bluuebunny/crossref_metadata_2025 huggingface dataset:
Revolutionizing Financial Health Predictions: The Integration of GenAI and Advanced Machine Learning Techniques
Authors: Kasztelnik Karina , Campbell Steven
Published: January 2025

Integrating Generative AI (GenAI) and advanced machine learning techniques into financial health predictions represents a revolutionary approach to financial technology. While prior research has incorporated machine learning and artificial intelligence into financial analysis, GenAI has not yet been incorporated into financial models. Our comprehensive experimental study aims to bridge this gap by harnessing the advanced capabilities of Generative AI to improve predictive accuracy and model robustness. The distinctive contribution of this study lies in its utilization of Generative AI, which offers novel insights and methodologies that traditional machine-learning techniques do not provide. A key discovery of this study is the alignment of Generative AI with quantitative models, revealing the potential to identify fraud and financial difficulties that stakeholders should consider before making investment decisions. Moreover, the study proposes that a mixed-method approach could be beneficial for future research in risk measurement. These unique and novel findings highlight that traditional methods would not have been able to uncover such insights. This research provides robust and interpretable financial assessments and contributes valuable knowledge to financial technology, showcasing the innovative application of Generative AI in financial health predictions.
The Impact of Ethical Orientation and Personality on Virtual Collaboration Usage
Authors: Kirby Eric G. , Kirby Susan L. , Konopaske Robert , Giacalone Robert A.
Published: January 2025

This paper predicts the use of virtual collaboration using instant messaging applications in a university setting. It examines the impact of age, sex, ethical orientations, and bright and dark personality traits on the usage of virtual collaboration applications. A significant finding is younger students and women are more likely to use this type of application. While ethical relativity and egoism also predict usage, being oriented toward justice negatively relates to application utilization. In terms of personality, the bright measure of extroversion is highly significant as is the dark variable narcissism. Finally, there is a very strong negative relationship with psychopathy.
Competitive Advantage and Marketing Strategies in the Entertainment Industry: From DVD by Mail to Streaming Services
Authors: Musonera Etienne
Published: January 2025

Netflix, the world’s largest subscription online movie and TV show streaming company, faces more competition than ever due to changing technology and consumer preferences. In this study, we analyzed Netflix competitive advantage and marketing strategies, performed a situational analysis, and provided potential strategic alternatives for Netflix to be competitive in this fierce and dynamic global environment. This paper uses a case analysis approach to address and analyze marketing strategies in the entertainment industry in general and Netflix in particular. We recommend that Netflix invest heavily in innovation to stay ahead in this global saturated and mature market.
Accountability and Governance in Kenya's Sustainable Development Goals Implementation: A Developing Country Perspective
Authors: Gitahi Jesse
Published: January 2025

This study analyzes how accountability mechanisms and governance structures impact the achievement of SDG targets, providing a comprehensive review of Kenya’s progress, a developing country facing unique governance challenges. Specifically, the paper aims to define key concepts of accountability and governance within the Kenyan context, assess the frameworks in place for ensuring transparency and resource management, and evaluate their alignment with international best practices. A review of relevant literature was conducted, analysis of available reports on Kenya’s SDG progress, and case studies that highlight successes and failures in accountability and governance. While Kenya has made significant strides in some areas, gaps in governance and accountability continue to limit the full realization of the SDGs. Case studies reveal how governance reforms and partnerships with international organizations have yielded positive outcomes in certain SDG initiatives, yet systemic challenges persist. The paper concludes by offering recommendations for enhancing governance and accountability frameworks in Kenya. These recommendations have broad implications for other developing countries facing similar challenges.
Risk-Adjusted Returns of Ethical Companies
Authors: Gullett Nell , Haddad Mahmoud , Hatch Laura
Published: January 2025

This study examines the investment performance of companies recognized for their socially responsible behavior, specifically focusing on the long-time honorees of Ethisphere's World’s Most Ethical Companies list. The analysis includes 28 publicly traded organizations from sectors such as Technology, Financials, and Consumer Goods, with stock returns and sector Net Asset Values (NAVs) used to assess risk-adjusted performance. Key metrics include the Sharpe Ratio, Jensen Alpha, and Treynor Ratio, all of which measure returns relative to risk. Results show that while more than half of the companies underperformed their respective sectors, the overall ethical portfolio generated positive risk-adjusted excess returns, primarily driven by strong performance from the technology sector. These findings suggest that ethical companies may provide valuable investment opportunities, particularly in sectors like technology and finance.
Innovative Insights: Impact of Crypto News on Corporate Earnings Through GenAI Models With Bert Framework
Authors: Kasztelnik Karina , Campbell Steven
Published: January 2025

