Emerging Science Journal (ESJ)
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Unveiling the Power of Emotional Intelligence: A Dynamic Exploration of Its Impact on Proactive Decision-Making
Full text linkIn this multifaceted, dynamic business world, leadership success and effective decision-making are highly dependent on understanding and controlling emotional intelligence (EI). In this multifaceted world of work, the integration and strategic use of emotional intelligence (EI) drives change and facilitates proactive and effective decision-making. The study aims to examine the direction and degree of relationship that exists between EI and proactive decision-making using one of the public service institutions in Ethiopia, the Ethiopian Customs Authority. The study uses a quantitative and correlational design utilizing a structured survey administered to a randomly selected sample of 156 employees. The study applied statistical analysis techniques, including correlation and stepwise regression analysis, to determine the strength and significance of the relationship between EI and proactive decision-making. Research has examined how and in what direction EI competencies (emotion perception, regulation, and utilization) influence features of proactive decision-making. The result accentuates that Emotional intelligence emerges as a critical driver, accounting for a substantial proportion of the observed differences in individuals' ability to make proactive decisions. Grounding on the findings, the study highlights the necessity of integrating EI training programs into organizational development strategies to enhance employees' decision-making capabilities, adaptability, and resilience in dynamic workplace environments
LRX: A Hybrid-based Real-Time Air Quality Index Prediction and Visualization Model
Full text linkAccurately predicting the air quality index significantly reduces health risks and supports urban environmental planning. This paper presents LRX, a hybrid predictive model, for Air Quality Index (AQI) prediction. The model employs Long short-term memory to capture temporal dependencies, Random Forest to fine-tune the features, and Extreme Gradient Boosting to enhance the final predictions. The objective of the study is to build a model that can accurately predict air quality index numbers in real time for many cities in India. The proposed model LRX design influences the depth of each algorithm to enhance accuracy and generalization. The experimental results show the model's ability to predict the AQI forecast of various cities in India with a root mean square error of 0.014 and R2 of 0.948, performing better compared to the models individually. To enhance this, a Stream lit-based user interface has been developed to enable real-time AQI predictions and visualization. The interface incorporates tabs for interactive inputs, model selection, graphical representation of predicted trends, ensuring accessibility and usability, and enhancing the practical applicability of the proposed model. This easy-to-navigate tool not only makes the prediction process more accessible but also helps bridge the gap between complex model results and practical environmental decision-making, enhancing the overall impact of the research. This research contributes to air quality prediction by presenting a robust modelling approach that can be applied in the real world
Modeling and Forecasting the Dynamics of BRICS Socioeconomic Integration in Context of Global Economic Fragmentation
Full text linkThis research aims to model and forecast the trajectory of socioeconomic integration between the BRICS countries and Turkey in the context of deglobalization and escalating geoeconomic fragmentation. The study evaluates the impact of external shocks, including the updated US tariff regime, trade conflicts, global downturns, sanctions pressure, and institutional limitations, on the sustainability and intensity of intra-bloc engagement. Employing a comparable panel time series from 2000 to 2023 for a selected set of countries, we apply several econometric methods for the first time in a unified framework: panel ARDL, Dumitrescu-Hurlin panel Granger causality tests, impulse response functions, variance decomposition, and ARIMA forecasting up to 2030. The empirical analysis results show that regional financial integration, infrastructure development, and R&D have a statistically significant and persistently positive impact on intra-bloc trade volumes and socioeconomic cooperation between BRICS countries and Turkey. Simultaneously, digital connectivity shows a short-term stimulating effect followed by phase saturation, indicating the need for the structural modernization of the digital environment. Institutional attributes exhibit heterogeneous effects, underscoring the necessity of harmonizing regulatory frameworks and aligning them with international sustainable development standards. The scientific novelty of this study resides in the design and empirical validation of an advanced forecasting model that incorporates institutional, infrastructural, innovation-related, and digital dimensions. This study delineates integration scenarios for BRICS and Turkey intended to inform strategies for regional macroeconomic coordination, establish transaction mechanisms based on national currencies, and define balanced investment priorities within the transition toward a multipolar global governance architecture
DML-IDS: Distributed Multi-Layer Intrusion Detection System for Securing Healthcare Infrastructure
