bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Genetic Parameters for Milk Urea Nitrogen in Iranian Holstein Cattle Using Random Regression Models
Authors: Mortazavi Mehridokht , Zandi Mohammad Bagher , Pahlavan Rostam , Eskandari Nasab Moradpasha , Rojas de Oliveira Hinayah
Published: February 2025

Reducing nitrogen excretion in dairy cattle is a critical factor for improving the environmental sustainability of the livestock industry. This research aimed to estimate the genetic parameters over time for the milk urea nitrogen (MUN) trait in Iranian Holstein dairy cattle. Data from 347,639 test-day records of 52,219 first-parity Iranian Holstein dairy cows (spanning 2018 to 2023), were sourced from the Iranian National Animal Breeding Center. A single-trait random regression test-day animal model was used for the genetic evaluation of MUN. Three orders of Legendre orthogonal polynomials (ranging from 1 to 3) were tested to fit the fixed curve, additive genetic effects, and permanent environmental effects. Based on the AIC, BIC, and residual variances to compare the models, the third order was considered as the appropriate order for this dataset. The average heritability and repeatability of the MUN trait were estimated to be 0.027 and 0.081, respectively. The average estimates for additive genetic variance, permanent environmental variance, and phenotypic variance were 0.14, 0.28, and 5.17, respectively. The genetic trend analysis revealed that the MUN trait exhibited fluctuations across birth years (2016–2021), with an overall negative trend. Importantly, the average MUN levels remained within the desirable range of 13–16 mg/dL for Iranian Holstein cows across calving years from 2019 to 2023. Despite the low heritability estimates, the genetic parameters obtained in this study are valuable for improving MUN in Iranian dairy cattle. These findings provide critical insights for designing effective breeding programs aimed at reducing nitrogen excretion and promoting environmental sustainability in the dairy industry.
A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network
Authors: Bai Longpeng , Wang Kaiyi , Zhang Qiusi , Zhang Qi , Wang Xiaofeng , Pan Shouhui , Zhang Liyang , He Xuliang , Li Ran , Zhang Dongfeng , Han Yanyun
Published: February 2025

The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration.
Modeling Water Distribution Uniformity of Medium-Sized Sprinklers Using Artificial Neural Networks
Authors: Faria Lessandro Coll , Flores José Henrique Nunes , Fuga Erick Costa , Nörenberg Bernardo Gomes , Beskow Samuel , Oliveira Henrique Fonseca Elias de , Prado Giuliani do , Colombo Alberto
Published: February 2025

Artificial neural network-(ANN) simulation models have been successfully applied in various fields, including agricultural sciences. This study evaluated the applicability of an artificial neural network for predicting water distribution uniformity in medium-sized sprinklers under varying wind conditions. Using data from 74 field tests, the ANN was developed and trained through supervised learning to optimize its predictions. The water distribution patterns simulated by the ANN closely mirrored observed field data, with 50% of the test set classified as ‘optimal’ in the highest reliability category. Statistical analyses indicated that the ANN performance was not significantly influenced by operating pressure, wind speed, or direction, underscoring its reliability across diverse scenarios. Furthermore, the ANN performed robustly in estimating the Christiansen uniformity coefficient, with statistical indices demonstrating excellent performance (r = 0.9475, d = 0.9689, and c = 0.9181), classifying the results as ‘optimal’. Linear regression analysis further confirmed the model’s robustness, with a slope close to 1 (0.988 ± 0.053) and a mean absolute deviation of 4.33%, indicating high accuracy in the simulations. These findings suggest that the trained ANN provides an accurate and efficient alternative to extensive fields.
Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
Authors: Kozłowski Edward , Zimakowska-Laskowska Magdalena , Dudziak Agnieszka , Wiśniowski Piotr , Laskowski Piotr , Stankiewicz Michał , Šnauko Boris , Lech Norbert , Gis Maciej , Matijošius Jonas
Published: February 2025

Using the standardised SORT, the article analyses instantaneous energy consumption and recuperation processes in an electric bus. The test includes three scenarios: SORT 1 (heavy urban traffic), SORT 2 (mixed driving conditions), and SORT 3 (suburban routes), enabling precise assessment of the energy efficiency of vehicles while eliminating environmental variables. The recuperation system significantly enhances energy efficiency, though its effectiveness varies based on the driving scenario. Modelling methods were compared as follows: linear regression, KNN algorithms, and neural networks, achieving a high fit (R2 > 90%). While KNN and neural networks were better at reproducing nonlinearities, they indicated the need for additional variables and time delays to enhance accuracy. The article sets itself apart by incorporating predictive models and examining recuperation efficiency across various scenarios. It emphasizes the importance of combining SORT results with real operational data and developing adaptive energy management systems. The results indicate the potential for optimizing electric buses for public transport, including route planning and further improving recuperation technology, which can significantly reduce energy consumption and greenhouse gas emissions.
Resilience Analysis Grid–Rasch Rating Scale Model for Measuring Organizational Resilience Potential
Authors: Falegnami Andrea , Tomassi Andrea , Corbelli Giuseppe , Romano Elpidio
Published: February 2025

