About Emerging Science Journal (ESJ) Emerging Science Journal is a multidisciplinary, an open-access, internationally double-blind peer-reviewed journal which 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. ESJ covers the following main subjects: Engineering and Technical Sciences Natural Sciences Social and Management Sciences Formal Sciences Physical Sciences For more information about ESJ Focus and Scope, you can click here. Current areas of interest, along with related associate editors, are listed below: Engineering and Technical Sciences Prof. Francesco Tornabene Department of Innovation Engineering, University of Salento Lecce, Italy Associate Editor Natural Sciences Prof. Nour Shafik Emam El-Gendy Egyptian Petroleum Research Institute (EPRI) Cairo, Egypt Associate Editor Social and Management Sciences Prof. Ferda Halicioglu University of Lincoln Lincoln, United Kingdom Associate Editor Formal Sciences Prof. Sunil Kumar National Institute of Technology Jamshedpur, India Associate Editor Physical Sciences Prof. Shen Xiu-Min Beijing Normal University Beijing, China Associate Editor Editorial Team View More International Emerging Science Journal Open Special Issues Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Aim and Scope Current issues in education involve differing views on standardized tests, socioeconomic factors, educational equity, and adaptive learning. It is important to understand that these issues are distinct from current trends in education, such as changes in the higher education system, the rise of online education, and the adoption of standards-based learning. To address and resolve these challenges, innovative ideas must be conceived and implemented. This Special Issue prioritizes studies that integrate insights from diverse disciplines, contextualize research findings within a broader framework, advance methodological and theoretical understanding, and possess the potential to influence education policy and practice. It welcomes quantitative, qualitative, and mixed-method research papers, as well as comprehensive reviews of significant research studies and technical reports previously published. Guest Editor: Dr. Stamatis Papadakis Affiliations: Department of Preschool Education, School of Education, University of Crete, Crete, Greece. Interests: Distance Learning; Mobile Learning; Learning Analytics; Game-based Learning; Preschool Education; Primary Education; Secondary Education; Educational Robotics. 0000-0003-3184-1147 Co-Guest Editor: Prof. Paula Escudeiro Affiliations: Instituto Superior de Engenharia do Porto, Porto, Portugal. Interests: Educational Technology; Learning Technology; Information Technology in Education; Serious Games. 0000-0003-2528-572X Manuscripts should be submitted online at www.ijournalse.org by registering and logging in to this website. For more information Click Here. This Issue is now open for submissions. Deadline for manuscript submissions: 31 December 2025. Closed Special Issues Special Issue "Internet of Things, Internet of Vehicles, and Blockchain"-2020-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2022 - Published Papers Special Issue "Current Issues, Trends, and New Ideas in Education"-2022 - Published Papers Most Cited & Viewed Articles The most cited articles from Emerging Science Journal published since 2019, extracted from Scopus. Citation Article Big Data, Knowledge Mapping for Sustainable Development: A Water Quality Index Case Study by: Lei Zhang Effect of Salinity on Evaporation and the Water Cycle by: Jude Iloabuchi Obianyo Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications by: Allan Ramone Araujo Scharnberg, Adrison Carvalho de Loreto, Annelise Kopp Alves Revisiting Agroforestry for Building Climate Resilient Communities: A Case of Package-Based Integrated Agroforestry Practices in Nepal by: Kishor Aryal, Prakash Singh Thapa, Dhananjaya Lamichhane Impact of Transportation on Carbon Dioxide Emissions from Locally vs. Non-locally Sourced Food by: Bradley Striebig, Eric Smitts, Samuel Morton Preliminary Studies on the Use of Sawdust and Peanut Shell Powder as Adsorbents for Phosphorus Removal from Water by: Marian Asantewah Nkansah, Moses Donkoh, Osei Akoto, James Hawkins Ephraim
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Dynamic Capabilities and Technological Innovation for Firm Resilience: A Configurational Analysis
Firm resilience is essential to manage response and rapid recovery from disruptive events for a firm. Moreover, there is limited literature that investigates the combined effects of dynamic capability and technological innovation that are interrelated with firm resilience. This study used the dimensions of firm resilience, which were investigated with both necessary condition analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) methods using survey questionnaires from 308 respondents operating in Bangladeshi corporate industries that are currently facing uncertainties due to unforeseen crises. NCA results showed that visibility, market position, and digitalization achieved firm resilience as these antecedents reached the full percentile to achieve an optimal level of outcome. On the contrary, the influence of reserve capacity and big data analytics was not empirically significant for achieving firm resilience. Moreover, fsQCA results appreciated NCA results and showed four solutions that are sufficient for achieving a high level of firm resilience. The study reveals the configurational effects of dynamic capabilities and technological innovation to achieve firm resilience. The results show the necessary effects of configurational relationships that lead to outcomes. The configurational method is applied to identify the combined effects of antecedents that help managers predict high levels of firm resilience in a turbulent environment.