This study represents a pioneering investigation into cryptocurrency news's repercussions on publicly traded companies' corporate earnings. Leveraging advanced Generative AI (GenAI) models and the BERT framework for sentiment analysis, we meticulously integrated comprehensive data from the Financial Modeling Prep API to employ a rigorous event study methodology alongside advanced machine learning algorithms. Noteworthy insights were derived from the BERT model, shedding light on the rationales behind abnormal returns and facilitating an in-depth analysis of material and immaterial impacts. The study’s findings underscore the significant impact of both positive and negative cryptocurrency news on cumulative abnormal returns (CAR), particularly within firms deeply entrenched in crypto activities. Notably, deliberate news, including official announcements, exerts a more pronounced influence than unintentional market reactions. This innovative approach furnishes actionable insights for financial services, investment management, and corporate communication, providing a framework for enhancing predictive models, investment decisions, and risk management strategies.
Internet Access and Independent Contracting Through the 2010s: A Varied Tale How Uptake and Returns Differ Across Demographics, Income Levels, and Time
Authors: Murray Sheena
Published: January 2025

The internet led to the proliferation of platforms that connect workers to individuals and firms seeking assistance and significantly increased independent contractor rates across the income and skill spectrum. Consequently, communities that adopted the internet earlier or at higher rates may have higher rates of contracting. Using data from the FCC on mobile and broadband internet rates, I examine their relationship with business earnings reported to the IRS from 2010 to 2019. Local race and ethnicity characteristics interact with internet measures and show that counties with higher minority concentrations have a higher correlation between internet expansion and growth in independent contractors. However, they do not reap as high growth rates in contractor earnings as predominantly white counties. Further, results show the relationship between growth in independent contractor earnings shifts mid-decade, with declines in growth observed post-internet expansion in the latter half of the 2010s.
Expanding the Johari Windows to Describe Interpersonal Communication
Authors: McQuillen Jeffrey S. , Cunningham Cory B. , McQuillen Marcolfa Z.
Published: January 2025

During the past 40 years considerable focus has been placed on the Johari Window (Luft, 1984), a tool designed to understand the process of self-disclosure and to increase self-awareness of the conscious and unconscious knowledge of self. Typically, a single, four quadrant model is employed to achieve these goals. The present paper offers an expansion to the application of the original model. Rather than a single model representing two interactants, two interlinked windows are recommended. This change provides a unique window for each interactant participating in the communication process. The window for each interactant has three unique quadrants (i.e., Blind, Hidden, and Unknown) and a shared Open quadrant. The proposed modification to the original Johari Window model illustrate more clearly the transactional nature of self-disclosure and provide the means to quantify and analyze the reciprocal awareness resulting from self-disclosure. These benefits offer a more sophisticated basis for instruction and research.
First-Generation College Students With Disabilities: The Impact of Relationships With Institutional Agents on Their Journey
Authors: Bueno Sydney A.
Published: January 2025

Students with disabilities who are attending college are less likely to enroll in a four-year postsecondary program. Once enrolled, students with disabilities are also more likely to drop out of college and not complete their degree than their peers without disabilities. This qualitative study used in-depth interviews with ten first-generation college students with disabilities to discover if they had relationships with institutional agents that helped put them on the path to a four-year college. The results of this study suggest that first-generation students with disabilities can benefit from relationships with institutional agents.
Understanding Professional Learning Practices of Engineering Educators: Insights From the SPARK-ENG Program
Authors: Wang Xiong , McFeetors P. Janelle , Kim Mijung , Rose Kerry
Published: January 2025

To meet our institutional demands for enhancing engineering education, we developed a professional learning program to comprehensively support our engineering educators at our university. In this study, we investigated participants’ authentic professional learning experiences that emerged from their engagement with the program through interpretive inquiry, framed by complexity theory. Data analysis revealed that the program enabled participants to experience four-layered learning practices that functioned as learning systems—an evolving learning process. This process empowered them to transition from knowledge recipients to active and authentic educators. Our program provides a valuable reference for creating supportive environments for engineering educators’ professional learning.
Examining Social and Economic Dynamics: A Comparative Analysis of Varna and Ashrama Systems in Ancient India and Adam Smith’s Division of Labor
Authors: Shukla Sushma
Published: January 2025

This research paper presents a comparative analysis of social and economic dynamics, focusing on the Varna and Ashrama systems in ancient India and Adam Smith’s theory of the division of labor. Delving into the principles of the ancient Indian systems, namely Varna and Ashrama, the study examines their influence on social structures, labor allocation, and economic roles. Simultaneously, it explores Adam Smith’s influential theory on the division of labor during the Enlightenment era, emphasizing its conceptualization and implications for economic development. By comparing these distinct frameworks, the research seeks to unveil commonalities, disparities, and the enduring impact of these historical systems on societal organization, economic efficiency, and individual contributions. Employing a multidisciplinary approach, the paper contributes to a nuanced understanding of the historical roots of economic philosophies and their implications for social and economic dynamics.
Hedging Oil Shocks in Monetary Policy
Authors: Rith Daraboth
Published: January 2025

