bluuebunny/crossref_metadata_2025 huggingface dataset:
Fiscal Policies in the Era of Digital Technology Development: European Experience
Authors: Andrii Zolkover
Published: February 2025

Introduction: The increasing demand for efficient finance management and tax system improvement urges a detailed study of foreign practices and their effect on the Ukrainian economy. For instance, developing countries can learn from developed nations' experiences in developing effective strategies for financial stabilisation. It can also aid in the advancement of the Ukrainian economy. The study of fiscal practices and their outcomes in the EU and Ukraine can help to learn the experience and choose the optimum ways of reforming the country's fiscal policy. We are speaking of the changes in Ukraine's tax systems, budgetary policies, and public finances management.
Objectives: The policies applied in every EU member state, including Ukraine, are the primary focus of this paper, in addition to the taxation and the revenue collected therein. Mechanisms of how growth can be achieved in Europe are also considered, especially during periods of fighting.
Methods: The study relied on a literature review, a comparative assessment of the fiscal practices of the EU and Ukraine, an analysis of policies on public finances in times of war, a classification of the related constructs on reconstruction in war, and a regression analysis on public finance and management.
Results: At the same time, the main problem with implementing this plan is focused on the problem of taxation as it is becoming more complex. On the contrary, the preparation in Ukraine is making progress in simplifying the taxes and introducing gradual tax reductions. Especially during armed conflict, using a market-oriented approach in implementing expansionary or contractionary fiscal policy is problematic. Therefore, a necessary precondition for ensuring stability and restoring health is rejecting other financial and economic management methods. The need to build up fiscal reserves emerged within the framework to enhance fiscal policy under martial law conditions.
Conclusions: Even under martial law, sound fiscal policy is crucial for maintaining liquidity and enabling economic development to sustain change. The practised strategies are geared towards the fundamental objectives of achieving equity, providing a decent standard of living, and fairness while ensuring that the cost is acceptable. In this context, fiscal measures, in conjunction with social policies, can bring about equity and economic development in the future.
Exploring the Impact of 'Buy Now, Pay Later' on E-Shopper Motivation: The Role of Consumer Demographics in Egypt and Saudi Arabia – A Comparative Study
Authors: Hatem H. Garamoun
Published: February 2025

This study investigates the moderating role of demographic variables including age, income, marital status, and gender on the effect of Buy Now, Pay Later (BNPL) services on motivation of e-shoppers in Egypt and Saudi Arabia. With a sample of 524 respondents from both countries, data was collected through Google Forms in order to measure the influence of these factors on the use of BNPL services. The analysis of the data was carried out using a structural equation modeling which indicated that although BNPL strengthens e-shopper motivation in both regions, the model fit and effect were more visible in Saudi Arabia. Also, among age, income, and marital status that were significant predictors of e-shopper motivation, gender did not significantly affect the adoption and consequences of BNPL services. These results do not only add value to the knowledge of financial behavior in the digital marketplace, but also assist marketers and policy makers in creating tailored BNPL offerings for different consumer segments.
A Phenomenological Inquiry into IT Professionals' Perspectives on AI-Powered Cybersecurity Systems for Intrusion Detection
Authors: Hondor Saragih
Published: February 2025

Cybersecurity has become a critical field in protecting digital infrastructure, with increasing reliance on AI-powered systems for intrusion detection. However, the integration of AI into cybersecurity raises concerns about trust, effectiveness, and the human role in decision-making. Despite significant advancements, there is limited understanding of how IT professionals experience and interact with AI-driven intrusion detection systems in real-world settings. This study aims to explore these experiences and identify factors influencing trust and acceptance of AI in security contexts. Using a phenomenological approach, the research delves into the subjective perspectives of IT professionals, uncovering insights into their interactions with AI systems. Through in-depth interviews and thematic analysis, the findings highlight the dual role of AI as an assistant rather than a complete replacement for human decision-making, while also revealing challenges related to false positives and the need for human oversight. These results contribute to a more nuanced understanding of human-AI collaboration in cybersecurity, emphasizing the ongoing importance of human expertise. The study’s implications suggest that future research should focus on optimizing AI systems for better alignment with user trust and expectations.
ML-DLNN-based Heartbeat Detection System for Diagnosing Cardiac Arrhythmia Disease using ECG Signal
Authors: Sheerinsithara A
Published: February 2025

