bluuebunny/crossref_metadata_2025 huggingface dataset:
Cloud-Based Inventory System with Quick Response (QR) Tagging for Technology Workshop
Authors: Pabelona Jr. Richard M. , Sermonia Crist Joseph G.
Published: February 2025

The study aimed to design and develop a Cloud-based Inventory System with Quick Response (QR) Tagging for Technology Workshop to manage inventory system technology workshop. Specifically, the study aimed to design and develop a cloud-based inventory system with QR tagging with the technical features: managed equipment and user data, accurate printed QR code, real-time QR code scanning for equipment, actual monitoring of reports, mobile applications with internet-synchronization. The study also aimed to test the functionality of the aforementioned technical features, evaluate the usability of the system in terms of system usefulness, information quality, interface quality, and overall usability and to develop a user’s manual of the system. The method of research used was developmental and descriptive. Test cases were conducted to evaluate the identified technical features of the system. Post-Study System Usability Questionnaire (PSSUQ) a standardized research instrument was used to evaluate the system’s usability. Result of the study revealed that the system’s technical features has passed all test cases conducted. The usability of the system revealed that the system usefulness, information quality, interface quality, and overall usability were rated strongly agree. The user’s manual will guide its target users to use the system effectively. The system can be used on workshops both in school and industry to make the inventory system fast, accurate and convenient with QR technology.
ROMANI/GYPSY WOMAN
Authors: Stefanovsky Voria , Costa Elisa
Published: February 2025

Reflection on the subjective dimension of being a Romani/Gypsy woman is the central axis that starts and guides this text. The proposal to decolonize and combat epistemological racism in academic practice is also carried over into the language used by the authors in the text, which includes poetry and the first person voice, and to the very process of intellectual construction of the same, which privileged the oral dialogue between two women who share common destinies and struggles and reflect on them. In the light of theories, but also of experiences, of scholars who also belong to the group to which they dedicate their work. Issues related to exclusion, anti-gypsyism, stereotypes and representations and the manifold subordination of Romani women are themes that permeate the discussions.
GOIÁS X SANTOS
Authors: Elias Garcia Tatiane
Published: February 2025

This study analyzes the song “Goiás e Santos”, composed by Benê, Benedito Mário Trindade and performed by the duo Os Piracans de Goiás, as an example of intangible heritage that connects oral memory, popular culture and regional identity. Using the methodology of analyzing musical lyrics, the article explores how the sports narrative transforms into cultural resistance, highlighting the role of country music in preserving collective memory and strengthening the identity of Goiás. Based on the approach of Critical Environmental History, the impacts of socioeconomic transformations on the musical culture of the Piracanjuba region are also discussed.
“O mais antigo gênero de ficção que se conhece”: Conto do vigário (Rio de Janeiro, 1885-1895)
Authors: Tortato Amanda Corrêa
Published: February 2025

Este ensaio examina o conto do vigário a partir de uma crônica de Machado de Assis publicada em 1895, na série “A Semana” da Gazeta de Notícias. O objetivo é analisar essa forma de trapaça para compreender seus simbolismos, especialmente nas relações de confiança e na economia monetária do Rio de Janeiro na transição do século XIX para o XX. A abordagem combina história social e antropologia, interpretando o conto do vigário como um ritual que reflete as ansiedades e transformações sociais do período. Por meio de uma leitura atenta da crônica e de outras fontes literárias e jornalísticas, argumenta-se que essas narrativas permitem acessar as ficções do dinheiro e das formas de trapaça, oferecendo uma chave para entender a cultura e as tensões impostas pela modernização e pela hegemonia do capital financeiro no início do período republicano.
Resenha
Authors: Santos Anna Beatriz Esser dos
Published: February 2025

Esta resenha apresenta a recente obra de Cristiano Barros, Como uma gota de água no mar: Michel de Certeau, a teologia e a história (2023). Produto de uma rica tese de doutorado, o livro explora o entrelaçamento entre história e teologia na obra do pensador francês, jesuíta e historiador Michel de Certeau. Discerne-se em seu desenvolvimento uma interessante inversão: de uma história pensada sob o paradigma da teologia para uma crítica da teologia a partir da história, revisitada teórica e politicamente à medida em que Certeau engaja-se no ambiente intelectual francês das décadas de 1960 e 1970 e na crise institucional e doutrinária vivida então pela Igreja Católica. Expressão central da abertura à diferença característica de Michel de Certeau, essa passagem se põe sob o signo do conceito de “hospitalidade”, que atravessa as fronteiras disciplinares para se tornar uma disposição epistemológica e ética.
Uma terceira idolatria? Crença e descrença em Deus entre os ameríndios segundo Alonso de La Peña Montenegro (1668)
Authors: Mota Bento Machado
Published: February 2025