Significant fluctuations in crude oil prices draw attention from policymakers, academics, and practitioners. These fluctuations often arise from global demand changes, supply disruptions, or precautionary motives, prompting critical questions about monetary policy responses. Understanding the interplay between oil shocks and monetary policy requires examining central bank actions and their economic impacts. This study investigates monetary policy responses to oil shocks since the 1990s using Structural Vector Autoregression, Impulse Response Functions, and Variance Decomposition. These methods reveal dynamic relationships between crude oil prices, inflation rates, and monetary policy rates. The findings highlight distinct responses among countries. Major oil importers like the U.S. and China significantly raise policy rates in response to oil shocks, while Japan shows a more modest reaction. Among oil-exporting nations, Saudi Arabia and Canada respond swiftly and substantially, whereas Nigeria adopts an unconventional approach, loosening monetary policy after an oil shock. These variations underscore the complex interactions between oil prices and monetary policy globally.
Précis: Eco-Affectivity: Exploring Pathos at Life’s Material Interface
Authors:   Radboud University, NL, Oele Marjolein
Published: January 2025

In this Précis I provide a brief overview of my monograph E-Co-Affectivity, which combines biology, phenomenology, Ancient Greek thought, new materialisms, environmental philosophy, and affect studies. I briefly outline central concepts such as affectivity, interface, community, and the middle voice.
Affectivity, Continuity, and Separation
Authors:   Fordham University, NY, Paley Miguel
Published: January 2025

This commentary explores the issues of separation and continuity in Marjolein Oele’s E-Co-Affectivity. I begin by providing a summary of what makes the book unique and its important achievements before moving on to a critical discussion of Oele’s concept of temporality. There, I claim that an overemphasis on the continuity between an organic being and the material interfaces that constitute it might be responsible for essential confusions in the understanding of time. My commentary begins by using Bergson’s philosophy to distinguish two common views on time and then argues that in several instances, Oele’s descriptions of temporality are in fact describing space rather than time. As Bergson shows us, affect and emotion must be understood as qualities of experience rather than quantitative forms of presence. I then conclude by arguing that E-Co-Affectivity might benefit from further thinking about separation and auto-affectivity as essential features in the process of affectivity.
Protective Effects of Aronia melanocarpa Extract against Cyclophosphamide-Induced Delayed Toxicity on the Bladder
Authors: Çelik Hümeyra , Karabörk Şeyda , Şaylan Aslıhan , Çetinkaya Ayhan
Published: January 2025

Aim: Aronia melanocarpa is a red-purple medicinal fruit known for its therapeutic properties in the urinary system by anti-inflammatory effects with high antioxidant content. The aim of the study is to show the supportive effect of Aronia melanocarpa extract delayed toxicity on the bladder induced by cyclophosphamide (CYC) that an antineoplastic agent.
Material and Methods: In the study three groups were constituted control (n=7), CYC(urotoxicity group, n=7) and CYC+ARONIA(treatment group, n=7). 100 mg/kg CYC intraperitoneally were given to CYC and CYC+ARONIA groups and waited for 4 weeks to be created delayed toxicity. At the end of the 4 weeks, 200 mg/kg Aronia melanocarpa was administered 15 times by oral gavage every other different day to CYC+ARONIA group (1 month in total). Sacrification was performed and after serum and urine samples were taken, the bladder was released from the sphincter region with curved-tipped forceps. Bladder tissues were investigated histologically. P38 mitogen activated preotein kinase (P38 MAPK), total antioxidant (TAS) and oxidant (TOS) status were evaluated in serum and urine samples.
Results: In histology, histological damage in the bladder continued in the CYC group, while Aronia melanocarpa treatment caused healing in the bladder tissue in the CYC+ARONIA group. No difference was found between the groups in terms of P38 MAPK, TAS and TOS in serum and urine samples.
Conclusion: According to the experimental results, the fact that Aronia melanocarpa extract improves the histological damage caused by CYC in the delayed period, and the serum and urine findings were the same as the controls, brought up the therapeutic effect of Aronia melanocarpa in urotoxicity.
Taxation in Ukraine under martial law: organizational and legal
Authors: Isaieva Nataliya
Published: January 2025

The issue of proper legal regulation of the tax sphere and the activities of tax authorities in Ukraine becomes critically important under martial law conditions. This is due to the significant losses suﬀered by the Ukrainian economy as a result of Russian aggression, and the necessity to maintain the financial, economic, and social spheres in the state amid the objective need to finance the defense sector.