Modeling and Performance Optimization for Complex Workflow in IoT
This study addresses the growing challenge of time scheduling in Internet of Things (IoT) workflows, where efficiency in time utilization and resource profitability is increasingly constrained by uncertainty. Real-world workflows are characterized by non-deterministic activity execution and resource preparation times, yet existing research often neglects these fundamental dynamics when modeling IoT-based processes. To bridge this gap, we propose a comprehensive modeling and performance optimization framework that explicitly incorporates uncertainty. Methodologically, the framework introduces two distinct types of places to represent activities and resources, with resource properties capturing reusability and preparation processes abstracted as specialized activities. For workflow activities, timing functions are defined to model minimum and maximum execution times, enabling the computation of earliest and latest start times and the identification of critical activities driving overall workflow duration. To mitigate resource conflicts during execution, three alternative resolution strategies are developed and systematically evaluated. Results demonstrate that the proposed approach effectively identifies optimal scheduling strategies under uncertainty, enhancing both temporal efficiency and resource utilization. A workflow case study illustrates the applicability of the framework, offering methodological and practical insights for designing resilient IoT workflow scheduling systems in complex, real-world environments.
Agarose-Based Antibacterial Films from Gracilaria sp.: Isolation, Characterization, and Metal Nanoparticle Incorporation
The incorporated metal nanoparticles in a polysaccharide-based film exhibit efficient antibacterial activity against harmful germs. However, previous studies have used a commercial polysaccharide for their film production. Therefore, this study aimed to develop a natural polysaccharide-based film extracted from the local algae Gracilaria sp. originating from Sinjai Regency, South Sulawesi, Indonesia. Firstly, the polysaccharide agarose was isolated and its properties compared with those of commercial agarose. A present low-cost isolation process produces agarose with 1.8% (w/w) of yield. Results also showed physicochemical properties similar to those of the commercial agarose. Secondly, the agarose-based antibacterial film was synthesized at 0, 0.5, and 1% glycerol concentrations. The synthesized film was incorporated with silver (Ag) and copper (Cu) nanoparticles (NPs). Morphological, mechanical, and physicochemical properties of the incorporated Ag-agarose and Cu-agarose films were characterized using Field Emission Scanning Electron Microscope (FESEM), Universal Testing Machine (UTM), and Fourier Transform Infrared Spectroscopy (FTIR), respectively. Results showed the film stiffness and tensile strength increased by incorporating either AgNPs or CuNPS. The interaction of AgNPs-agarose most likely involves physical bonds, while the interaction of CuNPs-agarose forms coordination bonds. An antibacterial test showed that the Ag-agarose nanocomposite inhibited the growth of Escherichia coli, Salmonella typhimurium, Staphylococcus aureus, Staphylococcus epidermidis, and Bacillus subtilis. In the meantime, Cu-agarose prevented the growth of Staphylococcus aureus. Overall, antibacterial activity was influenced by the interaction between metal nanoparticles and agarose, the concentration of metal nanoparticles, and the film's solubility. An agarose-based antibacterial film from Gracilaria sp. has the potential for use in various applications, including food packaging, pharmaceuticals, and other industries.
Study on Preparation of Nano Humic Acid and Adsorption Effect of Heavy Metals in Soil
Nano humic acid (NHA) offers a promising strategy for remediating agricultural soils contaminated by livestock and poultry manure. This study investigates the adsorption behavior of NHA for heavy metals (Cu, Zn, As, Mg) and nitrogenous compounds (nitrate and ammonium nitrogen) in real-world polluted soil collected from a poultry farm in Changzhou, China. NHA was synthesized via high-shear, acid-precipitation, and surfactant-assisted methods, and its structure was characterized using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and particle size analysis. FTIR revealed the emergence of new functional groups (e.g., amino, ester, sulfonic), enhancing the active sites available for pollutant binding. At 30 days, NHA treatments achieved substantial reductions in Cu (76.1%), Zn (57.5%), and As (12.9%), with NANO3 and NANO4 showing the highest adsorption capacity. At 90 days, Cu and Mg continued to exhibit strong dose-responsive removal (up to 49.9% and 26.8%, respectively), while Zn and As showed nonlinear responses, likely due to saturation effects. NHA also outperformed traditional humic acid in nitrate and ammonium nitrogen adsorption, with the 25 g/kg application (NANO2) achieving up to 55% nitrate and 20% ammonium reduction. Correlation analysis confirmed that material type, rather than dosage alone, was the dominant factor influencing pollutant immobilization. These findings demonstrate that NHA is an effective, dual-function soil amendment capable of long-term remediation of both heavy metal and nitrogen pollution, offering a cost-effective and scalable solution for improving soil quality in degraded agricultural regions such as the Yellow River basin.
