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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 803 Documents
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European Green Deal Objective for Sustainable Agriculture: Opportunities and Challenges to Reduce Pesticide Use Muska, Aina; Pilvere, Irina; Nipers, Aleksejs
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-02

Abstract

Agriculture in the 21st century faces complex global challenges, including rising food demand, resource depletion, and climate change. These pressures, however, also create opportunities to foster sustainability, enhance resource efficiency, and reduce reliance on synthetic pesticides. In response, the European Union (EU) adopted the European Green Deal in 2019, aiming for climate neutrality by 2050. The Farm to Fork (F2F) strategy sets a specific target: reducing chemical pesticide usage and its related risks by half by 2030. This study aims to assess the overall situation in the EU and the Member States' contributions to achieving the F2F objective of reducing pesticide use as well as risks at the policy level. A novel methodological approach was developed to assess Member State performance using a set of EU-defined indicators – such as Harmonised Risk Indicators (HRI 1 and HRI 2), pesticide sales data from Eurostat and FAOSTAT – and to classify countries into contribution-based groups. Findings reveal progress at the EU level: pesticide sales have declined and HRI 1 has dropped, but HRI 2 has increased. Significant variation among Member States was observed, highlighting the need for tailored policy actions. The study provides an innovative framework and practical insights for policymakers and stakeholders working toward sustainable agricultural transitions in the EU.
A Study of the Effects of Knowledge Management on Enterprise Innovation Performance Qu, Juanjuan; Batool, Hira; Ullah, Kalim
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-030

Abstract

This study aims to explore how knowledge management capability and enterprise innovation behavior jointly affect the innovation performance of manufacturing SMEs. Based on the background of the knowledge economy, we selected manufacturing SMEs with knowledge as their core competitiveness as the research object and constructed and optimized the theoretical model of “Knowledge Management Capability-Innovation Behavior-Innovation Performance”. Based on the research data from 400 manufacturing SMEs in China, the study adopts the empirical analysis method and examines the relationship between the variables through structural equation modeling. The results show that knowledge management capability has a significant positive impact on firms' innovation performance, while firms' innovation behavior mediates the relationship between knowledge management capability and innovation performance. The findings of this study not only validate the key role of knowledge management in enhancing the innovation capability of enterprises but also reveal the path mechanism for enterprises to realize knowledge transformation and innovation results by stimulating innovative behaviors. Compared with previous studies, this study systematically optimizes the construction of theoretical models and the analysis of mediating effects, enriches the research content in the field of knowledge management and innovation performance, and provides new theoretical support and empirical evidence for the knowledge management practice and innovation strategy formulation of manufacturing SMEs.
Defining the Determinants of Corporate Financial Performance: A Machine Learning Approach Badykova, Idelia R.; Dinmukhametova, Aliya A.
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-01

Abstract

This study investigates the determinants of corporate financial performance (CFP) among Russian enterprises (2012–2023) through the lens of geopolitical disruptions, employing ensemble machine learning (ML) to address methodological gaps in modeling non-linear institutional interactions. Using data from 25 large non-financial firms, we analyze sectoral, organizational, and strategic drivers, integrating train-test splits (75%/25%) and 10-fold cross-validation to mitigate overfitting. Results reveal that industry affiliation, initially dominant (28% explanatory power pre-2022), declined sharply post-sanctions (15%), reflecting vulnerabilities in globally integrated sectors like manufacturing and extractives. Organizational size exhibited a nonlinear relationship with CFP, favoring comparatively smaller firms’ agility over larger enterprises’ rigidity, consistent with transaction cost economics. Strategic investments in corporate social responsibility (CSR) and research and development (R&D) diminished post-2022 as firms prioritized liquidity and operational stability, aligning with resource-based view principles. Methodologically, Shapley Additive Explanations (SHAP) clarified threshold effects in CSR returns and innovation’s reduced role under sanctions. The study innovates by applying ensemble machine learning to sanction-affected emerging markets, challenging linear econometric assumptions and advancing institutional theory through a crisis-contextualized framework of resource dependence and stakeholder salience. Findings underscore the fragility of intangible assets under systemic shocks and advocate adaptive resource allocation frameworks to balance short-term survival with long-term resilience. This work provides policymakers and managers actionable insights for fostering operational agility and strategic foresight in volatile institutional environments.
Unleashing Effective Identification of ALS Based on Vowel Phonation: A Deep Learning Approach Al-Dossary, Hussein; Rahamathulla, Mohamudha Parveen; Sha, Mohemmed
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-012