Este artigo busca investigar o problema da crença e da descrença em Deus entre os indígenas sul-americanos a partir do pensamento do teólogo Alonso de La Peña Montenegro (1596-1687), Bispo de Quito entre 1653 e a sua morte. Em 1668, o ainda pouco conhecido bispo escreveu um manual e tratado intitulado Itinerários para Párocos Indígenas. Entre outros pontos, ele propôs dividir a idolatria em três categorias: a do menosprezo a Deus, a do culto supérfluo e a “terceira idolatria”, reservada a povos que eram miseráveis e ignorantes invencíveis em relação a Deus. A partir de dois conceitos teológicos fundamentais de sua época — a ignorância invencível e a idolatria —, este artigo pretende investigar de que maneira o autor encarou a ausência de conhecimento de Deus entre diversos povos da América do Sul, que ele classificava como “miseráveis” e “incapazes de filosofar por si mesmos”. A hipótese central é que De La Peña expandiu o conceito de idolatria para contemplar “casos” excepcionais dentro da concepção ortodoxa de crença na teologia católica Moderna.
Econometric Models for Optimum Exploration in Mining Industry
Authors: BULEARCĂ Marius , NEAGU Cornelia , Fistung Daniel , SIMA Cristian
Published: February 2025

This paper intends to present different models for the decision-making process in mining enterprises. Initially, the most significant models for determining the optimal drilling research programs for mineral deposits are presented. Hence, the paper first introduces four models dedicated to solving different problems raised by the needs of geological practice (such as the number of drillings that must be executed in certain areas to obtain maximum efficiency, to ensure a total given efficiency thus minimizing the execution expenses, and to accrue amounts of information for achieving optimum exploration in the mining industry), and the total investment available to acquire a maximum economic effect. The analysis continues by presenting a fifth model that helps find the probability of intercepting and discovering a useful mineral vein. The rest of the paper is dedicated to models regarding the exploitation of poor resources and how determining the optimal value of the parameters can be made. In doing this, the first model is based on the calculation of total costs related to the geological block, while the second model estimates the volume of accumulations of useful mineral substance so that the extraction is within acceptable cost limits. Finally, we may say that the obtained results and conclusions can be useful for further analysis. As such, to deepen the analysis related to these issues, in the following papers, other models that solve the problem of optimum investment programs in the mining industry to maximize the company’s profits will be introduced.
The Effectiveness of Artificial Intelligence in Judicial Decision-Making in Indonesia
Authors: Hukom Rafli , Martinus
Published: February 2025

The advancement of artificial intelligence (AI) technology has brought significant transformations across various sectors, including the judicial system. Countries such as China and the United States have adopted AI in legal decision-making, whereas Indonesia remains in the early stages of implementing this technology. While AI has the potential to enhance judicial efficiency, challenges related to regulation, transparency, and algorithmic bias raise concerns regarding the accuracy and fairness of legal rulings. Therefore, this study aims to analyze the effectiveness of AI utilization in Indonesia’s judicial system, identify key challenges, and propose policy recommendations. This research employs both normative and empirical approaches by examining AI-related regulations and conducting interviews with judges and technology experts in Indonesia. The findings indicate that 65% of judges in Indonesia remain skeptical about AI's role in legal decision-making. In contrast, in countries like China, AI adoption in the judiciary has improved case resolution efficiency by 30–40%. Additionally, a public survey reveals that while 55% of respondents believe AI can enhance the objectivity of legal rulings, 45% remain concerned about potential algorithmic bias. Furthermore, the study highlights that Indonesia lacks specific regulations governing AI in the legal system, which may hinder the widespread adoption of this technology. This research contributes to understanding the readiness of Indonesia’s judicial system in integrating AI and emphasizes the necessity of clear regulations and robust oversight mechanisms to ensure transparency and accountability. The findings are expected to serve as a foundation for policymakers in formulating comprehensive regulations to optimize AI implementation in the Indonesian judicial system
Contrasting the roles of regional anthropogenic aerosols from the western and eastern hemispheres in driving the 1980–2020 Pacific multi-decadal variations
Authors: Diao Chenrui , Xu Yangyang , Hu Aixue , Wang Zhili
Published: February 2025