Combining a Moving Average with a Triple EWMA Chart to Improve Detection Performance
This article aims to introduce the novel mixed triple exponentially weighted moving average-moving average (MTEM) chart to accurately detect position changes for both symmetric and non-symmetric distributions. The MTEM chart constructs a moving average (MA) structure to filter out fluctuations in the raw data and then applies triple exponential weighting to improve the ability to identify minor shifts. The average run length (ARL) and median run length (MRL), which are run length profiles derived from the Monte Carlo simulation (MC) strategy, were used to compare the performance of the suggested chart with that of MA, EWMA, TEWMA, and mixed moving average-triple exponentially weighted moving average (MMTE) charts. In addition, the expected average run length (EARL) and expected median run length (EMRL) were also used to rate the overall results. Results of the study indicate that the MTEM chart surpasses competitor charts in detecting minor to moderate changes. The MMTE chart responds slightly slower than the proposed chart. Due to its smoothed and re-averaged structure, it may lose significant information. The MA chart worked better for greater shifts. Furthermore, the MTEM chart competency was applied to two real-world datasets, confirming its practicality.
Europe’s Energy Shift: From Fossil Fuels to Renewable Energy
Objectives: This study explores the transformation of energy consumption in Europe between 2002 and 2022, focusing on the declining role of fossil fuels and the increasing significance of renewable and nuclear energy sources. The study also considers how countries with varying levels of economic development adopt different energy strategies and how these strategies correlate with shifts in energy usage. A circular economy approach that includes energy recovery from waste and resource reuse is a complementary aspect of sustainable energy transitions. Methods/Analysis: The per capita energy consumption data were analyzed through decile classification and cluster analysis to group countries with similar energy profiles. To explore the relationship between GDP and energy use—both total and renewable—linear and exponential regression models were applied. Outlier countries with atypical consumption trends were excluded to improve model reliability. Statistical analyses were conducted using SPSS, and Excel was used to support the visualization process. Findings: Six distinct clusters of energy consumption patterns emerged. In lower- and middle-GDP countries, renewable energy use showed a stronger exponential correlation with GDP growth than total energy use. While fossil fuel dependence has declined across most countries, the pathways taken have been diverse. High-GDP nations such as Iceland and Norway have demonstrated unique, resource-driven strategies. Novelty/Improvement: This study introduces a novel methodological blend of decile-based classification and clustering to enable clearer cross-country comparisons of energy use. The results also highlight the importance of excluding statistical outliers to improve regression precision. By integrating insights relevant to circular economy principles, the findings contribute to designing more effective and regionally adapted energy transition strategies.
Performance Evaluation of Significant Feature for Interest Flooding Attack Detection on Named Data Networking
One of the internet architectures of the future that has advantages over the current system is Named Data Networking (NDN). However, Denial of Service (DoS) attacks, such as interest flooding attacks (IFA), can still disrupt the network. Detecting IFA attacks is crucial for preventing further damage. Several approaches to detection systems have been proposed, including a classification approach to detecting attacks with multiple detection parameters or features. However, the many detection system features that can be extracted from the network result in longer computation times for the classification algorithms. This research focuses on enhancing the detection of IFA by evaluating the features of the detection system and identifying significant features to improve detection accuracy and reduce computation time. We employed various feature selection algorithms, including information gain, wrapper naive Bayes, gain ratio, and correlation-based feature selection (CFS). The selected features are tested to detect attacks using several classification algorithms, including naive Bayes, random forest, J48, and Bayesian network. Our proposed method found only three essential features for detecting IFA from 18 features available, resulting in better detection accuracy and increasing by 47.8% the time to build the model. This study enhances NDN security while reducing computational cost, making real-time attack detection more feasible.
