSWN represents the relationship between health conditions for patients with similar illnesses. LSTM was used to capture the temporal relationship between features in the patient network using a complex network theory. Finally, a deep extreme learning machine (DELM) was applied as a classifier to predict heterogeneous chronic disease. Subsequently, the proposed model was evaluated against previous studies, and the result revealed that the proposed model was more accurate. Remarkably, the proposed architecture achieves an accuracy of 95.02% for hypertension, 96.15% for heart disease, and 98.39% for chronic kidney disease prediction. The relevance of the model is also evaluated with the use of the Wilcoxon signed-rank test. As evidenced in this study, the suggested model outperformed previous research and state-of-the-art approaches, suggesting its potential for a pivotal role in the medical decision-making of heterogeneous chronic diagnoses. Furthermore, the HCDP-DELM model contributes to healthcare authorities by establishing a system of smart surveillance and precise forecasting that allows for delayed disease detection and provides insightful potential strategies for developing effective healthcare.

CONECTAR: A MINDSET TO INCREASE HISPANIC ENGAGEMENT IN DIGITAL TECHNOLOGY RESEARCH

Abstract In-home digital technology affords the opportunity for more precise and ecologically valid assessments of daily activity engagement. However, concerns about loss of privacy can hinder recruitment, especially within communities already underrepresented in research. Literature on effective, culturally tailored recruitment strategies is limited. Herein, we describe strategies employed for an in-home digital technology study with 100% Mexican American representation. Hispanic participants were recruited from the greater San Antonio area. Direct recruitment strategies conducted in both English and Spanish included community outreach events, a participant repository, cross-enrollment with other research studies, and by word of mouth. Mass recruitment strategies included flyer dissemination, newsletters, and digital announcements through community organizations (i.e., Salud America!) and national (i.e., Research Match) and institutional websites. Between September 2022 and February 2023, 31 participants were enrolled out of 154 individuals who were contacted. Of these, 29% were recruited from community events, 22.6% from word of mouth, 19.4% from cross-enrollment, 12.9% from the research repositories, and 16.1% from mass recruitment strategies. The most common reasons for non-enrollment included lack of interest (n=49), screen failure (n=29), and privacy concerns (n=12).Direct recruitment efforts achieved higher rates of enrollment, while mass media campaigns yielded lower responses and resulted in more unsuccessful contact attempts. Understanding the relationship between social networks and research beliefs may be particularly relevant given the Hispanic culture’s collectivist and family-oriented nature. Hence, social network theories, combined with a diffusion of innovation framework, may be advantageous for increasing trust and may consequently improve representation of diverse groups in research.

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Deciphering the immune modulation through deep transcriptomic profiling and therapeutic implications of DNA damage repair pattern in hepatocellular carcinoma

AimsDNA damage repair (DDR) plays a pivotal role in hepatocellular carcinoma (HCC), driving oncogenesis, progression, and therapeutic response. However, the mechanisms of DDR mediated immune cells and immuno-modulatory pathways in HCC are yet ill-defined. MethodsOur study introduces an innovative deep machine learning framework for precise DDR assessment, utilizing single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Single-cell RNA sequencing data were obtained and in total 85,628 cells of primary or post-immunotherapy cases were analyzed. Large-scale HCC datasets, including 1027 patients in house together with public datasets, were used for 101 machine-learning models and a novel DDR feature was derived at single-cell resolution (DDRscore). Druggable targets were predicted using the reverse phase protein array (RPPA) proteomic profiling of 169 HCC patients and RNA-seq data from 22 liver cancer cell lines. ResultsOur investigation reveals a dynamic interplay of DDR with natural killer cells and B cells in the primary HCC microenvironment, shaping a tumor-promoting immune milieu through metabolic programming. Analysis of HCC post-immunotherapy demonstrates elevated DDR levels that induces epithelial-mesenchymal transition and fibroblast-like transformation, reshaping the fibrotic tumor microenvironment. Conversely, attenuated DDR promotes antigen cross-presentation by dendritic cells and CD8+ T cells, modulating the inflammatory tumor microenvironment. Regulatory network analysis identifies the CXCL10-CXCR3 axis as a key determinant of immunotherapeutic response in low DDR HCC, potentially regulated by transcription factors GATA3, REL, and TBX21. Using machine learning techniques by combining bulk RNA-seq data in house together with public datasets, we introduce DDRscore, a robust consensus DDR scoring system to predict overall survival and resistance to PD-1 therapy in HCC patients. Finally, we identify BRAF as a potential therapeutic target for high DDRscore patients. ConclusionOur comprehensive findings advance our understanding of DDR and the tumor microenvironment in HCC, providing insights into immune regulatory mechanisms mediated via DDR pathways.