A condition when the heart rate is irregular either the beat is too slow or too fast is named Cardiac Arrhythmia (CA). Sudden Cardiac Death (SCD) could be caused by risky arrhythmia disorders. Early detection is required for reducing the fatality rate. In current years, several techniques have been developed for automatic Electrocardiogram (ECG) beat classification. Hence, a novel Memory layer with a Logistic BCM function-based Deep Learning Neural Network (ML-DLNN)-based CA disease detection based on fiducial point detection using ECG signal is proposed. Primarily, by utilizing the First Order Derivation based Kaiser Derived Bessel (FOD-KDB), the noises are removed from the input signal, and the isoelectric line is corrected. Afterward, the waves are detected from the obtained signal using PTA and it is decomposed using Deviation Measure based Ensemble Empirical Mode Decomposition (DM-EEMD). The fiducial point is accurately detected from the decomposed signal using CPA. From the fiducial point, the relationship betwixt each interval is computed and the features are extracted. Next, by utilizing Frechet Distribution-based Lemurs Optimization Algorithm (FD-LOA), the important features are selected. For classifying the conditions, both of them are given into the ML-DLNN. The proposed mechanism’s experimental outcomes are contrasted with the conventional approaches, which exhibit the proposed one’s higher detection accuracy
Advancing Machine Learning and Deep Learning Techniques for Predictive Analytics in Cyber Security and Data Science Applications
Authors: Ravi Kiran Kodali
Published: February 2025

The rapid evolution of cyber threats and the exponential growth of data-driven applications have necessitated the advancement of predictive analytics techniques in cybersecurity and data science. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for detecting, analyzing, and mitigating cyber threats while also enhancing decision-making processes in data science applications. This paper explores state-of-the-art ML and DL methodologies for predictive analytics, emphasizing their role in proactive security measures and intelligent data analysis. Traditional security approaches often struggle to keep pace with the increasing complexity and volume of cyber threats. The integration of ML and DL offers dynamic, adaptive, and automated solutions that can identify anomalies, predict potential attacks, and strengthen defensive mechanisms. Supervised, unsupervised, and reinforcement learning models have been widely adopted for various cybersecurity applications, including intrusion detection, malware classification, fraud detection, and threat intelligence. Meanwhile, DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers have demonstrated superior performance in feature extraction and pattern recognition, enabling advanced predictive analytics in cybersecurity. Beyond security applications, ML and DL play a crucial role in data science, enabling predictive modeling across diverse industries, such as healthcare, finance, and smart cities. Predictive analytics in data science leverages vast datasets to forecast trends, optimize decision-making, and drive innovation. However, challenges such as data privacy, model interpretability, adversarial attacks, and computational complexity must be addressed to ensure the reliability and ethical deployment of AI-driven solutions. This study presents a comprehensive review of the latest advancements in ML and DL for predictive analytics, examining their applications, benefits, and limitations. It also explores hybrid approaches that combine multiple techniques for enhanced accuracy and robustness. The paper further discusses emerging trends, including federated learning for privacy-preserving analytics, explainable AI (XAI) for model transparency, and quantum-enhanced ML for accelerated computations. Through extensive analysis and comparative evaluation, this research highlights the transformative potential of ML and DL in securing digital infrastructures and optimizing predictive analytics. The findings underscore the need for continuous innovation in algorithm design, data handling strategies, and cybersecurity frameworks to counter evolving cyber threats and maximize the utility of AI-driven predictive models. Ultimately, this study contributes to advancing the intersection of ML, DL, cybersecurity, and data science, paving the way for resilient, intelligent, and efficient digital ecosystems.
A Hybrid Bio-Inspired GRU Model with Multiheaded Attention for Predicting Smart Grid Stability
Authors: Kazi Kalim Nasir
Published: February 2025

Smart grids are a big part of modern energy systems because they make it easier to handle and distribute energy. Still, it's hard to keep the grid stable because working conditions, supply, and demand for energy are always changing. The Bioinspired (PSO) + GRU Model is a strong way to handle the complexity of grid stability prediction that is shown in this work. An algorithm based on biology and deep learning make the system very accurate and reliable. It uses Gated Recurrent Units (GRUs) to model time and Particle Swarm Optimisation (PSO) to choose which features to use. The suggested design uses the Smart Grid Stability collection, which has 60,000 records of grid properties such as delay, flexibility, and power. It also has a goal variable that shows how stable the grid is. Using tools like SMote can help even out the classes in a dataset, which makes sure that the model is trained fairly. PSO is used to pick the most important features, which lowers the number of dimensions and speeds up processing. GRUs can find sequential links in the grid's operational data using Multiheaded attention methods. This lets them make accurate predictions about stability. For example, SVC, LGBR, and ANN are not as accurate as this model, which has an F1-score of 99.1% and an accuracy rate of 99.5%. The confusion matrix study shows that the framework is even more stable, with low judgement mistakes. Thorough planning steps, such as normalisation, dimensionality reduction, data cleaning, and more, make sure that the model gets good inputs. This paper stresses how important it is for smart grid systems to use cutting edge deep learning and efficiency methods to solve real-world problems. The suggested method could be used to make predictions more accurate and could also be scaled up to be used in real-time grid tracking systems. In the future, this method could be used in other study areas that need model time data and big decisions.
NLP Based Protein Sequence Classification using CNN
Authors: Prativesh Pawar
Published: February 2025