Adaptive Segmentation of Information Sequences for Machine Learning Modular Regression Models
The research objective is to construct an adaptive model for modular machine learning structures that improves the processing quality of information sequences. The novelty of the proposed methodology is that it can identify segments of an information sequence obtained using various methods and assign models with the best quality indicator values to subsequences. Classical methods allow tuning of the model to the entire data sample. The improvement consists of the proposed solutions that consider the inverse problem of forming segments of data sequences, such that their properties correspond to the processing model. The proposed methodology was tested on various models and datasets. Segmentation and assignment of regression models with the best characteristics to individual segments allow the reduction of the mean square error (MSE) and mean absolute error (MAE) to 8%. The findings show an opportunity to increase of 5-8% for weak LR, SVM, and GR models, while strong DT, CNN, ANN, ANFIS, and XGBoost models improve by 1-4% in scenarios with limited data. Segmentation enables more efficient training and reduces sensitivity to noise and outliers. The proposed solution allows the selection of segmentation strategies and model combinations considering local data properties. Its application enables the implementation of existing machine learning architectures to improve the quality of training and analysis of information sequences and increase adaptability, scalability, and interpretability.
Driving Mangrove Recovery: Community Engagement and Socio-Economic Shifts in Aquaculture Areas
Land-use change and recovery patterns of mangroves in the Tha Sak subdistrict, Nakhon Si Thammarat, Thailand, were examined utilizing multi-temporal Landsat images and socio-economic data from 1988 to 2023. Land use was classified through visual interpretation, and potential changes were predicted using a Markov chain model. The results showed a significant expansion of mangrove forests (1.11 km² to 9.10 km²), indicating a clear recovery. At the same time, the aquaculture area decreased drastically (from 25.69 km² to 8.79 km²), indicating a significant change in land use. The recovery of mangroves is primarily attributed to the cessation of aquaculture and the active involvement of the Tha Sak subdistrict's Small-Scale Fishermen Group, highlighting the success of community-based restoration. This study provides evidence of the critical role local communities play in bringing about positive environmental change and enabling Sustainable Development Goals (SDGs) 15: Life on Land from ecosystem restoration, SDG 14: Life Below Water for conservation of coastal areas, and SDG 11: Sustainable Cities and Communities for increasing community resilience. Involving local communities in mangrove restoration and preservation is key to long-term sustainability.
LRX: A Hybrid-based Real-Time Air Quality Index Prediction and Visualization Model
Accurately 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.
Integrated AI, IoT, and Blockchain for Enhancing Security and Traceability in Perishable Logistics
The 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.
Corrosion Performance of a Novel Aluminium 6061-Sea Sand Composite Under Electrochemical Method
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.
Deep Learning-Based Behavior Recognition for Group-Housed Pigs: Advancing Livestock Management with Segmentation Techniques
The increasing demand for sustainable, welfare-oriented livestock management necessitates innovative solutions for behavior monitoring, particularly in group-housed settings, where challenges such as animal density and overlapping bodies hinder traditional observation methods. This study introduces a Convolutional Neural Network (CNN)-based model enhanced with segmentation techniques to accurately classify behaviors among group-housed pigs, a context in which individual monitoring is crucial for welfare assessment, disease prevention, and production efficiency. By leveraging segmentation, the model isolates individual pigs in video footage, overcoming occlusion issues and significantly improving classification accuracy. This approach not only advances the analysis of animal behavior in dense environments but also aligns with the principles of innovation, promoting the adoption of AI-driven monitoring solutions in livestock management. In comparison with various models, YOLOv11m-augmentation achieved the highest mAP@0.5 score of 0.969 and a notable precision of 0.925. This CNN and segmentation-based method effectively identifies key behaviors, including eating, drinking, sleeping, and standing, with particularly high precision for behaviors most indicative of animal welfare. This research contributes to sustainable livestock practices by offering a scalable, cost-effective technology for real-time welfare assessment, potentially reducing labor requirements, enhancing farm management decisions, and promoting animal health. The study’s findings underscore the potential of integrating innovation principles with AI in agriculture, presenting a viable pathway toward sustainable livestock management practices that balance productivity with animal welfare.