Abstract. The multi-decadal variations in the Pacific climate are extensively discussed as being influenced by external forcings such as greenhouse gases (GHGs) and anthropogenic aerosols (AAs). Unlike GHGs, the potential impacts of AAs could be more complex because of the heterogeneity of spatial distribution during the past few decades. Here we show, using regional aerosol forcing large-ensemble simulations with the Community Earth System Model 1 (CESM1), that the increasing fossil-fuel-related aerosol emissions over Asia (EastFF) and the reduction in aerosol emissions over North America and Europe (WestFF) have remarkably different impacts on driving the Pacific circulations and sea surface temperature (SST) changes since the 1980s. EastFF excites a typical El Niño-like SST pattern in the tropical Pacific and weakens the climatological Pacific Walker circulation. WestFF induces a central Pacific (CP)-type El Niño-like SST pattern with warming in the middle region of the equatorial Pacific, which is consistent with the second leading empirical orthogonal function (EOF) pattern of the observation. Over the North Pacific region, EastFF, located at low to middle latitudes, favors an Interdecadal Pacific Oscillation (IPO)-like SST pattern (horseshoe-like SST pattern in the North Pacific) through a teleconnection pathway between the tropical and extratropical Pacific but is overwhelmed by internal variability evolving from a positive phase to a negative IPO phase. In contrast, WestFF, located at middle to high latitudes, strongly affects the North Pacific via a west-to-east mid-latitude pathway and induces extensive warming. The competing effects of the heterogeneously distributed regional aerosol forcings are expected to exhibit different patterns in the near future, especially the redistribution of aerosol emissions within the domain of EastFF (i.e., from East Asia to South Asia) and changes in aerosol composition. The complex future changes in anthropogenic aerosol emissions are likely to introduce more profound impacts of aerosol forcing on the Pacific multi-decadal variations.
Partitioning anthropogenic and natural methane emissions in Finland during 2000–2021 by combining bottom-up and top-down estimates
Authors: Tenkanen Maria K. , Tsuruta Aki , Denier van der Gon Hugo , Höglund-Isaksson Lena , Leppänen Antti , Markkanen Tiina , Petrescu Ana Maria Roxana , Raivonen Maarit , Aaltonen Hermanni , Aalto Tuula
Published: February 2025

Abstract. Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study investigated Finnish CH4 emissions from 2000–2021 using bottom-up and top-down approaches. We evaluated the ability of a global atmospheric inverse model CarbonTracker Europe – CH4 to estimate CH4 emissions within a single country. We focused on how different priors and their uncertainties affect the optimised emissions and showed that the optimised anthropogenic and natural CH4 emissions were strongly dependent on the prior emissions. However, while the range of CH4 estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis showed that the optimisation aligned the trends of anthropogenic and natural CH4 emissions and improved the modelled seasonal cycles of natural emissions. Comparison of atmospheric CH4 observations with model results showed no clear preference between anthropogenic inventories (EDGAR v6 and CAMS-REG), but results using the highest natural prior (JSBACH–HIMMELI) agreed best with observations, suggesting that process-based models may underestimate CH4 emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using an uncertainty estimate based on a process-based model ensemble for natural CH4 emissions seemed to be advantageous compared to the standard uncertainty definition. The average total posterior emission of the ensemble from one inverse model with different priors was similar to the average of the ensemble including different inverse models but similar priors. Thus, a single inverse model using a range of priors can be used to reliably estimate CH4 emissions when an ensemble of different models is unavailable.
Seeds of transformative learning and its pedagogical implications on a conference-based university course for environmental and geosciences
Authors: Siponen Joula Onerva , Salovaara Janne J. , Särkelä Karoliina , Ronkainen Inka , Veijonaho Salla , Vesterinen Veli-Matti , Barrio Isabel C. , Riuttanen Laura Irmeli , Lauri Katja Anniina
Published: February 2025

Abstract. Expertise in Arctic climate change requires not only a deep knowledge of the physical processes but also an awareness and understanding of the region’s complex socio-economic dynamics. In this study, we explore students’ learning experience on a university course where students of environmental and geosciences attend the Arctic Circle Assembly conference, introducing them to a wide range of stakeholders and viewpoints from geopolitics to Indigenous perspectives. With a qualitative inquiry, we study the students' sense of belonging and transformativeness of the learning process, and how those might influence the development of the students’ professional identity. Interviews in the event, written reflections of the students and post-course in-depth interviews reveal elements of the transformative learning process, in which the students’ sense of belonging played a role: lack of belonging to the expert community induced dilemmas and belonging to the student group enabled joined reflection. However, some dilemmas do not seem to lead into transformation. Therefore, as pedagogical implications of our findings, we highlight the importance of facilitation of critical reflection and discourse of the learner’s values and beliefs. Facilitation should consider students’ prior learning and background and include building of trust and belonging in the learning community, enabling the challenging reflections. We suggest that flexible pedagogies and approaches of transformative climate change education have potential to mould students’ professional identity and widen their perspectives on what it means to be a responsible scientist or expert in the Arctic context.
Assessing stratospheric contributions to subseasonal predictions of precipitation after the 2018 SSW from SNAPSI
Authors: Dai Ying , Hitchcock Peter , Butler Amy H. , Garfinkel Chaim I. , Seviour William J. M.
Published: February 2025