The capacity to modify proteins to have better or novel functions has led to a significant increase in interest in protein redesign in the pharmaceutical industry. Natural selection, amplification, and mutation processes may now be simulated in the lab because to recent technological developments. However, a significant barrier remains since protein sequences are complex structures with a large number of potential mutations. Not all possible variations of a protein can be synthesised or evaluated. Protein prediction algorithms have shown very little success in predicting protein structures, despite advances in machine learning. Furthermore, most current approaches concentrate on a narrow set of traits associated with protein sequences.
This study offers a novel approach to categorise protein sequences using artificial intelligence (AI) and convolutional neural networks (CNNs). Our research aims to evaluate three distinct prediction models for efficacy. A combination of single-amino-acid and three-dimensional protein structure-based descriptors are used to train each model. To evaluate the accuracy of our forecasts, we employed a variety of evaluation metrics, encompassing both publicly accessible and proprietary datasets. The results demonstrate the remarkable effectiveness of Convolutional Neural Network (CNN) models trained using amino acid property descriptors in addressing the challenges related to protein structure estimation. For applications in the pharmaceutical industry, this renders them highly advantageous.
Integrating Graph-Based Features with CNI-VIF for Enhanced Botnet Detection in Network Traffic
Authors: Anagha Patil
Published: February 2025

The growing threat of social botnets demands advanced detection techniques to identify sophisticated malicious activities within network traffic. This paper introduces a graph-based detection framework leveraging the Composite Node Information - Variance Inflation Factor (CNI-VIF) method for enhanced feature selection. By integrating traditional statistical metrics with graph-specific attributes like centrality measures, CNI-VIF effectively reduces dimensionality while preserving crucial features. The proposed methodology is validated using multiple machine learning models across CTU-13, IoT-23, and NCC-2 diverse botnet datasets, demonstrating superior accuracy, reduced computational overhead, and robust detection performance. The framework integrates machine learning models, counting Logistic Regression, Random Forest, SVM, Ensemble, FFNN, and Convolutional Neural Networks, achieving near-perfect detection rates with minimal false positives and false negatives. Furthermore, the proposed methodology substantially reduces computational time, up to 80%, compared to the state-of-the-art method, highlighting its suitability for real-time botnet detection in complex datasets. Comparative analysis confirms the methodology's advantage over existing state-of-the-art solutions, emphasizing its practical utility for real-time botnet detection.
Optimized Fused Regression Model for Regression Algorithms
Authors: Swati Gupta
Published: February 2025

One or more independent variables are compared to a dependent variable using regression analysis. Prediction and inference are its key goals. This strategy helps identify data patterns and trends to estimate constant outputs from variables. This research examines how Gradient Adaptive Moment Estimation Optimiser and ensemble multiple linear regression may improve regression task prediction. OFRM efficacy is assessed using six datasets from distinct sectors. six datasets from different domains were utilised to test OFRM. Test it against five regression models. Apply strict criteria and test OFRM extensively to establish its impact on anticipated accuracy, robustness, and generalisability. OFRM dominates individual regression on all datasets. This research shows OFRM's performance in regression scenarios to advance ensemble learning. This paper emphasises the necessity to combine optimisation and ensemble techniques to enhance regression models for real-world applications. Regression model performance on NFT datasets was evaluated using MSE, RMSE, MAE, and R³ measures. OGFR predicts accurately with the lowest MSE (1.04), RMSE (2.21), and MAE (1.29). Best fit is achieved with a R² value of 0.85, accounting for 85% of sample variance. By outperforming DNR and KNN with R² of 0.55 and MSE of 1.91, OGFR is the top model for NFT dataset predictions with a R² of 0.75 and MSE of 1.24.
A Comparative Study of Sequence of Multiple Fingerprints for Secured Authentication
Authors: Shiva Prasad M S
Published: February 2025