Institutional Co-Evolution and Hybrid Regulation in the Digital Economy: A Case Study of BRICS Nations
This study investigates the institutional co-evolution associated with digitalization processes in BRICS countries, emphasizing the development of hybrid regulatory frameworks that integrate state intervention, platform-based self-regulation, and entrepreneurial institutional agency. The primary objective is to analyze how these frameworks operate within heterogeneous governance environments and address the sustainability challenges arising in emerging digital economies. Grounded in the theory of institutional co-evolution, the research applies a mixed-methods design, combining bibliometric mapping, comparative policy analysis, and multiple linear regression on cross-national panel data from Brazil, Russia, India, China, and South Africa (2018 - 2022). The findings demonstrate that increasing levels of digitalization and innovation are significantly correlated with reductions in environmental risks, while GDP growth remains positively associated with CO2 emissions; underscoring a structural tension between economic expansion and ecological resilience. To address this contradiction, the study proposes and empirically validates an Optimized Hybrid Model of institutional regulation, which improves sustainability indicators by 18.5%. The novelty of this research lies in the operationalization of institutional co-evolution within digital governance, offering a transferable policy model for flexible, adaptive regulation in complex, data-intensive economies. These results contribute to the advancement of institutional theory and provide actionable insights for the governance of transitional digital systems.
Unveiling the Power of Emotional Intelligence: A Dynamic Exploration of Its Impact on Proactive Decision-Making
In 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.
Microstructural and Elemental Characterization of TPU/Jute CNFs Nanocomposites via FESEM and EDX Analysis
This study aims to investigate the microstructural and elemental characteristics of thermoplastic polyurethane (TPU) nanocomposites reinforced with jute cellulose nanofibers (CNFs), with the objective of understanding the dispersion behavior and interfacial interactions within the polymer matrix. CNFs were extracted from jute fibers through a chemo-mechanical process involving alkaline treatment, acid hydrolysis, bleaching, and high-energy milling, followed by melt blending with TPU to fabricate nanocomposites at varying filler loadings (1–5 wt%). Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDX) spectroscopy were employed to analyze the surface morphology and elemental distribution of the nanocomposites. The FESEM results revealed that uniform CNF dispersion was achieved up to 4 wt%, beyond which noticeable agglomeration occurred. EDX analysis confirmed the successful incorporation of CNFs and identified performance-enhancing elements such as Si, Ca, Na, and Al in the reinforcement phase. These findings suggest that CNF content strongly influences microstructure and bonding quality, which are key factors for mechanical performance. The novelty of this work lies in its exclusive focus on microstructural and elemental characterization—providing essential insight into filler distribution and matrix compatibility—offering a foundation for optimizing sustainable, high-performance TPU/CNF nanocomposites for advanced industrial applications.
Odor Profiling of Blood Shells Using TGS Gas Sensor and PCA-SVM Analysis
Blood cockles (Andara granulosa) are among the most popular animal protein sources due to their rich nutritional content and high economic value. The storage period and temperature are two critical factors that significantly influence the freshness of blood cockles. One key indicator of blood cockle quality is the odor they emit. An unpleasant or inappropriate odor can indicate contamination or a decline in quality, posing potential food safety risks. However, conventional methods of odor quality testing are often subjective, require specialized skills, and may not always be reliable. To address the limitations of human olfaction, advancements in gas sensor technology, specifically gas array sensors (also known as the electronic nose), have been developed. This research aims to profile the freshness of blood cockles by identifying their odor under different storage conditions using electronic nose technology. The study used fresh blood cockle meat, which was stored under varying temperature conditions: at room temperature, in a cooler, and in a freezer. The storage periods for the samples were 1, 2, 3, 4, and 5 days. The samples were placed in sealed bottles and tested using a gas array sensor. The data collected from this process were in the form of voltage readings, which were analyzed using machine learning techniques, specifically Principal Component Analysis (PCA). The data were then classified using a Support Vector Machine (SVM) model. The study results showed that the gas array sensor successfully classified the odor profiles, with PCA explaining 93.83% of the variance in the data. The SVM model achieved an accuracy of 89.66% for PCA-reduced data and 91.44% for non-PCA data.
Innovation Adoption and Resistance of Functional Postbiotics: Consumer Intentions for Sleep and Mental Wellbeing
This study investigates the key factors influencing consumer purchase intention toward functional postbiotic beverages designed to enhance sleep quality and mental health. It focuses on perceived innovation characteristics (PIC), innovation resistance (IR), attitude, and the moderating role of product knowledge. Data were collected through a structured questionnaire from 400 health-conscious Thai consumers aged 18–65 with prior experience in functional foods. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships among PIC, IR, attitude, and purchase intention, including the moderating effect of product knowledge. The findings reveal that relative advantage, compatibility, and attitude positively influence purchase intention, while the claim skepticism barrier has a negative impact. Complexity and trialability were found to be non-significant. Additionally, compatibility significantly influences attitudes across high- and low-product-knowledge consumers. However, product knowledge did not moderate the direct relationship between PIC and purchase intention. Attitude emerged as a key mediator. This study contributes to innovation adoption theory by highlighting the roles of compatibility and attitude while introducing trust and claim skepticism as critical resistance factors. It offers actionable implications for marketers aiming to enhance consumer trust and align products with daily routines.