Abstract. The sudden stratospheric warming (SSW) event in February 2018 was followed by dry spells in Scandinavia and record-breaking rainfall over the Iberian Peninsula through the following March. Here, we study the role of the 2018 SSW in subseasonal to seasonal (S2S) prediction of the 'Wet Iberia and Dry Scandinavia' precipitation signal, using a new database of S2S forecasts generated by the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. This database includes three sets of forecast ensembles: a free ensemble in which the atmosphere evolves freely, a nudged ensemble in which the stratosphere is nudged to the observed zonal-mean evolution of the 2018 SSW, and a control ensemble in which the stratosphere is nudged to climatology. Each set of ensembles has two initialization dates: 25 January 2018 and 8 February 2018, both before the onset of the SSW on 12 February. We find that the 'Wet Iberia and Dry Scandinavia' pattern was captured by the late free ensemble (initialized at 8 February) which successfully predicted the stratospheric warming, but not by the early free ensemble (initialized at 25 January) which predicted a stratospheric cooling. Unlike the early free ensemble, the early nudged ensemble successfully captured the 'Wet Iberia and Dry Scandinavia' pattern, indicating that an accurate forecast of stratospheric variability can improve S2S predictability of precipitation. While the pattern of European precipitation anomalies is evidently connected to the stratosphere, we estimate that only roughly a quarter of the amplitude was expected given the stratospheric anomalies. Nonetheless, the likelihood of Iberian rainfall extremes comparable to or even stronger than the one observed doubles in the nudged ensemble, compared to the control ensemble. The increased likelihood in the nudged ensemble suggests that the weakened stratospheric polar vortex can increase the risk of Iberian rainfall extremes.
Remote sensing measurements during PaCE 2022 campaign
Authors: Tukiainen Simo , Siipola Tuomas , Leskinen Niko , O'Connor Ewan
Published: February 2025

Abstract. Continuous ground-based remote sensing measurements were conducted during the Pallas Cloud Experiment (PaCE) 2022 campaign. Remote sensing instruments, including two ceilometers (models CL31 and CL61, Vaisala Oyj), a Doppler cloud radar (model RPG-FMCW-94, RPG Radiometer Physics GmbH), and a Doppler wind lidar (model StreamLine XR, HALO Photonics), were deployed at Kenttärova, Finland, a measurement station that is part of the Pallas Atmosphere–Ecosystem Supersite. The instruments operated continuously throughout the entire campaign, with the exception of a few technical interruptions and brief maintenance periods. The PaCE 2022 remote sensing measurements provided vertical profiles of atmospheric targets with high temporal and vertical resolution, extending from the ground up to an altitude of 10–15 km depending on instrument. By combining the data from these instruments and a numerical weather prediction model, cloud micro- and macrophysical properties—such as ice water content, ice effective radius, and target classification—were retrieved using the Cloudnet methodology. The processed remote sensing data set complements the PaCE in situ measurements, providing valuable validation opportunities. The data set is available on the Cloudnet data portal at https://doi.org/10.60656/b3460d9d88d14fe6 (O’Connor and Hyvärinen, 2024).
GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds
Authors: Oakley David , Loiselet Christelle , Coowar Thierry , Labbe Vincent , Callot Jean-Paul
Published: February 2025

Abstract. The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the HDBSCAN clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds. This clustering algorithm is chosen because it does not require the number of clusters to be known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets, where they both prove capable of identifying folds. We also find that distinguishing folds from similar map patterns produced by topography is a major issue that must be accounted for with both methods. The unsupervised method has advantages, including the explainability of its results, and provides clearly better results in one of the two real-world test datasets, while the supervised learning method is more fully automated and likely more easily extensible to other structures. Both methods demonstrate the ability of machine learning to interpret folds on geological maps and have potential for further development targeting a wider range of structures and datasets.
CHANGES IN THE TEMPERATURE REGIME IN THE ZABAIKALSKY KRAI FOR 1976-2021 AND ITS POSSIBLE CONSEQUENCES FOR REGIONAL ECONOMY
Authors:   Institute of Natural Resources, Ecology, and Cryology, Siberian Branch, Russian Academy of Sciences, NOSKOVA E.V. , VAKHNINA I.L. ,   Institute of Natural Resources, Ecology, and Cryology, Siberian Branch, Russian Academy of Sciences
Published: February 2025

The analysis of average annual surface air temperatures for 1976-2021 in the Trans-Baikal Territory makes it possible to distinguish three periods with different warming rates. The most intensive temperature rise was registered in 2011-2021 (1.33°C/10 years). In 1976-1995, the increase was 0.88°C/10 years, while the average annual temperature was not rising during 1996-2010. For the entire analyzed period, the number of days with positive and large positive anomalies of average daily surface air temperature increased significantly, and the one with negative and large negative anomalies decreased. Possible consequences of the changes for the regional economic activity are shown.
SEA SURFACE WIND SPEEDS IN THE NORTH ATLANTIC EXTRATROPICAL CYCLONES ACCORDING TO MTVZA-GYA DATA
Authors:   Russian State Hydrometeorological University, ZABOLOTSKIKH E.V. , ZHIVOTOVSKAYA M.A. ,   Russian State Hydrometeorological University, YARUSOV K.I. ,   Russian State Hydrometeorological University, AZAROV S.M. ,   Russian State Hydrometeorological University
Published: February 2025