Smartphones are becoming increasingly prevalent and most of them use fingerprint recognition to authenticate any application, from financial transactions to login. Imposters are attacking single fingerprint template quite easily. So, next security level for smartphones needs to be implemented in order to strengthen existing method. One such method is implemented by employing Sequence of Multiple Fingerprints (SMF). This paper presents a lightweight, cost-effective application based sequential fingerprint authentication technique when compared with other modern techniques. The proposed system is designed for environments with limited computational resources, offering enhanced security and efficiency. Unlike traditional single-fingerprint authentication methods, our algorithm employs sequential fingerprint input for improved accuracy and robustness. Experimental results demonstrate a low False Acceptance Rate (FAR) of 0.5%–3% and a False Rejection Rate (FRR) of 1.8%–5%, with significantly reduced execution costs and processing times compared to existing methods. The system is ideal for Smartphones, IoT applications, including access control and smart lock systems, where lightweight and scalable solutions are essential.
Integrating LSTM and Hybrid Methods for Automatic Balinese Script Transcription
Authors: Made Sudarma
Published: February 2025

The preservation of Balinese script through digitalization faces challenges, particularly in recognizing words with multiple meanings that require different transliterations depending on context. This study developed an artificial intelligence system based on Long Short-Term Memory (LSTM) to detect the meaning of specific words in Balinese sentences and provide accurate recommendations for Balinese script transliteration. Testing showed that the LSTM model achieved an accuracy of 71% in identifying word meanings in new sentences. To enhance accuracy, this research integrated a hybrid method combining Jaro-Winkler and Damerau-Levenshtein as a preprocessing step. This combination successfully increased system accuracy to 94.58%, surpassing the previous approach, which reached 94.3% without LSTM. This integration demonstrates that the hybrid method effectively corrects spelling errors and reduces ambiguities before deep learning processing. These results represent a significant step in preserving Balinese culture through script digitalization, with further potential for development through context-based processing.
SUSTAINABILITY OF CIVIL AVIATION DEVELOPMENT IN THE ARCTIC REGIONS ON THE BASIS OF AN ENVIRONMENTAL AND RESOURCE APPROACH
Authors:   St. Petersburg State University of Civil Aviation, Zaitseva Irina V. , Sycheva Ekaterna G. ,   St. Petersburg State University of Civil Aviation
Published: February 2025

Problems of development of the Arctic are relevant in terms of effective resource supply and improving environmental
safety. The implementation of the concept of sustainable development is possible with the integrated development of the
Arctic and requires the development of an environmental and resource approach. Strategic documents for the development
of the Arctic 2020–2024 define, that improving transport infrastructure should preserve the ecosystem and reduce air pollution.
Trends in aviation ecology improvement according to ICAO forecasts are analyzed, operation of cross-polar routes, number of
interregional airfields and landing sites. Conclusion, the aviation network needs to be upgraded and expanded. The authors
have developed a problem field of management of economic resources of civil aviation in the Arctic zone. Obstacles to the
development of the air transportation system have been identified. The characteristic of economic resources management for
different groups of air transport enterprises is proposed. Conclusion, that key aspects of environmental management of
resources in the Arctic zone should be based on an environmental-resource approach in the development of civil aviation,
increase in efficiency taking into account the modernization and implementation of digitalization. Increasing social
responsibility based on the ESG-concept in civil aviation, adaptation of the strategy for the development of civil aviation to the
implementation of national goals for the development of the Arctic.
CREATIVE SPACES AS A VECTOR FOR THE DEVELOPMENT OF AN URBAN AGGLOMERATION
Authors:   St. Petersburg State University, Radushinskaya Alexandra I. , Chernomorets Anna B. ,   North-West Institute of Management of the Russian Presidential Academy of National Economy and Public Administration, Sheldovitskii Kirill E. ,   St. Petersburg State University
Published: February 2025

In the conditions of globalization and accelerated technological progress, creative clusters and industries are
becoming increasingly important for the economic and social development of urban agglomerations. This article is devoted
to the study of the peculiarities of the development of creative clusters in modern agglomerations and their impact on the
economy and society. The article considers the theoretical foundations and conceptual approaches to the formation of
creative clusters, including Richard Florida’s theory of creative classes and Michael Porter’s theory of clusters. In addition,
current trends and challenges such as globalization and digitalization and their impact on the development of industries are
analyzed. Key factors contributing to the success of creative clusters are discussed, including the presence of developed infrastructure, education and innovation systems, government support and international cooperation. The conclusion identifies
the main conclusions and recommendations for the effective development of creative clusters in the Russian context. Thus, this
paper contributes to the understanding of the processes and mechanisms contributing to the successful development of
creative clusters and emphasizes their importance in the context of sustainable economic and social development of modern
urban agglomerations.
FEATURES OF SUB-FEDERAL BUDGET REVENUE IN THE RUSSIAN FEDERATION
Authors:   Institute of Economics and Industrial Engineering of Siberian Branch Russian Academy of Sciences, Sumskaya Tatiana V.
Published: February 2025

