Emerging Science Journal (ESJ)
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Innovative Approach to the Optimal Distribution of Citizens' Pension Savings to Non-State Pension Funds
Full text linkIn the Russian Federation, persistent economic and legal tensions surround the allocation of citizens' pension savings; however, individuals retain the option to select the organization that manages the funded portion of their pension. This study aims to address the challenges posed by dynamic programming regarding the optimal distribution of Russian citizens' pension savings to non-state pension funds (NPFs), using predictive analyses of expected returns generated by the Verhulst forecasting equation. The research methodology encompassed system analysis, the Verhulst prognostic equation, dynamic programming models, and conditional optimization based on R. Bellman's equations. The study's information and empirical foundation comprised current regulatory legal acts, data from the Federal State Statistics Service (Rosstat), open data from the Central Bank of the Russian Federation (Bank of Russia), analysis of information sources on the activities of domestic NPFs, results of empirical studies by domestic and foreign authors, and information obtained from open sources on the profitability of 22 NPFs of the Russian Federation. The forecast for the period from 2024 to 2063, using the Verhulst forecasting model developed in this study, indicates that the highest value of expected profitability in 2063, specifically 11.66% in annual terms, should be anticipated from the JSC NPF Alliance, while the minimum (3.54% per annum) is expected from the JSC MNPF BOLSHOY. The solution to the dynamic programming problem concerning the optimal distribution of citizens' pension savings in NPFs demonstrated that the maximum return on investment of pension funds would be achieved under the condition that from 2024 to 2043, funds are invested in JSC NPF FUTURE, and from 2044 to 2063, the funds are invested in the JSC NPF Alliance. The total return on pension savings for the entire investment period (40 years) amounts to 5202%, or more than 52 times the initial investment. Doi: 10.28991/ESJ-2025-09-01-028 Full Text: PD
IoT System with ESP32 for Smart Drip Irrigation and Climate Monitoring in Greenhouses
No full textThe depletion of water resources and the need for sustainable agricultural practices require innovative technological solutions. This study develops an IoT-based smart drip irrigation and climate monitoring system for greenhouses using the ESP32 microcontroller. The methodology implements DHT11 sensors for temperature and humidity, GUVA-S12SD for UV radiation, capacitive soil moisture sensors, and HC-SR04 for water level measurement. Real-time data is displayed on an LCD screen and transmitted to the Arduino Cloud, enabling remote monitoring and control. Field tests showed a 35% reduction in water consumption compared to traditional methods, improving crop environmental conditions and reducing operating costs. The system operates in automatic and manual modes, adapting to various climatic conditions and user needs. The main innovation lies in its optimized water use efficiency through smart drip irrigation, ensuring precise humidity control and minimizing waste. Furthermore, its scalability allows integration with renewable energy sources, increasing its autonomy and sustainability. This approach fosters climate-resilient agriculture, aligned with the Sustainable Development Goals (SDGs), by promoting water conservation and efficient resource use
Adopting TOGAF Framework for Sustainable and Scalable Robusta Coffee Leaf Rust Management
Full text linkRobusta coffee (Coffea canephora) is a globally significant crop. However, managing Coffee Leaf Rust remains challenging due to the reliance on manual detection methods and the lack of structured technological integration. This study proposes a TOGAF-based framework as a scalable and adaptable solution for structuring Coffee Leaf Rust management strategies. The framework leverages enterprise architecture principles to integrate learning algorithms, image detection, and systematic plantation mapping within a structured approach that enhances data organization, rust severity visualization, and predictive analysis. The proposed framework provides a strategic roadmap for integrating technology into Coffee Leaf Rust detection and management by focusing on modularity, scalability, and stakeholder engagement. Unlike existing ad-hoc approaches, this framework is a foundation for future technology-driven solutions, balancing manual practices with structured digital adoption. As no prior research has combined TOGAF with agricultural disease management, this study presents a novel conceptual contribution that could guide future developments in smart agriculture. By adopting this framework, the Robusta coffee industry can move toward proactive, data-driven Coffee Leaf Rust management, fostering long-term resilience and productivity
Impact of Technologically Vigilant Leadership on Smart Sustainable Circular Supply Chain Management
No full textThe main aim of this study is to explore how technologically vigilant leadership (TVL) impacts on smart sustainable circular supply chain management (SSCSCM) in the context of Industry 6.0. This is accomplished by analyzing the mediating effect of the digital environment management accounting system (DEMA) in the relationship between TVL and SSCSCM. This research evaluated the proposed model using two distinct statistical methods within the framework of structural equation modelling, which facilitated the assessment of both linear and non-linear correlations among the constructs. The data was obtained from a sample of respondents employed in small and medium enterprises. Linear relationships were analyzed using SmartPLS, whereas non-linear relationships were examined with WarpPLS. In both instances, the influence of TVL on SSCSCM was significant and positive, as demonstrated by CB-SEM in SmartPLS 4.1.0.9 and PLS-SEM in WarpPLS 7.0. Furthermore, DEMA partially mediated the relationship between TVL and SSCSCM. The current investigation is one of the few that provides conclusive evidence on the impact of TVL on SSCSCM. Also, it offers new insights by highlighting that DEMA partially mediates the relationship between TVL and SSCSCM. These profound insights would provide guidance for practitioners and policymakers in developing improved TVL mechanisms to leverage the advantages of DEMA and thereby strengthen SSCSCM