The paper analyzes the possibilities of using the algorithm for sea surface wind speed retrieval from the MTVZA-GYa Russian microwave radiometer measurements for studying the wind fields in the North Atlantic extratropical cyclones. On the basis of analyzing the ASCAT scatterometer wind fields for one year, a database of the North Atlantic extratropical cyclones has been created. Quasisynchronous (within a 2-hour time difference) fields of sea surface wind speed have been constructed from the MTVZA-GYa measurements for the areas of strong (>15 m/s) and storm (>25 m/s) winds in the cyclones. The wind fields retrieved from the AMSR2 and MTVZA-GYa data have been recalculated to a single regular grid. Daily and monthly mean biases and root-mean-square errors of retrieval have been estimated for different ranges of wind speed. Analysis of the differences has shown that the algorithm for the wind speed retrieval from the MTVZA-GYa data systematically underestimates the speed of high (>15 m/s) winds in extratropical cyclones.
LONG-TERM DYNAMICS OF SULFUR AND NITROGEN IN THE SNOW COVER OF MINSK
Authors:   Institute for Nature Management, National Academy of Sciences of Belarus, KAKAREKA S.V. , KOKOSH Y.G. ,   Institute for Nature Management, National Academy of Sciences of Belarus, KUDREVICH M.A. ,   Institute for Nature Management, National Academy of Sciences of Belarus
Published: February 2025

Trends in the levels of sulfur and nitrogen in the snow cover on the territory of Minsk over the 13-year period from 2007 to 2019 have been revealed. The results of observations at permanent sites in various parts of the city were used. Despite a significant interannual variability in the concentrations of sulfur and nitrogen in the snow, a general downward trend in the concentrations of sulfur and nitrogen combined with an upward trend in pH of precipitation was revealed. An average reduction (trend) in the concentration in the snow cover for 2007-2019 was 0.061 mg S/dm3 per year for sulfur, 0.009 mg N/dm3 per year for oxidized nitrogen, 0.003 mg N/dm3 per year for reduced nitrogen. It was found that the average rate of nitrogen reduction in the snow cover in Minsk over the period from 2007 to 2017 was comparable to the rate of reduction of these compounds at the European Monitoring and Evaluation Programme (EMEP) stations in Europe, while the average rate of oxidized sulfur reduction in Minsk exceeded that the respective parameter at the EMEP stations. The levels of the atmospheric inflow of sulfur and nitrogen have been assessed in terms of their spatial heterogeneity in the city, and their links with the level of air pollution and emissions have been described.
FREAK WAVES IN THE BLACK SEA RESULTING FROM NONLINEAR PROCESSES: FIELD MEASUREMENTS
Authors:   Shirshov Institute of Oceanology, Russian Academy of Sciences, SAPRYKINA YA.V.
Published: February 2025

According to instrumental measurements with the Spoondrifter Spotter buoy in the Black Sea, examples of abnormally high waves with light winds are given. Such waves are formed due to nonlinear processes with a possible increase in wave steepness because of growing winds and their interaction with a current. It is shown that in this case, a characteristic change in the wave spectrum occurs, which is accompanied by a discrete shift in the frequency of the spectrum peak to the low-frequency range. A case of a multi-cascade frequency downshifting of the spectrum peak (previously described in laboratory conditions) has been revealed. This case is considered as a characteristic one for interaction of waves with a current.
Asedios, fantasmas e impunidad. Una comparación desde lo espectral entre Vértigo de Alfred Hitchcock y La mujer sin cabeza de Lucrecia Martel
Authors: Vilches Pino Emilio
Published: February 2025

Este artículo realiza un análisis comparativo de las películas Vértigo (1958) de Alfred Hitchcock y La mujer sin cabeza (2008) de Lucrecia Martel, desde la perspectiva de los estudios espectrales. A pesar de sus diferencias en términos de época, contexto cultural y artístico, ambas películas exploran temas relacionados con la memoria, la muerte y la justicia, utilizando la figura del espectro como un medio para profundizar en la interioridad de los personajes y en las dinámicas sociales que los rodean. Basado en un enfoque interdisciplinario que integra la crítica fílmica y la teoría espectral de Jacques Derrida, Mark Fisher y Avery Gordon, el estudio examina cómo los fantasmas en estas películas no son apariciones literales, sino representaciones de traumas e injusticias que influyen en el presente. A través de técnicas de close reading y la incorporación de referencias teóricas relevantes, se ofrece una comprensión más profunda de la manera en que lo espectral opera en estas obras, subrayando su pertinencia para los estudios culturales contemporáneos.
Securing Cross-Border Digital Trade: AI Strategies for Eu-Africa Economic Growth
Authors: A. A. Anya, , K. A. Anya, , E. K. Anya, , A. V. Ishola,
Published: February 2025