This paper presents a novel method for measuring organizational resilience by integrating the Rasch model into the Resilience Analysis Grid (RAG), providing a robust and objective tool for cross-sectional resilience studies. By treating the four cornerstones of resilience as abilities, Rasch’s model allows for an assessment that positions both the difficulty of the items and the organizations’ ability along a common scale. The requirement is the availability of a number of different organizations to be assessed. We employ a dataset generated through an artificial simulation and analyzed in a controlled environment, demonstrating the potential of Rasch-based resilience assessments to provide accurate, comparable, and scalable results in different organizational contexts. The traditional RAG is designed without a normative reference group, which makes it challenging to evaluate its results. The proposed model overcomes this limitation by offering a measurement scale on which different organizations can be placed without the need to use a normative group, facilitating the more consistent and timely monitoring of systems. This novel approach to quantifying resilience potentials highlights the transformative role of digital technologies in improving workplace safety and resilience. It advances resilience engineering and occupational health and safety practices in complex environments like manufacturing and industrial sectors.
Safety Design of Rotary Drilling Rig Mast Based on Multi-Condition Analysis
Authors: Yang Heng , Ren Yuhang , Yang Haorong , Xu Gening
Published: February 2025

To achieve both safety and a lightweight design for rotary drilling rig masts, this study proposes an optimization method incorporating safety evaluation constraints. The method employs the limit state method to validate the mast structure and uses fuzzy comprehensive evaluation to quantify safety performance metrics. The optimization objective is to minimize the mast’s self-weight, with design variables defined as the geometric dimensions of key cross-sections, while imposing constraints on the strength, stiffness, stability, and safety scores. The safety score utilizes fuzzy comprehensive evaluation and a weighted aggregation method, considering indicators such as strength, stiffness, stability, and fatigue strength. An improved Salp Swarm Algorithm is utilized to execute the optimization process. Engineering case studies demonstrate that the optimized design reduces the mast’s self-weight by 6.5% under safety constraints. Compared to designs without safety constraints, the material usage increases slightly by 7.3%, but the safety performance improves by 14.74%. The findings indicate that integrating safety evaluation constraints into the optimization process not only enhances the structural safety of the mast but also achieves a favorable balance between safety and economic efficiency. This approach provides a valuable reference for the safety-focused design of rotary drilling rig masts.
A Penalized Profile Quasi-Likelihood Method for a Semiparametric Varying Coefficient Spatial Autoregressive Panel Model with Fixed Effects
Authors: Tian Ruiqin , Xia Miaojie , Xu Dengke
Published: February 2025

This paper proposes a variable selection method for a semiparametric varying coefficient spatial autoregressive panel model with fixed effects based on a penalized profile quasi-likelihood method, which can simultaneously select significant variables in parametric components and nonparametric components without estimating fixed effects. With an appropriate selection of the tuning parameters and some mild assumptions, the consistency of this procedure and the oracle property of the obtained estimators are established. Then, we conduct some Monte Carlo simulations to assess the finite sample performance of the proposed variable selection method, and finally, we analyze a real dataset for further illustration.
Positive Definite Solution of System of Matrix Equations with X−k and Y−l via Coupled Fixed Point Theorem in Partially Ordered Spaces
Authors: Ali Aynur , Dinkova Cvetelina , Ilchev Atanas , Kulina Hristina , Zlatanov Boyan
Published: February 2025

We establish adequate conditions for the existence and uniqueness of solutions to systems of two matrix equations when the unknown matrices are raised to a power k∈[−1,1]∖{0}. The findings from coupled fixed points for ordered pairs of maps are used. Numerical examples are provided to illustrate the results shown. Some of the known results are the consequence of our acquisitions.
Comprehensive Analysis of Granzymes and Perforin Family Genes in Multiple Cancers
Authors: Mareboina Manvita , Bakhl Katrina , Agioti Stephanie , Yee Nelson S. , Georgakopoulos-Soares Ilias , Zaravinos Apostolos
Published: February 2025