The article analyzes the organizational and legal foundations of taxation in Ukraine under martial law and identifies optimal directions for their improvement. It highlights that in conditions of martial law, when normative legal acts need to regulate urgent problems in the financial and tax spheres without the possibility of detailed calculations and corresponding research due to the high dynamics of social processes, the main directions of the state’s financial and tax policy should be the main guide for the relevant state bodies. The orientation of this policy should be determined both by the interests of the state and Ukrainian business, as its preservation and functioning during wartime is a critically important task for the state.

The article discusses the main directions of Ukraine’s financial and tax policy as defined in the National Revenue Strategy up to 2030, approved by the Government in 2023. Together with the updated Memorandum with the International Monetary Fund, this Strategy forms the foundation for the development and strengthening of the state’s revenues and the comprehensive tasks of state bodies for their implementation. Undoubtedly, this involves the need to adopt a number of normative legal acts for its proper implementation. It should be noted that the Strategy is designed for implementation both during and after the war and envisages quite extreme approaches to maintaining the economy, financial, and tax system of Ukraine in diﬃcult conditions. The article also addresses the criticism by experts of certain provisions of the Strategy and its justification.

However, a positive aspect of the Strategy is that it envisages making unpopular decisions, in particular regarding tax increases, only after creating the appropriate conditions, and during the Strategy’s implementation, necessary clarifications of its certain provisions will be made.

The Strategy includes measures to improve the quality of services provided by tax authorities and to refine the process of organizing and conducting documentary and factual inspections. The article emphasizes that under martial law, tax authorities must consider the specifics of their implementation for diﬀerent categories of taxpayers. Also, the complexity and detail of tax reporting under martial law should be minimal.

Key words: tax legislation, financial policy, tax policy, tax audits, State Tax Service of Ukraine, tax reporting, taxpayers’ rights, administrative court, tax administration.
Law as a game: gamiﬁcation approach to the space law reconstruction
Authors: Hurova Anna
Published: January 2025

The research aims to assess the feasibility of utilizing gamification to analyze problem issues surrounding the task of ensuring space sustainability. The author explore shared characteristics of law and games and examines how gamification intersects with important concepts in the law-making process. The law was analyzed from the perspective of the language game. Furthermore, based on the recent space law and policy documents, the interpretation of current space activity as an important law-making mechanism was highlighted.

The increasing interpretative nature of current space governance sources was presented as an argument to support the eﬀectiveness of gamification in space law for promoting sustainable space activity. These observations highlight the utility of using gamification for the reconstruction of space governance for ensuring space sustainability.

Key words: gamification, space governance and law, language games.
Partisan Alignments and Climate Policy: Reassessing Ideological Effects on CO₂ Emissions
Authors: Islam Muhammad Muinul
Published: January 2025

This study explores how left-right partisan orientations influence climate mitigation, focusing on per capita CO₂ emissions in 120 countries from 1990 to 2018. Employing advanced quantitative methods, including time-series cross-sectional analyses, and multiple measures of political ideology, it finds that left-leaning governments can be more effective at lowering emissions, although under specific socioeconomic and institutional conditions. Socioeconomic and institutional variations shape the extent to which ideological commitments translate into tangible mitigation. This underscores that partisan alignment alone is insufficient for sustained climate progress; broader structural contexts remain pivotal. These findings deepen our understanding of how political orientations interact with national settings to affect environmental performance. They highlight the conditional nature of ideology, guiding future research on how partisanship can support more effective, context-sensitive climate policies.
Integrating the LC into cortical computation. Current trends, challenges, and future directions.
Authors: Cato Dylan
Published: January 2025

In this review, I cover the role of the LC (Locus coeruleus) in the evolution of dynamic cortical states and the coupling of LC activity to global evolutions of the cortex in response to relevant environmental contingency and behavioral states. I examine the work done Predictive coding and the underlying representational geometries of predictions and error signals that exist across the cortex. Important to note; I do assume these geometries converge towards an optimized global state of the cortex along a given gradient. Furthering discussion, I review current work on the LC and high level representation in cortical networks, along with spatiotemporal dynamics of the system in relation to computational processing of distributed information.
Analyzing The Sales Cycle and Accounts Receivable at Pt Citra Pangan Indah Semarang
Authors: Martini S. L. , Yuni K. K. C.
Published: January 2025