The rapid growth of cross-border digital trade has transformed economic interactions between regions, with the European Union (EU) and African economies playing critical roles in this evolving landscape. As a driver of innovation, economic growth, and regional integration, digital trade presents significant developmental opportunities. However, the proliferation of cybersecurity threats undermines trust in digital transactions and poses challenges to the sustainability of cross-border digital trade. These risks are exacerbated by regional disparities in cybersecurity readiness, infrastructure, and regulatory frameworks, highlighting the need for robust, innovative approaches to ensure secure and resilient digital trade ecosystems. This paper explores the potential of Artificial Intelligence (AI) as a strategic tool to mitigate cybersecurity risks and foster secure trade between the EU and Africa. The study examines the defining characteristics and economic significance of cross-border digital trade, emphasizing its role in fostering economic partnerships between the EU and Africa. It highlights existing trade agreements, collaborative efforts, and the projected growth of digital economies in both regions. Despite these opportunities, cybersecurity threats, such as data breaches, ransomware attacks, and phishing scams, present significant economic and operational challenges. The study underscores the disparities in cybersecurity preparedness, particularly in the African context, and their implications for sustainable digital trade growth. The role of Artificial Intelligence in enhancing cybersecurity is critically analyzed, focusing on its applications in threat detection, predictive analytics, anomaly identification, and automated incident response. Drawing on successful case studies, the paper demonstrates the transformative potential of AI in addressing complex cybersecurity challenges and strengthening the resilience of digital trade infrastructures. By leveraging AI-driven solutions, the EU and African economies can establish secure digital ecosystems, fostering trust and enhancing economic collaboration. The paper concludes with targeted recommendations to enhance cybersecurity in cross-border digital trade. Policy measures, such as harmonizing cybersecurity regulations and promoting AI-driven research and innovation, are essential for building a cohesive security framework. Technological investments in AI infrastructure and the development of shared cybersecurity platforms are equally vital. Furthermore, capacity-building initiatives, including specialized training programs for businesses and governments, are necessary to ensure effective implementation. These recommendations aim to address the cybersecurity challenges of cross-border digital trade and advance a secure, inclusive, and sustainable digital economy between the EU and Africa.
Penerapan Metode Drill untuk Meningkatkan Kompetensi Siswa: Systematic Literature Review
Authors: Triani Yeni
Published: February 2025

This research is a literature review that aims to analyze the application of the drill method in improving student competence. The drill method, which involves repeated practice, has been widely recognized as an effective technique in strengthening students’ understanding and skills in various subject areas. In this review, various studies identifying the positive impact of the drill method on improving students’ cognitive, affective and psychomotor abilities are reviewed. Based on the analysis, it was found that the application of the drill method can improve long-term memory, accelerate mastery of material, and increase accuracy and speed in completing tasks. However, the research also showed the importance of variation in the application of drill to avoid boredom and to maintain student motivation. The findings suggest that while the drill method is effective, its success is highly dependent on the teaching context, student engagement, as well as proper time management. Thus, the application of the drill method needs to be adapted to the characteristics of the students and the learning objectives to be achieved.
Development and Acceptability if Worktext in Differential Calculus Among Students of University of Rizal System
Authors: Jose D. Elvena Jr.
Published: February 2025

There has been an increased emphasis on students to recognize significance of Calculus in many areas of life. Hence, the demand of instructional materials like worktext despite rapid development of new technologies continues. This research is focused on developing a worktext that enhance students’ solving ability, improving academic performance and creative thinking towards teaching material for this course. It is based on the outline prescribed by the Commission on Higher Education Memorandum Order (CMO) No. 12, series of 2008 (CHED, 2008). Developmental method of research was employed, and it utilized the ADDIE model (Analysis, Design, Develop, Implement, and Evaluate) to develop the instrument of instruction. Mathematics experts determined its acceptability using a validated evaluation instrument. Results showed that the worktext is acceptable in terms of content, relevance’ style and presentation, usefulness, and clarity using weighted mean. Furthermore, the performance of students using the worktext reflected a significant difference compared to those who did not use. Thus, it validates the successful development and integration of the worktext into the course.
Mathematics teachers and professors are hereby recommended to develop and design their supplementary materials for their students to develop and enhance their mathematics performance.
Results for a Novel Family of Vector Hemivariational-Like Inequalities in Hausdorff Topological Linear Spaces via ζ-η-Monotone Operators
Authors: Ehsan M. Hameed
Published: February 2025

This article's goal is to present a novel family of vector hemivariational-like inequalities in Hausdorff topological linear spaces (HTLS) using a formulation of the ζ-η -monotone operator. We get existence results for this novel class of vector hemivariational-like inequalities by applying the FKKM method and making appropriate assumptions about the nonlinear mappings that are being investigated. Enhancing both theoretical and practical comprehension of certain forms of inequalities, the solutions given in this work contribute to the development and enhancement of several corresponding solutions supplied by other writers.
Fluid Flow Numerical Study Considering the Impact of Deborah Number and Slip Condition
Authors: R. Praveena,
Published: February 2025