Background/Objectives: Cancer remains a significant global health concern, with immunotherapies emerging as promising treatments. This study explored the role of perforin-1 (PRF1) and granzymes A, B and K (GZMA, GZMB and GZMK) in cancer biology, focusing on their impact on tumor cell death and immune response modulation. Methods: Through a comprehensive genomic analysis across various cancer types, we explored the differential expression, mutation profiles and methylation patterns of these genes, providing insights into their potential as therapeutic targets. Furthermore, we investigated their association with immune cell infiltration and pathway activation within the tumor microenvironment in each tumor type. Results: Our findings revealed distinct expression patterns and prognostic implications for PRF1, GZMA, GZMB and GZMK across different cancers, highlighting their multifaceted roles in tumor immunity. We found increased immune infiltration across all tumor types and significant correlations between the genes of interest and cytotoxic T cells, as well as the most significant survival outcomes in breast cancer. We also show that granzymes and perforin-1 are significantly associated with indicators of immunosuppression and T cell dysfunction within patient cohorts. In skin melanoma, glioblastoma, kidney and bladder cancers, we found significant correlations between the genes of interest and patient survival after receiving immune-checkpoint inhibition therapy. Additionally, we identified potential associations between the mRNA expression levels of these genes and drug sensitivity. Conclusions: Overall, this study enhances our understanding of the molecular mechanisms underlying tumor immunity and provides valuable insights into the potential therapeutic implications of PRF1, GZMA, GZMB and GZMK in cancer treatment.
A Compact Fluorescence System for Tumor Detection: Performance and Integration Potential
Authors: Ndabakuranye Jean Pierre , Raschke John , Avagiannis Preston , Ahnood Arman
Published: February 2025

Fluorescence-guided surgery (FGS) is an innovative technique for accurately localizing tumors during surgery, particularly valuable in brain tumor detection. FGS uses advanced spectral and imaging tools to provide precise, quantitative fluorescence measurements that enhance surgical accuracy. However, the current challenge with these advanced tools lies in their lack of miniaturization, which limits their practicality in complex surgical environments. In this study, we present a miniaturized fluorescence detection system, developed using state-of-the-art CMOS color sensors, to overcome this challenge and improve brain tumor localization. Our 3.1 × 3 mm multispectral sensor platform measures fluorescence intensity ratios at 635 nm and 514 nm, producing a high-resolution fluorescence distribution map for a 16 mm × 16 mm area. This device shows a high correlation (R2 > 0.98) with standard benchtop spectrometers, confirming its accuracy for real-time, on-chip fluorescence detection. With its compact size, our system has strong potential for integration with existing handheld surgical tools, aiming to improve outcomes in tumor resection and enhance intraoperative tumor visualization.
Emergent Aspects of the Integration of Sensory and Motor Functions
Authors: Florio Tiziana M.
Published: February 2025

This article delves into the intricate mechanisms underlying sensory integration in the executive control of movement, encompassing ideomotor activity, predictive capabilities, and motor control systems. It examines the interplay between motor and sensory functions, highlighting the role of the cortical and subcortical regions of the central nervous system in enhancing environmental interaction. The acquisition of motor skills, procedural memory, and the representation of actions in the brain are discussed emphasizing the significance of mental imagery and training in motor function. The development of this aspect of sensorimotor integration control can help to advance our understanding of the interactions between executive motor control, cortical mechanisms, and consciousness. Bridging theoretical insights with practical applications, it sets the stage for future innovations in clinical rehabilitation, assistive technology, and education. The ongoing exploration of these domains promises to uncover new pathways for enhancing human capability and well-being.
A Novel Semi-Supervised Method for Predicting Remanufacturing Costs of Used Electromechanical Devices Using Quality Characteristics
Authors: Hu Junying , Xu Huan , Zhang Ke
Published: February 2025

Remanufacturing cost is a key factor for making decisions on the remanufacturing of used electromechanical devices in the construction sector. Though, remanufacturing costs can vary significantly due to the diversity of quality characteristics, even for the same type of used electromechanical devices. To realize the prediction of the remanufacturing cost for used electromechanical devices relevant to construction, this paper proposes a semi-supervised remanufacturing cost prediction method based on quality characteristics. First, we establish a semi-supervised least squares support vector regression (SLSSVR) model. Then, a novel variable neighborhood search (VNS) algorithm is designed for SLSSVR parameter tuning and optimizing. To verify the performance of the VNS-SLSSVR, we provide three types of simulated examples and conduct a real case study on predicting the remanufacturing cost of used turbine worms. The experimental results show that the proposed methods are of high accuracy and reliability with a limited number of labeled samples and a substantial quantity of unlabeled ones.
A Comparative Analysis of the Spatial Design Perspective of Wayfinding: The Emergency Room as a Case Study
Authors: Haj-Saleh Ola , Çağnan Çiğdem
Published: February 2025

Medical buildings, particularly emergency rooms, can be challenging to navigate, leading to stress and time loss for users if spatial layouts are poorly organized. This study focused on the concept of wayfinding, particularly on oral and visual communication, which has been identified as a human factor influencing the wayfinding process. This study aimed to examine the spatial design phase of emergency rooms in terms of wayfinding. The study approach used quantitative and qualitative methods. A literature review and observations during field visits were carried out. During the field visits, the researchers considered the numbers of human factors in the emergency rooms. Space syntax software was used to analyze the architectural plans from the perspectives of (1) visibility and (2) connectivity, examining the location of human factors in the architectural layouts. A comparative study of three cases in the United Arab Emirates was performed. The following human factors were identified: receptions and two categories of nurse stations, namely, first-look nurse stations (FLNSs) and observation nurse stations (ONSs). The results demonstrate the need for a developmental phase regarding the spatial arrangements of human components, guided by a space syntax analysis, to enhance the wayfinding process in the three emergency room cases. This study provides various recommendations for the repositioning of the human factors in the three case studies: for case study “A”, one of the three human elements should be modified; for case study “B”, only one of the two human elements should be altered; and, for case study “C”, all three human components should be moved to an advantageous position based on the spatial data. The conclusion illustrates the feasibility of adopting a spatial analysis approach for emergency rooms in the early design stages.
Evaluation Study of Public Interaction Spaces for the Elderly in a Community—Taking the Railway Station Community in Jiaozuo City as an Example
Authors: Zhou Min , Zhu Nana , Peng Xueyi , Huang Jing , Ji Lijuan , Meng Xiangyun , Song Simin , Liu Simeng
Published: February 2025