The sales and accounts receivable cycles are key elements in trading companies. If any of these cycles encounter issues, it will impact the company’s profitability. Not only profitability, but the company's operations also rely on the smooth functioning of the sales and accounts receivable cycles. These cycles can be effectively managed when both internal and external factors of the company are well-handled. Employees, as part of the internal factors, must be capable of carrying out their duties and responsibilities. With strict supervision, external factors such as buyer stores will also adhere to their obligations in making payments to the company. The purpose of this research is to identify the issues within the sales and accounts receivable cycles at PT Citra Pangan Indah and how to resolve these problems. This study uses a qualitative approach with primary data collected from PT Citra Pangan Indah. The results indicate that the problems in the sales and accounts receivable cycles arose due to unresolved issues from previous years, which led to an increasing receivables balance. The frequent turnover of employees in a short period also contributed to weak internal control in the company. These problems were eventually addressed by PT Citra Pangan Indah, and a new sales and accounts receivable cycle was implemented to prevent the same mistakes from recurring
Analysis of Factors Affecting The Performance of Accounting Information Systems (A Study on Jakarta Motor Group)
Authors: Hartanto R. , Malinda O.
Published: January 2025

Jakarta Motor Group is one of the ahass groups that uses the Accounting Information System (AIS) in the company's work processes. This research was made to see all the factors that can affect the performance of accounting information systems in the Jakarta motor group. This research uses quantitative methods with descriptive research approaches and verification analysis. Programs, training and user education and user involvement have no influence on AIS performance. While user ability, formalization of system development and top management support have an influence on the performance of accounting information systems. Suggestions for the Jakarta motor group to involve users in the development of the system and provide training to AIS users so that they can use the system better.
Understanding Environmental Performance in Hospitality Industry: Role of Green Human Resource Management, Environmental Strategy, Environmental Legitimacy and Green Innovation
Authors: Arsawan I. Wayan Edi , Kazancoglu Yigit , Suhartanto Dwi , Koval Viktor , Brien Anthony , Jabar Nurul Asyikeen Binti Abdul
Published: January 2025

Literature indicates that environmental performance has received special attention due to the increasing awareness of business managers on its positive impacts on the economy, society and environment. However, few studies have been conducted to examine the drivers of green innovation i.e. green human resource management, environmental strategy, environmental legitimacy towards environmental performance in the context of hospitality industry especially in developing countries. This study aims to examine the relationship between green human resource management, environmental strategy, environmental legitimacy, green innovation, and environmental performance in the hospitality industry both in direct and mediation relationships. Data were collected from hotel and restaurant managers in Bali, Indonesia. Smart-PLS was used to examine the validity and reliability of the proposed structural equation modeling. The findings of the present study revealed that green human resource management significantly influences environmental strategy, legitimacy, green innovation and environmental performance. Furthermore, environmental strategy, legitimacy, and green innovation mediate the relationship between GHRM and environmental performance. This study contributes to theoretical and practical implications and provides valuable suggestions, especially for managers in the hospitality industry for environmental conservation.
Predicting the Total Export-import Volume of China's Economic Trade through Machine Learning
Authors: Zhu Qiuxia
Published: January 2025

By forecasting the total export-import volume of China's economic trade, valuable guidance can be provided for the formulation of relevant policies, thus holding significant practical importance. This paper first analyzed the current situation of the import and export of China's economic trade. Subsequently, key indicators such as gross domestic product (GDP) and producer price index (PPI) were selected. After eliminating irrelevant indicators through correlation coefficient calculation, seven remaining indicators were employed for research. Building upon the machine learning algorithm-support vector machine (SVM), an improved sparrow search algorithm (ISSA) was developed to optimize SVM parameters, forming the ISSA-SVM prediction method. Experimental validation was conducted using data from 2003 to 2022. The results revealed that the average time consumed by ISSA-SVM for forecasting was 1.742401 s, displaying a minimal difference compared to the SVM method. In terms of total volume prediction, the ISSA-SVM approach achieved a mean absolute percentage error of 0.02% and a root-mean-square error of 1068.25, surpassing logistic regression, back-propagation neural network (BPNN), and other methods. These outcomes verify the reliability of the ISSA-SVM method in total volume prediction, showcasing its practical applicability.
Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Prediction
Authors: Kumar Anil , Chawla Sonal , Mann Supreet Kaur
Published: January 2025