Abstract

ALS (Amyotrophic Lateral Sclerosis) is one of the fatal diseases across the world. Therefore, early detection can save patients suffering from ALS from life-threatening consequences. Typically, ALS can be identified based on different factors, and one such factor is voice analysis. Detection of ALS using sound signals is convenient and simpler than other methods, as it is a non-invasive approach, which makes the process faster and more efficient for detection. However, detection of ALS using traditional approaches is challenging, as it is a time-consuming process and heavy reliance on medical experts is needed. Therefore, AI-based models can be used for effective classification of ALS and non-ALS patients, as AI-based models possess the immense ability to examine vast amounts of data, including audio files, effectively. Owing to these factors, the proposed model focuses on employing an AI-based model for ALS classification based on vowel phonation /a/ and /i/. The process is carried out using the Minsk2020 dataset, where important features needed for the proposed model are extracted using MFCC (Mel-frequency cepstral coefficients) by removing the shakiness and jitteriness of the voice. The MFCC feature extraction technique extracts features based on the mel scale, as this reflects human auditory perception, thereby extracting features that are useful for classification. These extracted features are fed to CNN-LSTM (Convolutional Neural Network – Long Short Term Memory) with rapid dilatenet for classifying ALS and non-ALS patients accurately by identifying even the subtle changes in audio signals using maximizing the expansion/dilation rate and aid the context information for interpreting and analyzing the sound of vowels accurately and correctly without any loss of information. Finally, the efficacy of the proposed model is assessed using evaluation metrics. The proposed research work can assist medical professionals in detecting patients with ALS based on vowel phonation.
Extending C-TAM-TPB: Dual-level Moderation of Perceived Web Security and Age in Digital Banking Abdalla, Reem Abbas
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-013

Abstract

This study examines the intention to adopt digital banking in Saudi Arabia by integrating the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). It includes Perceived Web Security (PWS) and Age as moderators, addressing trust and security perceptions across user segments in emerging markets. Data were collected from 353 digital banking users in Saudi Arabia using a cross-sectional quantitative design. The model was tested via Partial Least Squares Structural Equation Modeling (PLS-SEM), assessing measurement validity, path significance, moderation, and mediation effects. Predictive accuracy was evaluated with in-sample (R²) and out-of-sample (Q², PLS-Predict) indicators. The results confirmed the significance of core TAM variables—Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Attitude Toward Use (ATU)—on Behavioral Intention (BI). Attitude was a strong predictor of BI, but Subjective Norms (SN) and Perceived Behavioral Control (PBC) were not significant. PWS moderated the ATU–BI relationship, enhancing intention under high security perception, but Age's dual-moderation effect was unsupported. Sequential mediation analysis validated that PEU and PU influence BI indirectly via ATU. This study enhances digital adoption research through a validated dual-level moderation model combining security perception and age. It refines TAM-TPB integration and offers practical insights for creating secure, user-centered digital banking systems tailored to specific cultures.
Reindustrialization Plan Using the DPSIR and TOPSIS Methods Cardoso, Wagner; Siqueira, Regiane Máximo; Ferraz, Diogo
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-017

Abstract

(De)industrialization is a phenomenon that affects the economic development of developed and developing countries. However, there is a lack of studies that evaluate strategies to promote the industrialization of a country through the information of industry experts. Objective: this article aims to develop a reindustrialization plan based on strategies to increase the relative participation of Brazilian industry in the Gross Domestic Product (GDP) in order to mitigate the Brazilian deindustrialization process. Method: the method used was DPSIR (Driving Forces, Pressures, State, Impacts, and Responses) in order to map information from specialists directly involved in the theme of industrial development in the areas of economics, public law, scientific research, public management, and private management. In addition, the TOPSIS method was used to prioritize the specialists' responses in order of urgency of implementation. Findings: the main result of this work revealed the strategies that should be prioritized to promote the country's industrialization. Novelty: this research served as a basis for the elaboration of the Brazilian Reindustrialization Plan, presented at the end of the article.
Effect of Gadolinium Doping on the Structure of Ce₁₋ₓGdₓO₂₋₍ₓ/₂₎ Solid Solutions Prepared by Ionic Gelation Approach Ilcheva, V.; Boev, V.; Lefterova, E.; Avdeev, G.; Dimitrov, O.; Bojanova, N.; Kolev, H.; Petkova, T.
Emerging Science Journal Vol. 8 No. 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-01

Abstract

The current research aims to present the structural characterization of Gd-doped ceria powders and ceramics, investigating the structural evolution resulting from cerium substitution with Gd across the entire composition range from 0 to 100 mol.% Gd2O3. Ce1-xGdxO2-x/2 powders with varying Gd contents (0 ≤ x ≤ 1) were synthesized using the ionic gelation method followed by thermal annealing. The resulting powders were subjected to high-temperature treatment to obtain ceramics. Characterization methods included X-ray diffraction (XRD) to identify phase composition and confirm the formation of Ce1-xGdxO2-x/2 solid solutions, infrared spectroscopy (IR) and scanning electron microscopy (SEM) for structural and morphological studies, and X-ray photoelectron spectroscopy (XPS) to evaluate the electronic structure. Comparative analysis of Gd-doped calcined powders and sintered pellets revealed the impact of thermal treatment on the structural features of the resulting solid solutions, elucidating the influence of gadolinium substitution. The novelty of this research lies in demonstrating the successful preparation of Ce1-xGdxO2-x/2 solid solutions via an alginate-mediated ion-exchange process and providing a detailed structural investigation over the entire range of dopant concentrations. This assessment highlights the feasibility for further research of these materials as suitable candidates for intermediate-temperature solid oxide fuel cells (IT-SOFCs) or catalyst applications.   Doi: 10.28991/ESJ-2024-08-05-01 Full Text: PDF
The Impact of Higher Secondary ICT Education on University STEM Student Performance Biplob, Khalid Been Badruzzaman; Hashim, Mohd Azman; Hossain, Mohamed Emran; Bitto, Abu Kowshir; Hassan, Syed Najihuddin Syed; Sultana, Most.Nahida
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-014