This article measures Maxwell fluid flow above a horizontal plate over a porous medium with the influence of MHD. Heat generation and radiation are left for discussion and explanation. The governing equations are converted into nonlinear ODEs by using similarity variables. The nonlinear ODE's answer is obtained using the renowned numerical Runge-Kutta method. The outcome of magnetic field (M), Deborah number (De), porosity (λ), radiation (Rd), Prandtl number (Pr), thermal slip (β), and energy generation (Q) are widely examined over graphs. The visualization and analysis of the consequences of modifying the model's parameters are conducted with the predefined values: Pr = 5, Rd = 2, M = 0.5, Q = 0.3, β = 0.5 and De = 0.6. In order to increase the values of M and Rd, this study attempts to determine the outcomes of thermal slip circumstances and examine the entropy generation and Bejan number. A good agreement is found when the results of the current work are compared with published work for code validation. The research's crucial conclusion is that the M affects both the expansion of thermal fields and momentum. On the other hand, the energy field accelerates due to heat radiation, and the Deborah number is influenced by the magnetic intensity.
AI-Powered Multi-Modal Routing Framework for Secure and Scalable Big Data Transfers in Area Networks
Authors: Sadaf Zama Hussain
Published: February 2025

The more & increasing reliance on big data transfer in area network generates a corresponding need for adaptive routing protocols to provide security for the data flow and to optimize the network's performance. Traditional methods of routing usually fail to address the dynamic change in threats and conditions of the network, which leaves them with poor performance and more vulnerability to breaches. This paper presents an AI-based framework that synergizes several advanced techniques to overcome the process limitations outlined above. Our approach starts with Multi-Agent Deep Reinforcement Learning (MADRL), wherein distributed agents collaborate for optimizing zone-specific routing while proactively mitigating threats. Federated Graph Neural Networks (FedGNN) enable decentralized threat detection, thus preserving data privacy and improving the accuracy of anomaly detection by up to 60%. Bayesian Neural Networks (BNN) improves routing decisions with uncertainty quantification, which helps in robust path selection even in dynamic environments. Hybrid Optimization Framework combines Bayesian methods with Genetic Algorithms to find and refine secure high-performance paths leading to near-optimal configurations with minimal vulnerabilities. Ensemble Learning with Stacked Models combines the outputs of all models to provide holistic routing decisions, improving decision accuracy by 20–30%. The integration of these techniques offers an all-rounded balance between throughput, latency, and security. The impacts gathered from our findings include a great improvement in throughput up to 60%, a reduction in breach up to 50%, and improved network resilience. The proposed framework, therefore, represents a new paradigm for secure adaptive big data routing that solves major challenges in contemporary area networks, paving the way for scalable intelligent network management solutions.
Strength of Vertex Irregularity of Some Product of Graphs
Authors: T. Manimaran,
Published: February 2025

“G is a connected graph with number of vertices . Let  be the set of vertices and  be the set of edges of . Let  be a function which assigns the integers {1, 2, …n} not necessarily distinct to the edges of . The weight of a vertex  denoted by  is the sum of all the labels of the edges which are incident with . Then the function  is called a vertex irregular labelling if all the weight of the vertices are distinct. The strength of vertex irregularity of the graph  denoted by  is the least positive integer n such that there exists a vertex irregular labelling . In this paper, we determine the strength of vertex irregularity of some edge corona product of graphs”.
e determine the strength of vertex irregularity of some edge corona product of graphs”.
Critical Review of Fault Tolerance Methods used in Wireless Sensor Networks
Authors: S.G.Mungale
Published: February 2025

This paper addresses the critical necessity for developing robust fault-tolerant and resilient systems within the domain of Wireless Sensor Networks (WSNs), a field where the reliability and continuity of data collection and transmission are paramount. Given the inherent challenges of WSNs, including resource constraints, environmental adversities, and operational failures, this work embarks on a comprehensive review of the existing methodologies aimed at identifying and mitigating faults. Notably, techniques such as Distributed Fault Detection, Energy-Efficient Fault Identification, and Machine Learning-based Fault Diagnosis are scrutinized for their efficacy in enhancing network resilience. Central to our investigation is a meticulously designed review process, which employs a multi-faceted evaluation framework to dissect the operational principles, strengths, and limitations of each identified method. By leveraging evaluation metrics such as fault detection accuracy, energy consumption, scalability, and recovery time, this study not only sheds light on the performance of existing solutions but also facilitates a nuanced comparison across different operational scenarios. The implications of this research are manifold. Firstly, it furnishes network designers and operators with a nuanced understanding of the trade-offs associated with each fault-tolerant technique, guiding the selection of optimal methods tailored to specific network requirements and constraints. Secondly, by highlighting the gaps and challenges in current approaches, this work sets the stage for future innovations in the design of more efficient, scalable, and resilient fault-tolerant systems for WSNs. Ultimately, the findings of this review hold the potential to significantly advance the reliability and efficiency of WSNs, thereby bolstering their applicability in critical applications ranging from environmental monitoring to disaster management and urban planning.
A Numerical Approach for Solving Fuzzy Differential Equation Using Enhanced Euler’s Methods Based on Contra Harmonic Mean and Centroidal Mean
Authors: A. Hari Ganesh
Published: February 2025