This research presents a novel approach named “Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Predictions". This approach overcomes the limitations of traditional techniques by considering multi-dimensional data, including users' past behaviour, buying patterns, and sentiments to enhance the rating predictions and recommendations. The proposed prediction approach incorporates users’ past behaviour i.e. ratings through the Collaborative Filtering technique. The users’ sentiments are included by implementing Hybrid Sentiment Analysis and the sequential buying patterns are considered through the Generalized Sequential Pattern Mining technique. The Hybrid Sentiment Analysis technique combines Lexicon-based and Deep Learning-based Sentiment Analysis methodologies for more comprehensive sentiment evaluation. The proposed Hybrid Rating Prediction System is evaluated using a standardized public dataset and standard evaluation metrics including Accuracy, Precision, Recall, and F1-Score. Therefore, the research study has three primary objectives. The first objective is to identify the existing recommendation techniques through a literature review. The second objective is to propose a hybrid approach that mitigates the limitations of traditional systems by incorporating multi-dimensional information about the user and items. The third objective is to evaluate, validate, and compare the proposed approach against existing state-of-the-art systems and possible hybrid systems. The results demonstrate that the proposed hybrid approach achieves an Accuracy of 79.79%, with significant improvements in Precision and F1-Score compared to existing systems.
A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance
Authors: Belacel Nabil
Published: January 2025

Classifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods based on feature partitioning and outranking measures. Our approach eliminates the need for prior domain knowledge by automatically learning feature intervals directly from the data. These intervals capture key patterns, enhancing adaptability and insight. To improve robustness, we incorporate outranking measures, which reduce the impact of noise and uncertainty through pairwise comparisons of alternatives across features. We evaluate our classifiers on multiple UCI repository datasets and compare them with established methods, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NNs), Naive Bayes (NB), and Nearest Centroid (NC). The results demonstrate that our methods are robust to imbalanced datasets and irrelevant features, achieving comparable or superior performance in many cases. Furthermore, our classifiers offer enhanced interpretability while maintaining high predictive accuracy.
Digital Hydraulic Motor Characteristic Analysis for Heavy-Duty Vehicle Traction
Authors: Zhang Hao , Wei Wenshu , Wang Hong , Zhang Yang , Liu Xiaochao
Published: January 2025

Hydraulic motors have been widely used in large-scale machinery such as ground heavy equipment and heavy-duty vehicles, ships, and so on because of their high-power drive capability. However, the driving device is confronted with constraints related to its size and weight. Typically, the hydraulic axial piston motor is preferred for its simplicity and efficiency. However, the oil distributor in traditional hydraulic motors faces significant challenges, such as evident oil leakage and power loss from the mating surfaces of the fixed oil distributor and rotating cylinder block. To enhance the reliability and performance of hydraulic motors employed in paper driving applications, this paper introduces a digital radial hydraulic motor used for heavy-duty vehicle traction. The motor is powered by an on-board pump station from which several on/off valves can distribute the hydraulic oil. This design effectively mitigates the performance degradation issues associated with friction and wear in traditional hydraulic motor oil distributors. The drive characteristics of the motor can be flexibly adjusted through the combination of valves. Our investigation into the motor’s design principles and parameter analysis is poised to make an indirect yet significant contribution to the optimization of heavy-duty vehicle traction systems. This paper delineates the application conditions and operational principles of the digital hydraulic motor, thoroughly analyzes the intricate topological interrelationships of its parameters, and meticulously develops a detailed component-level model. Through comprehensive calculations, it reveals the impact of configuration and flow valve parameters on motor efficiency. A simulation model is established for the purpose of verification. Furthermore, the influence of the flow allocation method on efficiency and pressure pulsation is examined, leading to the proposal of a novel flow allocation strategy, the efficacy of which is substantiated through simulation. In conclusion, this paper formulates critical insights to inform the design and selection of components for digital hydraulic motors. These findings may provide a feasible solution for heavy-duty vehicle traction application scenarios.
Carbon Footprint of Milk Processing—Case Study of Polish Dairy
Authors: Wróbel-Jędrzejewska Magdalena , Klepacka Anna M. , Włodarczyk Ewelina , Przybysz Łukasz
Published: January 2025

Sustainable milk processing is essential to minimize negative environmental impacts. The purpose of this study was to determine the carbon footprint (CF) of the production of milk products in an industrial plant in Poland. Annual production and technological processes were analyzed, and relevant parameters were determined, as well as the method of data collection according to the chosen method of analysis and the developed database. It was found that each process is a source of greenhouse gas (GHG) emissions and affects the CF of the product. The total carbon footprint of the production of milk products was 0.367 kgCO2eq/kg. The average GHG emissions associated with production came mainly from indirect emissions (electricity consumption) and accounted for 50% of the total emissions. The determined relationship between the CF and monthly production volume also allows production planning in the context of sustainability. An increase in the monthly production volume by about 12% results in a reduction in the carbon footprint by about 18%. Decarbonization of dairies is possible through the use of renewable energy sources. Determining the CF of milk processing is the first step toward reducing GHG emissions, improving the sustainability of the sector and aligning with global trends and regulations.
Multi-Genotype Rice Yield Prediction Based on Time-Series Remote Sensing Images and Dynamic Process Clustering
Authors: Li Qian , Zhao Shaoshuai , Du Lei , Luo Shanjun
Published: January 2025