No full textIn recent years, the number of cyberattacks targeting healthcare resources has rapidly increased. Conventional IDSs rely heavily on predefined rules and attack signatures. However, modern zero-day attacks with unpredictable behavior and multi-vector attack patterns can still breach healthcare networks. When a new type of cyberattack targets a specific server, an existing IDS may fail to detect it because it depends on static, predefined rules. To address these issues, we propose DML-IDS: Distributed Multi-Layer Intrusion Detection System, designed to operate across multiple nodes in a network to collaboratively detect suspicious activities. The proposed approach employs a multi-layer ensemble strategy to improve detection accuracy while reducing computational overhead on a single machine. All incoming network packets are first analyzed by the Distributed Threat Analysis Module (DTAM), which runs a Random Forest-based model as the base classifier to distinguish between benign and malicious traffic. Based on the nature and severity of the threat, malicious packets are flagged as highAlert (HA) in the Threat Prioritization Layer (TPL) and then forwarded to the respective Confirmatory Ensemble Model (CEM) for further, attack-specific analysis. These CEM models are designed to scale efficiently and detect zero-day as well as multi-vector attacks. The proposed model was trained on the CICIDS-2017 dataset. DTAM achieved an accuracy of 98.5%, while the CEM models for DDoS, Patator, and Web Attack achieved 99.01%, 98.87%, and 98.91% accuracy, respectively. Furthermore, the computational overhead of the DML-IDS architecture was evaluated and compared with an existing ensemble learning-based IDS
Navigating Cultural Barriers: The Role of Socio-Technical Systems in Digital Transformation Readiness in SMEs
No full textDigitalization in the industrial sector has become necessary for both the public and private sectors worldwide to adapt to the rapidly evolving digital landscape. This initiative is articulated in strategic plans designed to foster positive learning environments while minimizing adverse impacts on organizations. Although digital maturity has become an organization's top priority, enterprises still lack digital transformation consciousness and means. The interaction between technological transformation and cultural change highlights the necessity of addressing the resistance during the transition. Utilizing the socio-technical system theory, this study investigates the impact of organizational culture on the readiness for digital transformation in SME enterprises in Malaysia. The study comprises responses from 176 employees across various sectors of SMEs in Malaysia. A quantitative data analysis was conducted by employing the PLS method. The study found that technology, processes, customers, and partners positively impact the organization's digital transformation readiness (DTR). However, it was found that cultural factors within an organization act as a barrier to progress on the DTR. The present study provides insights by identifying critical factors in the digitalization process to enhance operations and business value to achieve sustainable development and improve quality of life through the digital economy
Smart Irrigation System with IoT, Machine Learning, and Solar Power for Efficient Plant Care
No full textEfficient irrigation in green areas and homes is essential for environmental sustainability and water conservation. This study aims to develop an intelligent irrigation system based on the Internet of Things (IoT) and machine learning to optimize water use, improve plant monitoring, and enhance security. Two ESP32 microcontrollers and an ESP32-CAM were deployed to manage humidity, temperature, light sensors, and irrigation automation using a solenoid valve. A modified Yolov3-tiny model detects signs of dehydration and chlorosis in plants, while facial recognition restricts access to authorized users. Data is processed through IoT platforms such as Adafruit IO and Telegram, ensuring continuous solar-powered monitoring. Furthermore, integration with YouTube Live and Dropbox enables remote monitoring and intrusion detection. Experimental results indicate a 43.7% reduction in water consumption, efficient detection of plant problems (93.86% accuracy), and increased security. Based on AI and renewable energy, this innovative approach surpasses traditional systems and represents a scalable and sustainable solution for innovative irrigation management
The Impact of Financial Structure on Financial Security in an Emerging Market
Full text linkThis paper aims to understand the extent and trend of the impact of financial structure on the financial security of companies in an emerging economy. The paper uses panel data collected from 2010 to 2023 at Vietnamese real estate companies. OLS, FEM, REM regression models and necessary tests are applied in turn. GMM regression is used to overcome the shortcomings of the model. The research results show that financial security will be high in companies with high debt ratios and return on assets. In contrast, financial stability will be low in companies with high fixed asset ratios, inventory ratios, return on equity, and years of establishment. The findings also show that financial safety will decrease in companies with high receivables ratios, cash and cash equivalents ratios, return on equity, and large size. To our knowledge, this is the first quantitative study to examine the effect of financial structure on financial security from two aspects: financial safety and stability. This is also the first study to address financial structure from three perspectives: capital structure, asset structure, and the relationship between assets and capital. Doi: 10.28991/ESJ-2025-09-02-04 Full Text: PD
Analysis of Four-Species Diffusive and Non-Diffusive Food Chains Using Artificial Neural Networking