Under the guidance of the “community pension” initiative and adhering to the people-centered principle, this study focuses on the public interaction spaces within eight communities in the central urban area of Jiaozuo City. It assesses the current state of these spaces and the needs of the elderly population. A comprehensive evaluation system, integrating both subjective and objective factors, is developed for this purpose. Utilizing the railway station community as an illustrative case, this paper delves into the interaction behavior characteristics of various elderly groups and proposes optimal design directions for various public interaction spaces, encompassing functional diversity, space aging adaptability, and construction refinement.
Exploring the Potential of Adjuvant CDK4/6 Inhibitors in Hormone Receptor-Positive Early Breast Cancer: A Consistent Approach for All
Authors: Li Jianbin
Published: February 2025

Background/Objectives: Hormone receptor-positive, HER2-negative breast cancer is the most common subtype, with endocrine therapy as the standard treatment. Despite the advancements in adjuvant endocrine therapy, recurrence remains a challenge, particularly in high-risk patients. Recent trials on cyclin D kinase 4/6 (CDK4/6) inhibitors in adjuvant therapy have shown promise in reducing early recurrence and improving survival. Methods: This review analyzes the clinical evidence supporting the use of CDK4/6 inhibitors, focusing on the NATALEE and monarchE trials, which demonstrate comparable efficacy and manageable safety profiles for ribociclib and abemaciclib. Results and Conclusions: Ribociclib, with its broader applicability and impact on the decision making for axillary lymph node surgery, may be the preferred option in high-risk populations. The review also addresses unanswered clinical questions and highlights the need for ongoing research to optimize the adjuvant therapy strategies.
K-Means Clustering of Hyperpolarised 13C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade
Authors: Horvat-Menih Ines , Khan Alixander S. , McLean Mary A. , Duarte Joao , Serrao Eva , Ursprung Stephan , Kaggie Joshua D. , Gill Andrew B. , Priest Andrew N. , Crispin-Ortuzar Mireia , Warren Anne Y. , Welsh Sarah J. , Mitchell Thomas J. , Stewart Grant D. , Gallagher Ferdia A.
Published: February 2025

Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading. Methods: This study explored the use of hyperpolarised [1-13C]pyruvate MRI (HP 13C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC) as a method to guide biopsy targeting and to reduce undergrading. Six patients with ccRCC underwent presurgical HP 13C-MRI and conventional contrast-enhanced MRI. From the imaging data, three k-means clusters were computed by combining the kPL as a marker of metabolic activity, and the 13C-pyruvate signal-to-noise ratio (SNRPyr) as a perfusion surrogate. The combined clusters were compared to those derived from individual parameters and to those derived from the percentage of enhancement on the nephrographic phase (%NG). The diagnostic performance of each cluster was assessed based on its ability to predict the highest histological tumour grade in postsurgical tissue samples. The postsurgical tissue samples underwent immunohistochemical staining for the pyruvate transporter (monocarboxylate transporter 1, MCT1), as well as RNA and whole-exome sequencing. Results: The clustering approach combining SNRPyr and kPL demonstrated the best performance for predicting the highest tumour grade: specificity 85%; sensitivity 64%; positive predictive value 82%; and negative predictive value 68%. Epithelial MCT1 was identified as the major determinant of the HP 13C-MRI signal. The perfusion/metabolism mismatch cluster showed an increased expression of metabolic genes and markers of aggressiveness. Conclusions: This study demonstrates the potential of using HP 13C-MRI-derived metabolic clusters to identify intratumoral variations in tumour grade with high specificity. This work supports the use of metabolic imaging to guide biopsies to the most aggressive tumour regions and could potentially reduce sampling error.
Current Insights into Nutritional Management of Phenylketonuria: An Update for Children and Adolescents
Authors: Anton-Păduraru Dana-Teodora , Trofin Felicia , Chis Adina , Sur Lucia Maria , Streangă Violeta , Mîndru Dana Elena , Dorneanu Olivia Simona , Păduraru Diana , Nastase Eduard Vasile , Vulturar Romana
Published: February 2025

