Emerging Science Journal (ESJ)
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Microstructural and Elemental Characterization of TPU/Jute CNFs Nanocomposites via FESEM and EDX Analysis
Full text linkThis 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
Europe’s Energy Shift: From Fossil Fuels to Renewable Energy
No full textObjectives: 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
LH-Moments Parameter Estimation of Weibull Distribution
No full textNatural disasters such as sudden floods, storms, severe snowfall, and droughts are major problems in the world. Generally the distributions of extreme values are heavy-tailed distributions, and an important heavy-tailed distribution is the Weibull distribution, especially for non-linear behaviors. Therefore, accurately estimation of the occurrence of disasters is required to deal with such situations in a timely and efficient manner. Several methods can be used to estimate the parameters, for example, moments estimate, maximum likelihood estimate, linear of moment, and high-order L-moments. The objectives of this article are to estimate the parameters of the four-parameter Weibull distribution with weak non-linear effects (W4DN) based on the LH-moments method, and to propose a new parameter estimation formula. The proposed formula is classified into two cases based on the coefficient of the second-order term (δ): Case 1, where the coefficient is positive (δ > 0) and Case 2, where the coefficient is negative (δ < 0). In both cases, the corresponding estimation formulas are derived βr and λrp for p=1, 2, ... and r=1, 2, ..., respectively. The parameter estimations (γ ̂,α ̂,δ ̂,ϕ ̂ and κ ̂) are then optimized using the augmented Lagrangian adaptive barrier minimization algorithm. These formulas provide a practical approach for parameter estimation that is essential for forecasting extreme events in various disciplines, including hydrology, meteorology, insurance, finance, and engineering
From Silos to Synergy: Collaborative Laboratories and the Transformation of Knowledge Production
Full text linkThe 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
Credit Allocation to Private Sector and Growth: An ARDL Analysis for a Transitional Economy
No full textThis study examines the role of credit allocation to the private sector in driving economic growth in Vietnam’s transitional economy. The primary objective is to evaluate whether bank credit allocation fosters sustainable output expansion or, conversely, produces diminishing returns when it surpasses optimal levels. Employing the Autoregressive Distributed Lag (ARDL) bounds testing framework, the analysis uses annual data for 1990–2024 and compares three specifications: credit to the private sector, aggregate credit to the economy, and credit to the state sector. Findings indicate a robust long-run cointegration between credit and output, but with a clear nonlinear pattern: private credit enhances growth up to a threshold of roughly 91% of GDP, beyond which its marginal effect declines. While capital formation and moderate inflation consistently support long-term growth, foreign direct investment exerts mainly short-term benefits, and state-directed credit shows no significant contribution. The novelty of this paper lies in extending previous studies through a longer time horizon, updated post-GDP-revision data, and explicit disaggregation between private and state credit. By highlighting threshold effects and sectoral inefficiencies, this research improves understanding of the credit–growth nexus in transitional economies and underscores the need to prioritize credit quality, efficiency, and SME access in credit policy.
JEL Classification: E51, G21, O47, P27
The Performance Impact of Management Control Systems: Assessing the Mediating Role of Organizational Culture
No full textThis research examines the dual-pathway impact of Management Control Systems (MCS) on Company Performance (CP) in China’s liquor industry, with a focus on the mediating role of Organizational Culture (OC). The research aims to address gaps in understanding how MCS enhances both financial and non-financial performance through cultural mechanisms, a critical yet underexplored dynamic in heritage-based industries. Employing a mixed-methods approach, the research analyzes survey data from 497 firms using Structural Equation Modeling (SEM) and mediation analysis to test three hypotheses: (1) MCS directly improves CP, (2) MCS fosters OC, and (3) OC mediates the MCS-CP relationship. Key findings reveal that MCS significantly boosts CP (β=0.438, p<0.001), while OC partially mediates this relationship (indirect effect β=0.249, p<0.001). The novelty lies in demonstrating how MCS transcends operational efficiency to shape cultural assets, which in turn drive competitive advantage. This research advances contingency theory by highlighting sector-specific adaptations, such as digital MCS tools balancing tradition with market responsiveness, and offers practical insights for integrating control systems with cultural stewardship in traditional industries
Retrieval-Augmented Generation Assistant for Anatomical Pathology Laboratories