Leveraging Hybrid Deep Q-Learning for Early Identification of At-Risk Students
No full textStudent performance prediction is employed to predict the learning performance to identify at-risk students. However, prediction models should also consider external factors along with learning activities, such as course duration. The student’s performance gets affected, which leads to a high decreasing rate and meets the risk of failing to complete the course on time. To overcome these challenges, this work proposed a Sea Lion Search Optimization (SLnSO) based on the Deep Q network (DQN) for predicting at-risk students. Here, the input data is taken from the dataset and forwarded to the data transformation phase, which is performed by Yeo-Johnson (YJ) transformation. Then, in the feature selection stage, the most relevant features are selected using the Damerau-Levenshtein technique. Then, Data Augmentation (DA) is performed to increase the dimension of the features, which is followed by the Deep Q Network (DQN) that is utilized for predicting the students at risk. Finally, by implementing the proposed SLnSO, the predicted results will be executed by DQN. The SLnSO-DQN is the combination of both Sea Lion Optimization (SLnO) and Squirrel Search Algorithm (SSA). The outcomes of the proposed model SLnSO-DQN attain significant performance that is based on various parameters, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root MSE (RMSE), and also obtained better values of 0.327, 0.265, and 0.514, respectively
Reevaluating the Impact of University Reputation on Job Employment: A Structural Equation Modeling Approach
No full textThis paper examines the impact of university reputation on the employment of new graduates in Oman through industry linkages in higher education. Given that employability worldwide is connected to the reputation of universities, this study seeks to determine whether a similar situation exists in Oman. Specific indirect effects using Structural Equation Modelling (SEM) are evaluated based on research into particular factors affecting graduate employment. The survey was structured and yielded responses from graduate students. Of these, 76.6% were employed, while the remaining 23.4% reported being unemployed or unable to find work. Findings reveal that student performance and competency significantly influence internship opportunities, which in turn enhance job employment (SP IO JE: = 0. 067, p = 0. 001; SC IO JE: = 0.1, p <0.001). However, the university's reputation regarding employment through industry partnerships was found to be negative and insignificant (UR IP JE: = 0. 011, p = 0. 072). This implies that while partnering with industries may alleviate unemployment in Oman, as is well known in Western countries, aligning universities with weak reputations for students’ employment chances may not yield satisfactory outcomes in Oman. Consequently, the study advocates for improving the relationship between universities and industries in Oman. Policymakers and academic institutions must focus on skills training, marketable skills, curriculum relevance, and increased internship opportunities for graduates. Furthermore, enhancing structures for organized employer assurances and assemblies to support long-established university graduates could strengthen universities' reputations in Oman. The study emphasizes the importance of improving the relationship between universities and industries in Oman. Therefore, policymakers and academic institutions must concentrate on skills training, marketable skills, curriculum relevance, and expanded internship opportunities for graduates. Additionally, enhancing frameworks for organized employer assurances and assemblies to support long-established university graduates could reinforce the university's reputation in the job market
Promoting Pro-Environmental Behaviors via Green HRM: The Roles of Green Empowerment and Leadership
No full textWith growing environmental concerns, organizations worldwide are increasingly integrating green practices into their operational frameworks. The interplay between GHRM practices, green empowerment, and green leadership creates a conducive environment for promoting task-related and broader proactive pro-environmental performance among employees. Effective GHRM practices lay the foundation, while green empowerment acts as the driving force, and green leadership enhances the overall impact, ensuring a sustainable organizational culture. Therefore, this study explores the influence of GHRM practices on task-related proactive pro-environmental performance (T-PEP) and proactive pro-environmental performance (P-PEP) through green empowerment. This study examines the moderating role of green leadership in GHRM and green empowerment. Using partial least squares structural equation modeling (PLS-SEM), we analyze data collected from 312 Pakistan food industry employees. The results indicate that GHRM significantly influences the P-PEP and T-PEP through green empowerment of employees’ food industries of Pakistan. Additionally, green leadership is identified as a significant moderator in the relationship between GHRM and green empowerment. These findings underscore the importance of aligning HRM practices with leadership initiatives to cultivate an organizational culture supportive of environmental sustainability. Based on affective events theory principles, this study offers theoretical insights and practical guidance, presenting valuable recommendations for industry managers and academic researchers in the food manufacturing sector