Considering the fact that even today in the 21st century, diet is the basis of the treatment in phenylketonuria (PKU), this review aims to provide a comprehensive analysis of existing current data from the last 15 years on dietary treatment and its impact on nutritional status and quality of life to identify gaps in knowledge and offer insights into future directions for optimizing nutritional care in PKU. Dietary treatment for PKU has evolved over the years, and in order to optimize and standardize the care, European PKU experts developed guidelines useful for both professionals and patients and their parents. The current literature underscores the essential role of diet in both managing PKU and preventing obesity, but malnutrition in these children is a complex issue that necessitates a multifaceted approach. The literature emphasizes the crucial role of dietary adherence in managing PKU. Advancements in therapy offer the potential to reduce the challenges associated with dietary phenylalanine (Phe) restrictions. Maintaining adequate levels of essential nutrients in children with PKU and monitoring trace element intake and micronutrient levels are vital for preventing deficiencies and ensuring optimal growth and development. Overall, the literature highlights the importance of personalized treatment strategies. Conclusions. Effective management of PKU necessitates strict dietary control and personalized treatment to maintain optimal blood Phe levels. Continuous monito-ring, nutritional education, and adherence to dietary recommendations are critical components in achieving the best patient outcomes. Future studies should also explore innovative therapeutic modalities, including gene therapy and novel dietary strategies that consider the gut–brain axis, to enhance the quality of life and mental health for individuals with PKU.
Selection of Zinc Coatings Based on Corrosion Behavior and Environmental Impact Assessment
Authors: Wang Die , Gao Zhiyue , Wu Yaping , Du Baoshuai , Fan Zhibin
Published: February 2025

This paper presents a novel and effective approach for the rational selection of zinc coatings in industrial applications. The corrosion behaviors of three types of zinc coatings were investigated through salt spray testing in the laboratory. Corrosion failure characteristics of the coatings were analyzed using corrosion morphology observation, electrochemical analysis, corrosion product identification, and weight loss measurements. Additionally, the environmental impacts of the production processes for the three coatings were evaluated. Among the three coatings, the thermally sprayed zinc–aluminum coating exhibited the best corrosion resistance in the salt spray test, while the hot-dip zinc coating showed the poorest performance. The electrochemical characteristics of the coatings at various stages of corrosion were examined using polarization curves, revealing the changes in corrosion current and corrosion potential that corresponded to the failure progression of the coatings. The corrosion products of the thermally sprayed zinc–aluminum coating primarily included ZnO, Al2O3, Zn(OH)2, and ZnAl2O4 phases. In contrast, the corrosion products of the thermally sprayed zinc coating and the hot-dip zinc coating predominantly consisted of ZnO and Zn(OH)2 phases. Finally, the environmental impact indicators of the three coatings were assessed using the IMPACT2002+ method.
Displacement Current in Classical and Quantum Systems
Authors: Ferry David K. , Oriols Xavier , Eisenberg Robert
Published: February 2025

It is certain that electrical properties—whether slow (sec) or fast (nsec), even optical (fsec)—are described by Maxwell’s equations, and there are terms that depend on the rate of the change of the electric and magnetic fields. In particular, Maxwell’s equation for the curl of the magnetic field contains both the steady-state conduction current and a term depending upon the temporal derivative of the electric displacement field. The latter is referred to as the displacement current and is generally believed to have been included originally by Maxwell himself, although there is evidence it was earlier considered by Kirchhoff. Maxwell’s equations and Kirchoff’s circuit laws both are important over the wide range of frequencies with which electronics traditionally deal. Additionally, the displacement current is an important contribution to these in both classical and quantum mechanics. Here, the development of the displacement current, its importance in both classical and quantum mechanics, and some applications are provided to illustrate the fundamental role that it plays in the dynamics of a wide range of systems.
ChatGPT Integration in Higher Education for Personalized Learning, Academic Writing, and Coding Tasks: A Systematic Review
Authors: Naznin Kaberi , Al Mahmud Abdullah , Nguyen Minh Thu , Chua Caslon
Published: February 2025

The emergence of ChatGPT in higher education has raised immense discussion due to its versatility in performing tasks, including coding, personalized learning, human-like conversations, and information retrieval. Despite the rapidly growing use of ChatGPT, a dire need still exists for an overarching view regarding its role and implications in educational settings. Following the PRISMA guidelines, this study represents a systematic review of 26 articles exploring the use of ChatGPT in academic writing, personalized learning, and code generation. The relevant literature was identified through electronic databases, including Scopus, ACM Digital Library, Education Research Complete, Computers & Applied Sciences, Web of Science, and IEEE Xplore. Key details from each article were extracted and synthesized narratively to provide insights into ChatGPT’s efficacy in academic writing, personalized learning, and coding. The findings indicate that ChatGPT enhances tailored learning by adapting delivery methods to individual needs, supports academic writing through error detection and content refinement, and assists in coding by offering clarifications and reusable code snippets. However, there are concerns over its ethical implications, including the impact on academic integrity, overreliance by students on AI, and privacy concerns about data use. Based on these insights, this study proposes recommendations for the ethical and responsible integration of ChatGPT into higher education, ensuring its utility while maintaining academic integrity. In addition, the results are discussed based on the relevant learning theories to understand how students engage with, learn through, and adapt to AI technologies such as ChatGPT in educational contexts.
A Novice-Friendly and Accessible Networked Educational Robotics Simulation Platform
Authors: Stein Gordon , Jean Devin , Kittani Saman , Deweese Menton , Lédeczi Ákos
Published: February 2025