Full text linkThis study uncovers the findings of a four-species food chain model, focusing on its equilibrium points, global stability, and population dynamics. Through rigorous mathematical analysis, we identify the equilibrium points of the model and investigate the global stability of the coexistence equilibrium point. We present the existence conditions for all equilibrium points and assess the stability characteristics of the coexistence fixed point. Time series solutions offer a captivating perspective on the dynamic behavior of a system. Our investigation into the effects of parameters provides the fluctuations in population density, with specific parameters exerting significant influence as a result of the random movement of linked species. Understanding the need for taking account of diffusion-dominated situations, the diffusive version of the model is developed and analyzed. By constructing a numerical system with three-time levels (n-1, n, and n+1), its stability can potentially be tested thoroughly using the Von Neumann stability criterion. Numerical simulations and graphs depict the system's dynamic interaction. We also examine how diffusion coefficients affect population density, creating remarkable charts that show interactive species relationships. We also identify exciting bifurcation occurrences in the system, which helps us comprehend its complex dynamics. Predator-prey systems can be studied using Artificial Neural Networks (ANNs) to handle complexity, discover patterns, and predict future dynamics. ANNs can predict population dynamics and assess various parameters by analyzing prior data. Their adaptability lets them improve forecasts over time, improving management methods and ecosystem balance. We use ANN methods to see how specific parameters affect interacting species population dynamics. Doi: 10.28991/ESJ-2025-09-02-011 Full Text: PD
Empowering Higher Education Through Digital Transformation and Strategic Planning for Academic Advancement
Full text linkThe digitalization of strategic planning is crucial for Oman's postsecondary higher education. This study focuses on the impact of automating and digitalizing strategic planning management to bridge gaps and improve decision-making. Aligning strategies with goals through digitalization enhances academic excellence and global recognition, providing valuable insights for Omani higher education managers. Investigating strategic planning automation in non-Western settings, it advocates for integrating digital tools to advance academia globally. The main objective is to assess how automation and digital information affect these institutions' effectiveness, accuracy, and efficiency in strategic planning. Structural Equation Modeling (SEM) used as the primary methodology. The dataset consists of 224 participants and 15 indicators. Both the measurement model and structural model were employed for analysis and testing. The results show that a two-tailed test with t=-1.96 at a 95% significance level found automation significantly affects strategic planning (B=0.36, t=2.97, p=0.000), and digitalization significantly affects strategic planning (B=0.30, t=2.68, p=0.01). Mediation analysis of automation as a mediator revealed a noteworthy indirect relationship between digitalization and effective strategic planning (B=0.30, t=2.96, p=0.000). While automation offers many advantages, digitalization enables a more comprehensive and fundamental change that supports various strategic goals. By integrating digitalization into their strategic planning, organizations can create a robust digital infrastructure that improves data capabilities, encourages innovation, and boosts agility, providing a sense of security about the future of Omani higher education. This positions them for long-term success and resilience in the face of changes in the business environment. Doi: 10.28991/ESJ-2024-SIED1-019 Full Text: PD
Development of a Digital-Based Assessment of Key Competencies for Transformation toward Sustainability: Multi-Assessment Evidence
Full text linkThe objectives of this research were to develop a digital-based assessment system for evaluating key competencies for transformation toward sustainability, review the assessment system, examine the dimensions by analyzing multidimensionality, and make predictions using logistic regression analysis. The sample consisted of 449 students for the needs study, 10 experts for system development and assessment, 38 students for system assessment, and 674 students to examine the key competencies. The instruments used were a questionnaire, a focus group discussion record form, an assessment form for students and experts, and an assessment in the digital-based system. Data were analyzed using content analysis, mean, standard deviation, multidimensional analysis, and logistic regression analysis. The results of the research showed the following: 1) Students and experts must develop a digital assessment system. 2) A digital-based assessment system should be developed. This involved personalized assessment through the website. The assessment results were obtained in real-time, and development suggestions regarding each competency were incorporated. The use of the digital-based assessment system demonstrated that the screen, terminology, system information, and system capabilities were at a good level. The experts' assessment showed that the heuristic was at a good level, whereas the utility, interpretation, and accuracy of the system were deemed to be very good. 3) The evidence of the key competencies was multidimensional. In addition, the predictive model regarding academic achievement was consistent with the empirical data, in that seven competencies could be predicted, but two competencies could not. The model predicts academic success 68.50% of the time. Doi: 10.28991/ESJ-2025-09-02-016 Full Text: PD