A New Perspective on Digital Twin-Based Mechanical Design in Industrial Engineering

The advent of digital twin methodologies in industrial engineering is inspiring a transformative wave in mechanical design processes. This innovative framework signifies the convergence of real and virtual worlds, enabling an unprecedented level of synergy between design, simulation, production, and evaluation cycles. The resultant 'digital counterparts' serve as dynamic blueprints that echo the life cycle of their physical kin, unraveling sophisticated opportunities for predictive maintenance, accelerated prototyping, and mitigated risks. Residing at the cusp of this evolution, mechanical design draws immense strategic advantage. The adoption of digital twins unearths potential for holistic design enhancements through real-time condition monitoring, performance prediction, and comprehensive data analytics. These processes structure efficient decision-making protocols, fostering increased product reliability, enhanced operational efficiency, and reduced time-to-market. Moreover, digital twins open new avenues for developing complex systems and large-scale machinery through seamless integration of interdisciplinary skills. Intricate simulations aid in isolating potential hurdles and foreseeing outcomes with substantial accuracy, ensuring a meticulous yet fluid design process. Ultimately, the emergence of digital twins in mechanical design embodies a remarkable shift pivoting towards Industry 4.0, redefining the paradigms of industrial engineering with the blend of cornerstone technologies. Leveraging these state-of-the-art methodologies hints towards an optimistic future regarding sustainable industry practices and technological advancements.

An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection

Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the treatment of patients with epilepsy. EEG based epileptic seizure (ES) detection has significant applications in epilepsy treatment and medical diagnosis. Therefore, this paper presents an innovative framework for efficient ES detection, providing coefficient and distance correlation feature selection algorithms, a Bagged Tree-based classifer (BTBC), and Explainable Artificial Intelligence (XAI). Initially, the Butterworth filter is employed to eliminate various artifacts, and the discrete wavelet transform (DWT) is used to decompose the EEG signals and extract various eigenvalue features of the statistical time domain (STD) as linear and Fractal dimension-based non-linear (FD-NL). The optimal features are then identified through correlation coefficients with P-value and distance correlation analysis.These features are subsequently utilized by the Bagged Tree-based classifer (BTBC). The proposed model provides best performance in mitigating overfitting issues and improves the average accuracy by 2% using (CD, E), (AB, CD, E), and (A, B) experimental types as compared to other machine learning (ML) models using well-known Bonn and UCI-EEG benchmark datasets. Finally, SHapley additive exPlanation (SHAP) was used to interpret and explain the decision-making process of the proposed model. The results highlight the framework’s capability to accurately classify ES, thereby improving the diagnosis process in patients with brain dysfunctions.

Enhancing Personnel Selection through the Integration of the Entropy Synergy Analysis of Multi-Attribute Decision Making Model: A Novel Approach

Personnel selection stands as a pivotal component within the domain of human resource management, intrinsically tethered to the quality of the workforce at large. In this research endeavor, we introduce the Entropy Synergy Analysis of Multi-Attribute Decision Making (ES-MADM) model, an innovative framework expressly designed to rationalize and augment the decision-making processes inherent in the evaluation and selection of personnel within corporate entities. The ES-MADM model systematically navigates the complexities of personnel selection by imbuing objectivity into the assessment criteria, thereby facilitating the structured ranking of potential candidates and establishing a discernible selection sequence. Furthermore, it delves into the statistical significance of these criteria, thereby reinforcing the decision-making process’s stability. This research conducts a comparative analysis with alternative multicriteria methodologies and employs sensitivity analysis to ascertain the overall efficacy of the ES-MADM model. This scholarly pursuit, through its rigorous approach, furnishes a comprehensive solution to the intricate challenges surrounding personnel selection, thereby championing a systematic, data-driven approach to underpin pivotal decisions in this sphere.