Despite its potential for STEM education, educational robotics remains out of reach for many classrooms due to upfront purchase costs, maintenance requirements, storage space, and numerous other barriers to entry. As demonstrated previously, these physical robot limitations can be reduced or eliminated through simulation. This work presents a new version of RoboScape Online, a browser-based networked educational robotics simulation platform that aims to make robotics education more accessible while expanding both the breadth and depth of topics taught. Through cloud-hosted simulations, this platform enables distant students to collaborate and compete in real-time. Integration with NetsBlox, a block-based programming environment, allows students at any level to participate in computer science activities. By incorporating a virtual machine for running NetsBlox code into the server, RoboScape Online enables scenarios to be built using the same syntax and abstractions used to program the robots. This approach enables more creative curriculum activities while proving that block-based programming is a valuable development tool, not just a “toy language”. Classroom case studies demonstrate RoboScape Online’s potential to improve students’ computational thinking skills and foster positive attitudes toward STEM subjects, with especially significant improvements in attitudes toward self-expression and creativity within the realm of computer science.
Assessing the Role of Renewable Energy in the Sustainable Economic Growth of the European Union
Authors: Okunevičiūtė Neverauskienė Laima , Dirma Virgilijus , Tvaronavičienė Manuela , Danilevičienė Irena
Published: February 2025

The adoption of renewable energy sources offers significant economic and sustainability benefits. These sources—solar, wind, hydro, biomass, and geothermal—are crucial for transitioning to a sustainable, low-pollution energy model. Key benefits of renewable energy include job creation, reduced energy costs, decreased reliance on imported resources, minimized environmental impact, and long-term economic stability. Renewable energy also plays a vital role in achieving environmental and sustainability goals. By reducing dependence on imported energy resources, it enhances energy security. Countries investing in renewables lower their reliance on international supply chains and geopolitical risks, which is particularly relevant for European Union nations historically dependent on energy imports. Domestic renewable energy resources help stabilize prices and ensure a consistent long-term energy supply. This study aimed to assess the impact of renewable energy sources on the economy and sustainable development in the European Union. Data for all variables were collected from the Eurostat database. The unbalanced panel data sample consisted of 27 EU countries (N = 27), covering the period 2001–2022 (T = 22). The analysis and generalization of the scientific literature compared theoretical and practical statements, econometric models, and the least squares method. Here, the hypothesis that “the transition to renewable energy sources will have a smaller negative impact on economic growth when the country is more dependent on imported energy sources” was accepted, and this means that using renewable energy sources not only contributes to environmental goals and climate change mitigation but also provides various economic benefits, including job creation, lower energy prices, greater energy security, and long-term sustainability. It is essential in the transition to a cleaner and more sustainable economy.
Integrating Power-to-Methane with Carbon Capture (P2M-CC) for Sustainable Decarbonization in Cement Manufacturing
Authors: Dincă Cristian , Slavu Nela
Published: February 2025

The cement industry is one of the industries with the highest contribution to global CO2 emissions due to its energy-intensive processes and the use of fossil fuels. This study evaluates the integration of the P2M-CC (power-to-methane with carbon capture) concept in cement plants to reduce the carbon footprint of the cement produced. Three cement plant modernization scenarios, involving replacing natural gas with synthetic methane obtained by methanation of green hydrogen and CO2 captured from the industrial process, were analyzed. The results show that integrating the P2M-CC concept reduced the CO2 emission factor from 789 kg/ton cement (baseline scenario) to 85 kg/ton (in all analyzed scenarios). However, the initial investment costs increased significantly by 5.8 times in S2.2, 5.2 times in S2.3, and 13 times in S2.1, compared to the baseline scenario, by adding the necessary equipment for electrolysis, methanation, and CO2 capture. On the other hand, operating costs decreased the most in S2.2, by 42.2% compared to the baseline scenario, while in S2.1, they decreased by 10.9%, and in S2.3, they increased by 141%. The ideal scenario (S2.2) showed the best economic and environmental performance, with an LCOC of 71 €/ton of cement and an NPV of 2609 million €, due to excess electricity produced by the wind plants without additional investment costs. In contrast, the complete scenario (S2.1), characterized by significant investments in wind power plants and CO2 capture technology, showed an LCOC of 297 €/ton of cement, while the realistic scenario (S2.3), with high operational costs, had an LCOC of 333 €/ton cement. Using synthetic methane in all proposed scenarios reduced fossil fuel dependency and CO2 emissions.
A Review of Research on the Resource Utilization of Pyrolysis of Decommissioned Wind Turbine Blades
Authors: Ma Zhipeng , Qu Leying , Zhou Ping , Song Zhanlong , Zhao Xiqiang , Wang Wenlong
Published: February 2025