New Development Model of the Agro-Industrial Complex Under Green Economy Conditions
This study empirically examines opportunities and provides a conceptual rationale for developing a green economy model tailored to Kazakhstan’s agro-industrial complex (AIC). The research focuses on identifying effective strategies for sustainable agricultural development by combining environmental and economic approaches. The methodological basis includes the analysis of theoretical concepts in economic and mathematical modeling, identification of key factors influencing the sector, and the construction of a block structure to represent these factors. Forecasting models were developed to evaluate the efficiency of various strategic directions for transitioning to sustainable agriculture. The findings indicate that the most influential drivers of sustainable development include the expansion of green financial instruments, increased public and international investment, and the use of digital technologies for collecting and processing data. The forecast of key indicators of the green economy in Kazakhstan up to 2030 confirms the reliability and applicability of the proposed evaluation method. The main contribution of this research is the creation of an integrated model that connects theoretical frameworks with practical tools for managing ecological and economic performance in agriculture. The results can be applied in strategic planning, decision-making for the AIC, and academic programs focused on the green economy and sustainable development in Kazakhstan.
From Silos to Synergy: Collaborative Laboratories and the Transformation of Knowledge Production
The increasing societal importance of cutting-edge science and technology calls for a closer examination of public policies' influence on the evolving dynamics of knowledge production and transfer. This focus is especially pertinent in peripheral economies such as Portugal, where persistent structural challenges include the limited integration of highly qualified human resources within the economy. The purpose of this research is to investigate how the knowledge coproduction and transfer dynamics of ‘Collaborative Laboratories’ (CoLABs), a new form of intermediary organization in Portugal, differ from those of more traditional science-industry interface set-ups, in the Portuguese context. This research employed a deductive, quantitative, multiple-case, cross-sectional design, utilizing scientific publications as collaboration indicators and applying Social Network Analysis to map and analyze the knowledge coproduction and transfer networks of CoLABs in Portugal, comparing them to Technology Centers. The results reveal that CoLABs prioritize the creation of flexible collaboration networks and the broad coproduction and dissemination of knowledge. CoLABs are found to function as value-occupying hub organizations and serve as crucial bridging entities and are characterized by high connectivity, diverse collaboration, and cohesive research and innovation communities. The need for public agencies and CoLAB governance structures to devise strategies to enhance communication and collaboration within the CoLAB network is highlighted. This is the first study to investigate CoLABs as a new form of intermediary organization in Portugal, specifically examining how their knowledge coproduction and transfer dynamics differ from more traditional science-industry interface set-ups in the Portuguese context.
Employment Components and Ecosystem on Working Poor in an Emerging Economy
As an emerging economy, Uzbekistan is progressing rapidly in economic growth. The progress caused the rising number of working poor and poverty rate. Only a few studies focus on the impact on the working poor, especially in emerging economies. Employment components and ecosystems are critical to ensure a sustainable economy, stability, and growth. Information relating to the working poor is limited, and how the working poor navigate through challenges in employment is still unknown. The objective of the study is to explore the impact of employment and the ecosystem on the working poor in Uzbekistan. A quantitative approach was conducted through a face-to-face survey in fourteen states using simple random sampling. Data collected from 3298 respondents was then analyzed through descriptive analysis and multiple regression to investigate the relationship between variables. The findings revealed that employment components like opportunity, retraining, and income fairness are crucial to the working poor, as well as social infrastructure, taxes, and union support. This study contributes to the literature relating to poverty. The study also offers practical insights into how employment matters relating to the working poor, empowering policymakers and researchers to make informed decisions.
Stakeholder Engagement Based Moral Hazard Analysis Model in FPSO-Tanker Oil Transfer
This study aims to address moral hazard risks in FPSO-tanker oil transfer operations by introducing a semi-quantitative model rooted in stakeholder engagement theory. The model, named SEMHAM (Stakeholder Engagement-based Moral Hazard Analysis Model), incorporates four engagement indicators to calculate the total involvement percentage (PTI): Occurrence Frequency Value (OFV), Responsibility Weight Value (RWV), Critical Role Value (CRV), and Process Impact Value (PIV) to calculate the Total Involvement Percentage (PTI). This metric quantifies the behavioral influence of each stakeholder in the offloading process. Using operational data from 17 offshore zones based on Pertamina's 2023 report, eight primary stakeholder roles were evaluated using a weighted activity matrix. The findings indicate that FPSO Crew and Ship Crew possess the highest PTI scores, signifying greater control and potential risk, whereas administrative actors such as agents and port authorities were identified as lower-risk participants. The SEMHAM model facilitates risk classification and recommends appropriate digital oversight, including IoT-based monitoring, smart contracts, and role-based dashboards. This approach enables the integration of behavioral risk metrics into digital governance systems, thus supporting real-time operational monitoring. The model also demonstrates potential scalability to more complex offshore energy domains, such as LNG terminals and deepwater operations, enabling broader stakeholder governance beyond the current FPSO-tanker context.