Predicting rice yield in a timely, precise, and efficient manner is crucial for directing agricultural output and creating food policy. The goal of this work was to create a stable, high-precision estimate model for the yield prediction of multi-genotype rice combined with dynamic growth processes. By obtaining RGB and multispectral data of the rice canopy during the whole development stage, several bands of reflectance, vegetation index, canopy height, and canopy volume were retrieved. These remote sensing properties were used to define several curves of the rice-growing process. The k-shape technique was utilized to cluster the various characteristics based on rice growth features, and data from different groups were subsequently employed to create a yield estimation model. The results demonstrated that, in comparison to utilizing solely spectral and geometric factors, the accuracy of the multi-genotype rice estimate model based on dynamic process clustering was much higher. With a root mean square error of 315.39 kg/ha and a coefficient of determination of 0.82, the rice yield calculation based on canopy volume temporal characteristics was the most accurate. The proposed approach can support precision agriculture and improve the extraction of characteristics related to the rice growth process.
Correction: Mumtaz et al. A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging. Agriculture 2023, 13, 667
Authors: Mumtaz Sidrah , Raza Mudassar , Okon Ofonime Dominic , Rehman Saeed Ur , Ragab Adham E. , Rauf Hafiz Tayyab
Published: January 2025

Affiliation Revision [...]
Assessing the Economic Value of Carbon Sinks in Farmland Using a Multi-Scenario System Dynamics Model
Authors: Song Shixiong , Su Mingjian , Kong Lingqiang , Kong Mingli , Ma Yongxi
Published: January 2025

Exploring the economic value of carbon sinks in agricultural systems can improve the development of sustainable agriculture. However, there are few studies on the economic value of farmland carbon sinks from a systemic perspective. This study takes Zhejiang, China’s first common wealth demonstration zone, as an example, and quantifies the carbon sinks in farmland and their economic value. The driving mechanism is analyzed by using a system dynamics model. The potential value and management of farmland carbon sinks are discussed. The results show that from 2007 to 2021, the average annual carbon sinks in farmland of Zhejiang were 5.84 million tons, a downward trend. The annual economic value was CNY 149.80 million, a marked upward trend. A rational fertilization project is a win-win ecological and economical measure to enhance the carbon sinks in farmland. Artificially increasing the carbon price to 32% will help Zhejiang achieve the core goal of the common prosperity plan, bringing the urban–rural income gap below 1.9 in 2025. Achieving the economic value of farmland carbon sinks is a green way to narrow the urban–rural income gap. Our study indicates that the marketization of carbon sinks in agricultural land systems may be a very promising path to promote green agriculture.
Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea
Authors: Han Jeongho , Bae Joo Hyun
Published: January 2025

This study focuses on developing an hourly water level prediction model for small- and medium-sized agricultural reservoirs using the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) technique. The study area is the Baekhak Reservoir in South Korea, and various precipitation-related and reservoir water storage data were collected. Using these collected data, we compared widely used individual machine learning and deep learning models with the pipeline models generated by TPOT. The comparison showed that pipeline models, which included various preprocessing and ensemble techniques, exhibited higher predictive accuracy than individual machine learning and even deep learning models. The optimal pipeline model was evaluated for its performance in predicting water levels during an extreme rainfall event, demonstrating its effectiveness for hourly water level prediction. However, issues such as the overprediction of peak water levels and delays in predicting sudden water level changes were observed, likely due to inaccuracies in the ultra-short-term forecast precipitation data and the lack of information on reservoir operations (e.g., gate openings and drainage plans for agriculture). This study highlights the potential of AutoML techniques for use in hydrological modeling, and demonstrates their contribution to more efficient water management and flood prevention strategies in agricultural reservoirs.
Effects of Wheat Straw-Derived Biochar on Soil Microbial Communities Under Phenanthrene Stress
Authors: Wang Zhongyi , Li Jiawang , Kang Yuke , Ran Jie , Song Jichao , Jiang Muqin , Li Wei , Zhang Meng
Published: January 2025