Objectives: Fuzzy differential equations (FDEs) are crucial for modeling dynamic systems in science, economics, and engineering due to their unpredictable nature and the need for numerical methods for precise solutions. The objective of this work is to develop improved Euler's techniques for numerically solving fuzzy differential equations (FDEs) in order to produce approximate solutions for problems that are too complicated to be stated exactly. 
Methods: This study proposes improved Euler's approaches for solving differential equations with fuzzy initial conditions, including Euler's Method, Modified Euler's Method, and Improved Euler's Method  employing Contra Harmonic Mean and Centroidal Mean. Based on Zadeh's extension concept for fuzzy sets, we extend Euler's classical methods to address this dependence problem in a fuzzy setting.
Findings: A numerical example that contrasts the suggested approaches with the traditional Euler's Method is provided. The efficiency of the suggested methods is shown by numerical solutions and their geometrical representations. Furthermore, the numerical example demonstrates the acceptable accuracy of the improved Euler's techniques. 
Novelty: In this research proposal, we sought to solve first-order differential equations utilizing two creative approaches: the Contra Harmonic Mean and the Centroidal Mean of enhanced Euler's approaches for fuzzy primary value. The suggested methods yield great results and are quick, accurate, and easy to use.
Convergence Analysis of S-iteration Process of Generalized Nonlinear Variational Inclusion Problem
Authors: Syed ShakaibIrfan
Published: February 2025

To obtain the solution of generalized variational inclusion involving  co-coercive operators, a proposal for -iteration has also been proposed and analyzed. Existence theorems for the solution of generalized variational inclusion are proved by using co-coercive and relaxed co-coercive mappings. Also, certain particular cases, along with their comparison with some methods, have been studied. Finally, we present a numerical example to exemplify and show the convergence of the suggested algorithm in support of our main result, which has been formulated by using MATLAB programming.
A study on Mitigation Strategies and Techniques to Reduce IoT Attacks through Cybersecurity and Block Chain Technology
Authors: Mala B A
Published: February 2025

The Internet of Things (IoT) has completely changed depending on how we engage with technology by enabling automation, monitoring, and cross-domain data sharing by linking billions of devices globally. IoT device development has brought out new security challenges, as these devices frequently lack strong security safeguards, leaving them open to hacker attacks. Even after every attack is thoroughly examined by enlightening the methods that cybercriminals employ to take advantage of weaknesses in IoT devices, network attacks still exist. This paper addresses the reasons behind the attacks, which include espionage, sabotage, privacy invasion, financial gain and data theft. In addition, it also assesses the effects of IoT attacks on people, businesses, and society at large, considering financial losses, reputational harm, service interruptions, data breaches, and safety risks. These effects highlight how crucial it is to protect IoT ecosystems and take preventative action to reduce the dangers associated with cyberattacks. In addition, a framework is provided that includes security best practices, mitigation strategies, technological controls, policy measures, and security awareness training for reducing IoT attacks. Apart from this, integrating Blockchain Technology into IoT security, enterprises/organizations can fortify their IoT infrastructure and protect themselves from ever changing cyber threats by understanding the motivations of attackers is also discussed.
Slip Effects on Unsteady MHDCASSON Nano Fluid Flow Over a Porous Stretching Sheet
Authors: Tanuja Rachakonda
Published: February 2025

A rigorous theoretical investigation has been conducted to elucidate the prominent characteristics of slip phenomena on magnetohydrodynamic (MHD) Casson nanofluid flow across a porous stretching sheet, wherein the effects of the Soret effect, thermal radiation, and chemical reaction are analyzed numerically. We consider the magnetic field applied and the time-dependent stretching sheet, which is characterized by a non-uniform velocity profile. For the purpose of transforming the governing partial differential equations into a system of coupled nonlinear ordinary differential equations, appropriate similarity variables are employed. The resulting transformed equations are subsequently resolved numerically utilizing the Runge-Kutta Fehlberg method in conjunction with the shooting technique. A detailed examination reveals how various physical factors influence the flow properties related to velocity, temperature, and concentration patterns, alongside their effects on the skin friction coefficient, Nusselt number, and Sherwood number, which are illustrated through a collection of insightful graphs and tables.
Integrating Machine Learning Approaches for Predictive Analysis of Heart Disease Risk Factors
Authors: R. Raja
Published: February 2025

As one of the leading causes of global mortality, heart disease underscores the critical need for precise and effective predictive models that can identify individuals at heightened risk. This research focuses on predictive analysis of heart disease, employing machine learning techniques that utilize a comprehensive set of clinical parameters, including age, sex, chest pain type (cp), resting blood pressure (trestbps), serum cholesterol levels (chol), fasting blood sugar (fbs), resting electrocardiographic findings (restecg), maximum heart rate achieved (thalach), exercise-induced angina (exang), ST depression (oldpeak), slope of the ST segment (slope), number of major coronary vessels (ca), thalassemia (thal), and target classification. Several machine learning algorithms are implemented and rigorously evaluated—namely Random Forest, Random Tree, SMOreg, Multilayer Perceptron, Linear Regression, and REP Tree. To measure the predictive performance of these models, key metrics such as the Correlation Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE) are utilized. The findings underscore the effectiveness of machine learning methodologies in predicting heart disease, emphasizing the critical role of algorithm selection and parameter optimization in enhancing predictive performance and accuracy.