As a large number of wind turbine blades reach the end of their service life, effectively utilizing decommissioned blades has become a major challenge for the wind energy industry. Among existing treatment technologies, pyrolysis is considered the most promising. This paper, based on the Web of Science database, employs bibliometric methods to analyze research trends in this field. The results indicate a significant increase in the number of published papers, with China leading in publication volume and making a substantial contribution to the field’s development. Keyword analysis highlights the central role of pyrolysis technology. Therefore, this paper discusses the application of both conventional and microwave pyrolysis technologies in this field, outlining the advantages, disadvantages, processes, performance, and economic analysis of fiber recovery. Finally, the challenges faced by pyrolysis technology and future development trends are discussed.
Clever Hans in the Loop? A Critical Examination of ChatGPT in a Human-in-the-Loop Framework for Machinery Functional Safety Risk Analysis
Authors: Iyenghar Padma
Published: February 2025

This paper presents a first-of-its-kind evaluation of integrating Large Language Models (LLMs) within a Human-In-The-Loop (HITL) framework for risk analysis in machinery functional safety, adhering to ISO 12100. The methodology systematically addresses LLM limitations, such as hallucinations and lack of domain-specific expertise, by embedding expert oversight to ensure reliable and compliant outputs. Applied to four diverse industrial case studies—motorized gates, autonomous transport vehicles, weaving machines, and rotary printing presses—this study assesses the applicability of ChatGPT in routine risk analysis tasks central to machinery functional safety workflows, such as hazard identification and risk assessment. The results demonstrated substantial improvements: during HITL involvement and the subsequent iterations of risk assessment with expert feedback, a complete agreement with ground truth was achieved across all four use cases. ChatGPT also identified additional scenarios and edge cases, enriching the risk analysis. Efficiency gains were notable, with time efficiency rated at 4.95 out of 5, on average, across case studies. Overall accuracy (4.7 out of 5) and usability (4.8 out of 5) ratings demonstrated the robustness of the HITL framework in ensuring reliable and practical outputs. Likert scale evaluations reflected high confidence in the refined outputs, emphasizing the critical role of HITL in enhancing both trust and usability. The study also highlights the importance of prompt design, revealing that longer initial prompts improve accuracy, while shorter iterative prompts maintain usability without compromising efficiency. The iterative HITL process further ensures that refined outputs align with safety standards and practical requirements. This evaluation underscores the transformative potential of generative AI in functional safety workflows, enhancing routine activities while ensuring rigorous human oversight in safety-critical, regulated industries.
The Impact of Fermented Gamma-Aminobutyric Acid on Poultry Growth Performance Through Insulin-like Growth Factor-1 Activation
Authors: Ahmad Rafiq , Pandit Chetan , Yu Yu-Hsiang , Chen Wei-Jung , Cheng Yu-Chieh , Ali Ilyas , Cheng Yeong-Hsiang
Published: February 2025

Gamma-aminobutyric acid (GABA) is becoming more well-acknowledged for its ability to improve the development performance of poultry, particularly by interacting with the Insulin-like growth Factor 1 (IGF-1) pathway. This review emphasizes the importance of GABA in optimizing poultry production by thoroughly examining its biochemical characteristics, physiological roles, and molecular mechanisms of action. GABA is a crucial inhibitory neurotransmitter that not only regulates neuronal excitability but also optimizes metabolic processes critical for growth. Supplementing poultry feeds with GABA has been shown to enhance feed conversion efficiency, increase body weight, and bolster immune function. The interplay between GABA and IGF-1 is crucial, as GABA modulates IGF-1 levels and receptor sensitivity, promoting muscle development and overall metabolic health. This review emphasizes the importance of fermentation processes in producing bioavailable GABA, presenting promising implications for sustainable poultry farming practices. Further investigation into GABA supplementation strategies in poultry could lead to improved growth performance and healthier production systems.
Allergy to Thaumatin-like Proteins—What Do We Know?
Authors: Rydzyńska Magdalena , Bartuzi Zbigniew , Rosada Tomasz , Grześk-Kaczyńska Magdalena , Ukleja-Sokołowska Natalia
Published: February 2025

Thaumatin-like proteins (TLPs) are a class of allergens that are predominantly found in fruits. These proteins are involved in plant defense mechanisms and exhibit antifungal activity. TLPs are classified as pathogenesis-related proteins (PR-5) and are typically synthesized in response to biotic stress, such as pathogen attacks. Sensitization to TLPs can result in a broad spectrum of allergic reactions, ranging from localized symptoms, such as oral allergy syndrome, to severe manifestations, including anaphylaxis. Key allergens within this group include Mal d 2 (apple), Pru p 2 (peach), and Pru av 2 (cherry). The list of allergens belonging to the TLP protein group continues to expand with newly discovered molecules. Diagnostic approaches for TLP allergies remain limited. Allergen component-resolved diagnostics can detect specific TLPs. The epidemiology of TLP-induced allergies is underexplored, and further research is needed to elucidate the prevalence, natural course, and clinical outcomes of these allergic conditions.
Evaluation of Functional Quality of Maize with Different Grain Colors and Differences in Enzymatic Properties of Anthocyanin Metabolism
Authors: Li Jing , Chen Zhanqiang , Su Baojie , Zhang Yanan , Wang Zhiping , Ma Ke , Lu Boyu , Ren Jianhong , Xue Jianfu
Published: February 2025