The potential of biochar to mediate shifts in soil microbial communities caused by polycyclic aromatic hydrocarbon (PAH) stress in farmland, thus assisting in the bioremediation of contaminated soil, remains uncertain. This study introduced wheat straw biochars generated at 300 °C (W300) and 500 °C (W500) at varying levels (1% and 2% w/w) into agricultural soil contaminated with phenanthrene at 2.5 and 25 mg/kg. The aim was to investigate their effects on microbial community structure and phenanthrene degradation by indigenous microbes. Biochar application in both slightly (PLS) and heavily (PHS) contaminated soils increased overall microbial/bacterial biomass, preserved bacterial diversity, and selectively enriched certain bacterial genera, which were suppressed by phenanthrene stress, through sorption enhancement and biotoxicity alleviation. The abundances of PAH-degrading genera and nidA degradation gene were promoted by biochar, especially W300, in PHS due to soil nutrient improvement, enhancing phenanthrene biodegradation. However, in PLS, biochar, particularly W500, inhibited their abundance due to a reduction in phenanthrene bioavailability to specific degraders, thus hindering phenanthrene biodegradation. These findings suggest that applying wheat straw biochar produced at appropriate temperatures can benefit soil microbial ecology and facilitate PAH elimination, offering a sustainable strategy for utilizing straw resources and safeguarding soil health and agricultural product quality.
Key Intelligent Pesticide Prescription Spraying Technologies for the Control of Pests, Diseases, and Weeds: A Review
Authors: Ye Kaiqiang , Hu Gang , Tong Zijie , Xu Youlin , Zheng Jiaqiang
Published: January 2025

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, and make scientific decisions about pests, diseases, and weeds; formulate personalized and precision control plans; and prevent and control pests through the use of intelligent equipment. This study discusses key IPSS technologies from four perspectives: target information acquisition, information processing, pesticide prescription spraying, and implementation and control. In the target information acquisition section, target identification technologies based on images, remote sensing, acoustic waves, and electronic nose are introduced. In the information processing section, information processing methods such as information pre-processing, feature extraction, pest and disease identification, bioinformatics analysis, and time series data are addressed. In the pesticide prescription spraying section, the impact of pesticide selection, dose calculation, spraying time, and method on the resulting effect and the formulation of prescription pesticide spraying in a certain area are explored. In the implement and control section, vehicle automatic control technology, precision spraying technology, and droplet characteristic control technology and their applications are studied. In addition, this study discusses the future development prospectives of IPPS technologies, including multifunctional target information acquisition systems, decision-support systems based on generative AI, and the development of precision intelligent sprayers. The advancement of these technologies will enhance agricultural productivity in a more efficient, environmentally sustainable manner.
Performance Comparison of Cherry Tomato Ripeness Detection Using Multiple YOLO Models
Authors: Yang Dayeon , Ju Chanyoung
Published: January 2025

Millions of tons of cherry tomatoes are produced annually, with the harvesting process being crucial. This paper presents a deep learning-based approach to distinguish the ripeness of cherry tomatoes in real time. It specifically evaluates the performance of YOLO (You Only Look Once) v5 and YOLOv8 (with a ResNet50 backbone) models. A new dataset was created by augmenting the original 300 images to 742 images using techniques such as rotation, flipping, and brightness adjustments. Experimental results show that YOLOv8 achieved a mean average precision (mAP) of 0.757, outperforming YOLOv5, which achieved an mAP of 0.701, by 5.6%. The proposed system is expected to address labor shortages caused by population decline in rural areas and enhance productivity in cherry tomato harvesting environments. Future research will focus on integrating segmentation techniques to precisely locate cherry tomatoes and develop a robotic manipulator capable of automating the harvesting process based on ripeness. This study provides a foundation for intelligent harvesting robots applicable in real-world.
Ornamental Traits and Sensory Analysis of ‘Biquinho Vermelha’ Pepper Treated with Paclobutrazol
Authors: Morales Beatriz R. , Costa Lucas C. , Verruma-Bernardi Marta R. , Rodrigues Josiane , Sala Fernando C. , Finger Fernando L. , França Christiane F. M.
Published: January 2025

This study investigated the impact of paclobutrazol (PBZ) concentrations and application protocols on ornamental quality and consumer preference. PBZ was applied at five concentrations using three different protocols: drench at transplanting, drench 30 days after transplanting, and immersion. At commercial maturity, plants were assessed based on growth parameters related to height, canopy structure, fruit and leaf characteristics, and sensory attributes. The results indicated that PBZ treatments led to darker green leaves and, in general, the drench method, regardless of time application, was more effective in modulating plant growth compared to the immersion protocol. Conversely, the sensory analysis showed a greater preference among evaluators for plants treated with PBZ at 2.5 and 5 mg L−1 via drench at 30 days after transplanting (D30DAT) or through immersion (IM), even though IM was not associated with any of the ornamental quality parameters evaluated. Likewise, the application of PBZ by drench during transplanting is not recommended as the plants showed a reduced number and size of fruits, which affected the ornamental value of the plants and global preference. Therefore, PBZ applied by D30DAT at concentrations of 2.5 and 5 mg L−1 produced ‘Biquinho Vermelha’ pepper plants with attractive ornamental characteristics; and thereby, should be considered an alternative method for producers of potted ornamental plants.