Integrated AI, IoT, and Blockchain for Enhancing Security and Traceability in Perishable Logistics
Full text linkThe perishability of food products in the supply chain poses a significant challenge in ensuring quality and safety. Inefficient monitoring of temperature, humidity, and storage time results in substantial economic losses and increased health risks. Traditional traceability systems rely on manual audits or essential IoT platforms that lack predictive capabilities, leading to delayed anomaly detection and inefficient intervention. Blockchain-based solutions improve transparency but primarily focus on record verification rather than active anomaly detection and automated decision-making. This study proposes an integrated system combining Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain to optimize food traceability through real-time monitoring, predictive analytics, and secure decentralized record management. The system deploys smart sensors across storage and transportation units to continuously collect environmental data, which is processed by a deep learning model trained to detect deviations with 92.4 % accuracy. Detected anomalies trigger automated responses via smart contracts in a blockchain network, ensuring immediate corrective actions while maintaining immutable audit records. Results demonstrate a 64.3 % reduction in response time, improving reaction efficiency to critical storage failures. Additionally, false positive alerts decreased by 73.1 %, optimizing operational efficiency and minimizing unnecessary interventions. The blockchain implementation reduced storage overhead by 76.9%, ensuring scalability and long-term feasibility. This research establishes a foundation for intelligent, automated food supply chain management, demonstrating that integrating AI, IoT, and blockchain enhances safety, reduces waste, and optimizes logistics. Future work will focus on improvements in large-scale deployment and computational efficiency to refine this innovative approach
Invisible Scout: A Layer 2 Anomaly System for Detecting Rogue Access Point (RAP)
Full text linkRogue Access Points (RAPs) pose a significant security threat by mimicking legitimate Wi-Fi networks and potentially compromising sensitive data. To address this issue, this research has proposed an innovative mechanism called Invisible Scout, which uses a multi-module system to identify RAPs. This study aimed to develop and validate a mechanism capable of accurately detecting RAPs in controlled setups, real-world environments, and under de-authentication attack scenarios. The proposed system consists of four key modules: sniffer, detection, probing, and comparison. To evaluate its effectiveness, tests were conducted in controlled and open environments and under de-authentication scenarios, using decision tree models and various metrics to assess performance. The decision tree model showed promising results in the controlled setup, achieving an Area Under the Curve (AUC) score of 0.921 and classification accuracy (CA) of 0.875, indicating that the model effectively distinguished between legitimate access points and RAPs. When tested in an open environment, the model's performance improved, achieving an AUC score of 0.952 and a CA of 0.994. Furthermore, under a de-authentication attack, the model achieved an AUC score of 0.955 and a CA of 0.996. To gain a deeper understanding of RAP behaviors, linear regression analysis was conducted, revealing patterns and visualizing the existence of RAPs, which could assist in further analysis. In conclusion, the results demonstrated that the proposed mechanism was highly effective in identifying RAPs. Future research should focus on refining the detection mechanism, incorporating real-time response capabilities, and expanding testing to diverse network scenarios. Doi: 10.28991/ESJ-2025-09-01-016 Full Text: PD
Beyond Social Norms: Exploring the Drivers of Youth's Political Participation Via Social Media
Full text linkThis study examines the factors driving online political participation among young adults by integrating the Theory of Reasoned Action and the Civic Voluntarism Model. Structural equation modelling was applied using survey data from 236 young adults to analyse the relationships between attitudes, subjective norms, psychological engagement, political interactions, and resource availability. The findings indicate that attitudes and psychological engagement”comprising political interest, efficacy, and involvement”are the primary drivers of online political participation. In contrast, subjective norms and resource availability have no significant effect, suggesting that online engagement is primarily self-motivated rather than influenced by social expectations or material constraints. Furthermore, political interactions shape subjective norms, but these norms do not significantly impact participation intentions. This study contributes to understanding youth political engagement in digital environments by demonstrating that intrinsic psychological factors outweigh external influences. The findings have practical implications for strategies aimed at increasing youth political participation through social media, emphasising the need to foster political interest and efficacy rather than relying on peer influence or resource provision. By refining existing models of political engagement, this research provides a clearer framework for understanding and enhancing youth participation in democratic processes through digital platforms. Doi: 10.28991/ESJ-2025-09-02-05 Full Text: PD