Full text linkAccurate and efficient access to laboratory protocols is essential in Anatomical Pathology (AP), where up to 70% of medical decisions depend on laboratory diagnoses. However, static documentation such as printed manuals or PDFs is often outdated, fragmented, and difficult to search, creating risks of workflow errors and diagnostic delays. This study proposes and evaluates a Retrieval-Augmented Generation (RAG) assistant tailored to AP laboratories, designed to provide technicians with context-grounded answers to protocol-related queries. We curated a novel corpus of 99 AP protocols from a Portuguese healthcare institution and constructed 323 question-answer pairs for systematic evaluation. Ten experiments were conducted, varying chunking strategies, retrieval methods, and embedding models. Performance was assessed using the RAGAS framework (faithfulness, answer relevance, context recall) alongside top-k retrieval metrics. Results show that recursive chunking and hybrid retrieval delivered the strongest baseline performance. Incorporating a biomedical-specific embedding model (MedEmbed) further improved answer relevance (0.74), faithfulness (0.70), and context recall (0.77), showing the importance of domain-specialized embeddings. Top-k analysis revealed that retrieving a single top-ranked chunk (k=1) maximized efficiency and accuracy, reflecting the modular structure of AP protocols. These findings highlight critical design considerations for deploying RAG systems in healthcare and demonstrate their potential to transform static documentation into dynamic, reliable knowledge assistants, thus improving laboratory workflow efficiency and supporting patient safety
HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones
Full text linkSmartphone-based human activity recognition (HAR) is an important research area due to its wide-ranging applications in health, security, gaming, etc. Existing HAR models face challenges such as tedious manual feature extraction/selection techniques, limited model generalisation, high computational cost, and inability to retain longer-term dependencies. This work aims to overcome the issues by proposing a lightweight, homogenous stacked deep ensemble model, termed Homogenous Stacking Temporal Convolutional Network with Nu-Support Vector Classifier (HSTCN-NuSVC), for activity classification. In this model, multiple enhanced TCN networks with diverse architectures are organised parallelly to capture hierarchical spatial-temporal patterns from raw inertial signals. Each base model (i.e., TCN) incorporates dilations and residual connections to preserve longer effective histories, allowing the model to retain longer-term dependencies. Additionally, dilations can diminish the number of trainable parameters, reducing the model complexity and computational cost. The base models' predictions are concatenated and fed into a meta-learner (i.e., Nu-SVC) for final classification. The proposed HSTCN-NuSVC is evaluated using a publicly available database, i.e., UCI HAR, and a subject-independent protocol is implemented. The empirical results demonstrate that HSTCN-NuSVC achieves 97.25% accuracy with only 0.51 million parameters. The results exhibit the model's effectiveness in enhancing generalisation across individuals with better accuracy and computational efficiency. Doi: 10.28991/ESJ-2025-09-01-026 Full Text: PD
High-Sensitivity Toxic Gas Sensor Utilizing Photonic Crystal Fibers in the THz Spectrum
Full text linkSO2, HCN, and Cl2 gases are extremely toxic and can present significant health hazards even at minimal amounts, including respiratory and neurological systems. Timely identification aids in averting exposure and alleviating possible health risks, particularly in industrial and densely populated regions. Moreover, these gases can contribute to environmental pollution; thus, their monitoring is essential for human safety and environmental preservation. This specification recommends employing a photonic crystal fiber (PCF) to construct a terahertz octagonal core and curved air hole sensor for the detection of SO2, HCN, and Cl2 in the THz region. We routinely evaluate the recommended framework numerically, utilizing the entire finite element method. In terms of Cl2, the recommended sensor has a larger numerical aperture of 0.2909 and a superior sensitivity of 99.58%. Furthermore, this simulation yields a reduced effective material loss equal to 0.0020 cm-1 with a 3.094í—10-12dB/m confinement loss for this gas. This technology utilizes the distinctive interaction between THz vibrations and gas molecules, improving detection sensitivity at trace levels relative to other techniques. This type of sensor may have practical applications in chemical sensing, biosensing, and gas sensing. Doi: 10.28991/ESJ-2025-09-02-01 Full Text: PD
Key Drivers of Cruelty-Free Cosmetics: Mediating Role of Purchase Intention
Full text linkThis study aims to explore the factors influencing the purchase decisions of cruelty-free cosmetics, focusing on the mediating role of purchase intention. The objectives include evaluating the impact of internal factors (attitude, altruism, environmental knowledge) and external factors (social media, subjective norms) on purchase intention and decision-making. Structural equation modeling (SEM) analyzes the data from a survey of 199 participants to test the relationships between variables. Findings show that internal factors, especially altruism and environmental knowledge, have a stronger influence on purchase intention compared to external pressures from social media and subjective norms. The results indicate that personal beliefs play a vital role in shaping consumers' ethical purchase behavior. This study provides practical insights for cosmetic brands, suggesting that campaigns emphasizing environmental awareness and animal welfare can boost purchase intentions. The novelty of the research lies in differentiating the impacts of internal and external factors, highlighting that consumers prioritize personal values over social influences when making ethical purchasing decisions. The study also offers managerial implications, recommending that brands enhance consumer engagement through educational campaigns to foster long-term commitment to cruelty-free products. Doi: 10.28991/ESJ-2025-09-02-08 Full Text: PD