Waxy maize (Zea mays L. sinensis kulesh) contains a lot of nutrients, and has a long history of cultivation and extensive consumption. In this study, six waxy maize varieties of white (J18 and W2000), yellow (J41 and J7), and black (J10 and J20) were selected as experimental materials, and the functional nutrients and the differences in anthocyanin anabolic pathways in maize kernels at 14, 18, 22, and 26 days after pollination were determined. The result show that the varieties and kernel development stages had significant effect on the carotenoid, soluble sugar, vitamin C, anthocyanin, and mineral element content. The black waxy maize varieties had a higher anthocyanin content, which plays an important role in maize kernel coloration, whereas the yellow and black waxy maize varieties exhibit a greater abundance of mineral elements. Furthermore, the phenylalanine content, as well as the activities of phenylalanine ammonia lyase (PAL), chalcone isomerase (CHI), dihydroflavonol reductase (DFR), and flavonoid 3-glucosyltransferase (UFGT), played a significant role in the anthocyanin biosynthetic pathway. In conclusion, the comprehensive functional quality of waxy maize decreased with the delay of kernel development stage, and the black waxy maize varieties demonstrated superior functional quality. The PAL and CHI played a primary role in the initial phase of anthocyanin accumulation, while UFGT gradually assumed control in the subsequent stages.
Advances in Processing Techniques and Determinants of Sweet Potato Starch Gelatinization
Authors: Yang Songtao , Hu Wentao , Qiao Shuai , Song Wei , Tan Wenfang
Published: February 2025

Sweet potato starch is an important source of starch in food processing, but its natural functionality is relatively limited, restricting its performance in certain applications. Gelatinized sweet potato starch, with enhanced structural and functional properties, has broader potential applications in food products. During the gelatinization process, the crystalline structure of sweet potato starch changes, making it suitable for use in various food formulations. Gelatinized sweet potato starch can be produced through techniques such as moist heat processing, extrusion, and spray drying, with the gelatinization effect influenced by factors such as moisture content and temperature. This review summarizes the gelatinization techniques and influencing factors for sweet potato starch, highlighting how structural changes under different conditions affect the quality of the final food products. Understanding these techniques and influencing factors helps optimize the gelatinization process of sweet potato starch, enhancing its application in foods such as noodles and baked goods. This knowledge provides theoretical support and practical guidance for the further utilization of sweet potato starch in the food industry.
Application of Stable Isotopes and Multi Elemental Fingerprints to Verify the Origin of Premium Chinese Hainan Bananas
Authors: Huang Yurong , Mei Hanyi , Zhang Yongzhi , Wang Mingyue , Huan Zhibo , Nie Jing , Rogers Karyne M. , Nuralykyzy Bayan , Li Chunlin , Yuan Yuwei
Published: February 2025

China is the world’s largest consumer and second largest producer of bananas. This strong domestic demand consistently provides a reliable income for Chinese banana growers. The geographical origin of food is usually associated with product quality and safety, and this is especially noted for Hainan origin-labeled bananas, which are grown offshore on China’s largest tropical island. Hainan banana is recognized as a premium variety within China’s banana market, but there have been recent impacts on branding, profits, and a reduction in income for banana farmers due to the fraudulent in-market substitution of non-Hainan bananas. In this study, stable isotope and elemental chemometric models were used to differentiate bananas grown in Hainan province (HN) from non-Hainan provinces (NHN). The results showed that HN bananas had a specific isotopic and elemental fingerprint compared to NHN bananas. Bananas sampled from HN and NHN regions showed significant differences in δ13C values (HN: −22.2‰ to −27.7‰, NHN: −22.3‰ to −24.3‰), Al content (HN: 0.00 mg/kg to 0.10 mg/kg, NHN: 0.00 mg/kg to 0.02 mg/kg), Na content (HN: 0.00 mg/kg to 0.09 mg/kg, NHN: 0.00 mg/kg to 0.07 mg/kg), and other elements (p < 0.05). Overall, 14 key variables reflecting climate and soil properties were selected from a group of 53 variables to improve a partial least squares discriminant analysis (PLS-DA) chemometric model. The discrimination accuracy of the test set increased from 84.60% to 90.93% after variable reduction. The use of stable isotopes and elements combined with PLS-DA models provided an effective method for distinguishing Chinese HN bananas from NHN bananas and would be useful as a screening or regulatory tool to confirm instances of origin fraud.