Abstract

This study investigates the significant impact of ICT education from the Higher Secondary Certificate (HSC) on Bangladeshi students' progress in tertiary STEM fields. Through utilizing a comprehensive examination of demographic profiles, proficiency assessments, facility rating systems, and satisfaction measures, this study determines the complex relationships between HSC-level ICT education as well as success in STEM areas at the university level. Data were collected through an online survey from 244 students enrolled in Computer Science and Engineering (CSE), Software Engineering (SWE), and Information Technology Management (ITM) departments. The results highlight how many different factors have significant effects on students' first-semester SGPA. Several variables, including prior ICT knowledge on data handling from college, quality of instruction provided by the college ICT teacher, and HSC-level ICT course grade, have strong relationships with student performance at the university level. This study illustrates the positive impact of improved instructional materials and teacher-led projects on strong skill development, a phenomenon that will increase overall satisfaction among learners. Although geographical location, gender, and college type have been explored, it does not appear that they have significantly affected ICT course grades directly. Instead, instructional components and techniques for improving skills become important factors in determining students' academic performance. The study not only finds significant relationships but also promotes curriculum improvements with a focus towards ICT education technique optimization. With an aim of improving instructional methods and curriculum design, these observations provide governments and other individuals within education with suggestions that are applicable. The study highlights how important it is to effectively utilize ICT education in order to encourage overall STEM development in Bangladesh's educational system.
Ball Detection System for a Soccer on Wheeled Robot Using the MobileNetV2 SSD Method Puriyanto, Riky D.; Yunandha, Isro D.; Maghfiroh, Hari; Ma'arif, Alfian; Furizal; Suwarno, Iswanto
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-028

Abstract

This paper discusses the research on the use of Artificial Intelligence in autonomous robot object identification. The specific focus of this research is on a wheeled soccer playing robot. The goal is to recognize a ball as an object using the Single Shot MultiBox Detector MobileNetV2 model. This system has multi-vision inputs such as distance measurements and angle values ​​for object detection. This methodology is based on deep learning with the TensorFlow Object Detection API with the MobileNetV2 SSD model. This model is trained with a dataset of 3707 ball images over 617 thousand steps on Google Collaboratory. It was found that the average measurement error of the ball object is 6.58% for the distance when viewed through the robot's front camera. In addition, the omnidirectional camera is able to detect the ball object and angle values ​​from the front of the robot. What makes this research different is the use of distance and angle measurements for detection and the omnidirectional camera for system performance in dynamic environments. This research aims to address the improvement of AI-based object detection systems for autonomous robotics in the context of real-world use cases.
Corrosion Performance of a Novel Aluminium 6061-Sea Sand Composite Under Electrochemical Method Surojo, Eko; Triyono, Teguh; Akbar, Hammar Ilham; Pramudi, Ganjar; Triyono; Raharjo, Wijang W.; Agiel, Raiddin Muhamad; Majid, Faishal M.; Seputro, Harjo; Habibi, Ilham
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-012

Abstract

The need for lightweight materials is increasing from year to year. In its application, lightweight and strong materials also need to be corrosion resistant. Corrosion resistance is an important property in automotive, especially in high humidity areas. Al6061-Sea sand material is a novel material that meets the mechanical standards required in the automotive sector. A previous study of Al 6061-sea sand conducted the mechanical properties of the composite. This current research focuses on the development of Al 6061 material with variations in weight fraction of sea sand reinforcement against the corrosion rate under the potentiodynamic method to determine the corrosion resistance of the composite material. The composite fabrication uses the electroless coating method on sea sand and the stir casting method with a melting temperature of 750°C. The agitation process used a four-bladed impeller for 10 minutes at 600 rpm with a stirring depth of ½ of the height of the molten metal. The tests include density testing, microstructure observation, and corrosion rate under the potentiodynamic method using an electrochemical potentiostat. The test result obtained the lowest corrosion rate results in 2% wt sea sand with a corrosion rate of 0.61875 mmpy. The increase in corrosion rate value is directly proportional to the addition of the weight fraction of sea sand.

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