Real-Time FPGA-Based ADAS Solution for Driver Drowsiness Detection and Autonomous Stopping
This study addresses driver drowsiness, a leading cause of traffic accidents, by developing a real-time Advanced Driver Assistance System that integrates biometric detection and autonomous vehicle control. The objective of this study is to enhance road safety through the early detection of drowsiness and automated intervention. The proposed system detects signs of drowsiness by monitoring facial and ocular features using a real-time video stream. Once a predefined threshold is exceeded, an audible alert is triggered. If the driver remains unresponsive, the system gradually reduces the vehicle’s speed and initiates an automated stop procedure. Methodologically, the system employs OpenCV for image processing and a convolutional neural network for lane detection and vehicle control. It is implemented on a high-performance hardware platform using field-programmable gate arrays programmed via Vivado High-Level Synthesis to ensure low-latency operation. The results confirm the system’s real-time capability, accuracy in drowsiness detection, and effective vehicle control under drowsy driving conditions. The system’s novelty lies in its combination of biometric monitoring, deep learning, and hardware acceleration to provide faster and more reliable intervention than existing Advanced Driver Assistance System technologies. This integration sets a new benchmark for proactive road safety measures.
Mediating Role of Digital Service Adoption in Enhancing Human Resource Management Capacity of SMEs
In the context of rapid digital transformation, small and medium enterprises (SMEs) increasingly recognize the necessity of adopting digital services to enhance their human resource management (HRM) capabilities. This study aims to investigate the mediating role of digital service adoption in improving HRM capacity among Vietnamese SMEs. The Resource-Based View draws upon the Technology-Organization-Environment framework; the research examines how leadership support, organizational readiness, policy mechanisms, perceived benefits, and organizational culture influence digital service adoption and HRM development. A mixed-method was employed, combining qualitative interviews with 30 digital transformation experts across five major cities in Vietnam and a quantitative survey of 1,000 SME managers from July 2024 to February 2025. Structural equation modeling was utilized to test 11 proposed hypotheses. The results demonstrate that digital service adoption significantly enhances HRM capacity and mediates the relationships between organizational factors and HRM outcomes. Leadership support is the strongest predictor of digital adoption and HRM capacity, followed by perceived benefits and policy mechanisms. These findings emphasize the importance of strategic leadership, organizational preparedness, supportive policies, and a digital-friendly culture in fostering successful digital transformation in HRM. The novelty contributes a novel integrated model bridging digital transformation and HRM theories and offers practical insights for SME managers seeking to strengthen workforce capabilities in emerging economies.
Extreme Value Model to Forecast PM2.5 Concentration Through a Non-Stationary Process
The objectives of this research were to develop a model to forecast and estimate the return levels for daily maximum PM2.5 concentrations in Thailand, applying Extreme Value Theory (EVT) with the Generalized Extreme Value (GEV) distribution under eight models for stationary and non-stationary process. This research utilized reanalysis data from the NASA EARTHDATA satellite, represented as grid points with a spatial resolution of 50 × 62.5 km, enabling the analysis of daily maximum PM2.5 concentrations across 176 grid points from January 1, 2009 to October 31, 2024. The analysis revealed that Model 2 (μ(t)=β0+β1t where σ and ξ are constants) is the most suitable model for five grid points, namely Sa Kaeo Province, Uthai Thani Province, Nakhon Ratchasima Province, Bueng Kan Province and Mae Hong Son Province, whereas Model 1 (μ, σ and ξ are constants) is suitable for the remaining 171 grid points. Estimating the return levels for return periods of 5, 10, 25, and 50 years showed that Northern Thailand had the most extreme daily PM2.5 concentrations, for all return periods especially Mae Hong Son Province. The results of this analysis can serve as valuable information to support decision-making for response planning in high-risk areas, aiding in efficient resource allocation and preventive measures.
Factors Influencing Female Managerial Performance: A Pilot Study for Model Validation
This pilot study examines factors influencing the performance of female managers by testing a structural model that integrates psychosocial and organizational dimensions. The model includes organizational culture, managerial power, discrimination, prejudice, insecurity, and family roles. Data were collected through a survey of 179 female managers in Hungary. As the sample is geographically limited, the findings should be interpreted with caution. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyze the relationships among variables. Results indicate that a supportive organizational culture enhances perceptions of managerial power, which positively influences leadership performance. Conversely, experiences of discrimination reinforce prejudices, while family-related obligations heighten perceived bias toward women in leadership. Insecurity was also found to negatively impact managerial performance. The model showed strong internal reliability and acceptable discriminant validity, supporting its use in further research. This study offers novel insights by jointly examining individual, organizational, and societal barriers within a unified framework. Beyond its theoretical contribution, the findings provide practical guidance for organizations and policymakers aiming to foster inclusive leadership environments and promote gender equity in the workplace.
From Teaching to Employability: The Cultural and Performance Pathways to Success
The current research examines the possible mediating and moderating effects of Teaching Efficacy (TE) and National Culture (NC) on the nexus of Readiness of Students (RS), Interactive Online Collaboration (IOC), Faculty Training (FT), and Policy Support (PS) and the ensuing results of Student Performance (SP), Job Employment (JE), Student Competency (SC), and University Reputation (UR). We have evaluated both the direct and indirect association between the stipulated constructs by utilizing Partial Least Squares Structural Equation Modeling (PLS -SEM) on a sample of 291 respondents who were sampled using structured questionnaires. The empirical evidence suggests that TE is a medium of connecting between RS, PS, and SP and therefore enhances its impact on JE, SC, and UR. Notably, the influence of SP on JE is statistically significant in case of concurrent TE activity (O for indirect path = 0.215, p<0.001). Similarly, mediation helped students score better on SC (O = 0.327, t = 6.261, p < 0.001) and UR (O = -0.065, t = 1.911, p = 0.028). A substantial direct correlation was found between RS and TE (r = 0.282, t = 4.175, p < 0.001). The outcome of the moderate analysis indicated that Organizational Culture exerted a strong influence, leading to a positive impact on the correlation between TE and SP (O = 0.087, t = 1.994, p = 0.023). In addition, Information Culture (IC) acted as a protective factor, moderating the relationship between RS and TE (O = -0.093, t = 1.945, p = 0.026). Taking TE as the main factor and cultural elements as moderators significantly improved the model's performance, demonstrating that student results and university reputation can be enhanced when there is strong teaching competence and a positive organizational environment within these institutions.
Ball Detection System for a Soccer on Wheeled Robot Using the MobileNetV2 SSD Method
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.
Modeling and Forecasting the Dynamics of BRICS Socioeconomic Integration in Context of Global Economic Fragmentation
This 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.
The Occupational Indemnity Insurance Modelling: Brighton Mahohoho XGBoost Probabilistic Automated Actuarial Reserving-Pricing-Underwriting
This paper introduces the IFRS 17-Compliant Brighton Mahohoho Probabilistic Framework for Inflation-Adjusted Frequency-Severity Modeling in Occupational Indemnity Insurance, integrating AI-driven actuarial methodologies for loss reserving, risk pricing, and underwriting. Objectives: The framework ensures IFRS 17 compliance while enhancing actuarial accuracy and operational efficiency. Methods/Analysis: A simulation-based dataset of policy, claims, premiums, inflation adjustments, and underwriting data is generated. Claim frequencies and severities are modeled using Poisson and Gamma distributions, with inflation adjustments incorporated into reserves. XGBoost is applied for Automated Actuarial Loss Reserving (ALR) and Automated Actuarial Risk Pricing (ARP), while a weighted average approach estimates Automated Actuarial Loss Reserve Risk Premiums (AALRRP). Findings: Model accuracy is validated through MAE, MSE, RMSE, residual analysis, and scatter plots. IFRS 17 metrics—Contractual Service Margin (CSM), Fulfillment Cash Flows (FCF), Risk Adjustments, and Liabilities—are simulated, with sensitivity analysis ensuring robustness. Policyholders are segmented into underwriting clusters, incorporating expenses, outgo, and revenue to derive the Automated Net Actuarial Underwriting Balance (ANAUB). Novelty/Improvement: This integrated AI-driven actuarial framework significantly advances IFRS 17-compliant pricing and reserving, offering enhanced predictive accuracy, regulatory alignment, and improved risk assessment in occupational indemnity insurance.