The article assesses the structure of revenues of regional budgets of the Russian Federation for the period 2012–2020,
calculates the characteristics of heterogeneity of budget indicators before and after the allocation of funds from the federal
budget. The relationship between transfers from federal budget and tax and non-tax revenues is analyzed, the marginal effect
of increasing taxes received by the budgets of Russian regions is estimated using regression analysis methods. To assess how
well the current system of inter-budgetary fiscal relations copes with its functions, in particular with equalizing the differentiation
of budget provision of the subjects of the Russian Federation and stimulating regions to strengthen their own revenue base, the
methods of ranking the subjects of the Federation, as well as regression analysis tools were used.
Socio-Culture Impact of AI on Traditional Indian Communities
Authors: Sandeep Kumar Saroj
Published: February 2025

This article explores the socio-cultural impact of Artificial Intelligence (AI) on traditional Indian communities. In recent years, AI technologies have increasingly penetrated various aspects of Indian society, influencing cultural practices, economic structures, and social dynamics. The study examines how AI advancements are reshaping traditional roles, customs, and knowledge systems within these communities. It investigates both the positive and negative implications of AI adoption, highlighting changes in livelihood patterns, community cohesion, and intergenerational knowledge transfer. Special attention is given to the ethical considerations and challenges arising from the integration of AI into cultural frameworks that emphasize collective identity and spiritual values. Through a qualitative approach involving case studies and community interviews, the research elucidates the nuanced responses of traditional Indian societies to AI innovations, addressing issues of cultural preservation, adaptation, and resilience. By analyzing these dynamics, the article contributes to a deeper understanding of the complex interactions between technological progress and cultural continuity in contemporary India.
Unraveling the antifungal, antibacterial activity, and cytotoxicity analysis of Ziziphus oenoplia
Authors: Mishra Anshu , Kushwaha Ravi Kant , Akash Singh  , Khare Renu , Verma Sunil Kumar , Devendra Singh
Published: February 2025

Modern biomedical sciences are developing research in the field of antibacterial medications. The Indian traditional medical system of Ayurveda is being studied by researchers who are working to create new medications from natural resources. The majority of these medications have plant origins. This study studied the antifungal and antibacterial activity, along with the cytotoxicity of Ziziphus oenoplia. The fined powder of Ziziphus oenoplia was used, and a well diffusion assay was used to assess the antifungal and antibacterial activity. Finally, the cytotoxicity of the plant extract was also analyzed by using a cell line. The outcomes indicate that the bark, leaf, and fruit extract shows potent antibacterial as well as antifungal properties. The highest zone of inhibition observed against E. coli was shown by the fruit extract (20.66 mm). Furthermore, the cytotoxicity assay on the SW480 cell line demonstrated that the extracts are either minimally toxic or non-toxic. However, dose-dependent cytotoxicity has been seen in vitro. Although green synthesis has grown more popular due to its environmentally friendly processes, further clinical trials are required.
Impact of Organics and Inorganics on Growth and Yield of Sunflower Based Intercropping System
Authors: Dharanidharan V.  , C. Kalaiyarasan  , S. Kandasamy  , D. Venkatakrishnan  , S. Madhavan  , S. Ramesh
Published: February 2025

The field investigations were carried out at the Experimental Farm, Department of Agronomy, Faculty of Agriculture, Annamalai University, Annamalai Nagar 608 002 during July - October, 2023 and Jan - April, 2024 to study the conjoint influence of organics and inorganics on growth and yield attributes and yield of sunflower based intercropping system. The experiments were laid out in split plot design with three replications. The main plot treatments comprised of M1 - Sole sunflower, M2 - sunflower + greengram and subplot treatments comprised of S1 - RDF alone (60:90:60kgs of NPK ha-1), S2 : RDF + Poultry manure compost @ 6t ha-1, S3 : RDF + Poultry manure compost @ 6t ha-1 + Sulphur @ 45 kg ha-1 through gypsum, S4 : RDF + Poultry manure compost @ 6t ha-1 + Sulphur @ 45 kg ha-1 + AM Fungi @12 kg ha-1. The results of the experiments revealed that sole sunflower with RDF + Poultry manure compost @ 6t ha-1 + Sulphur @ 45kg ha-1 + AM Fungi @12 kg ha-1 (M1S4) significantly influenced the growth and, yield attributes and yield of sunflower in sunflower based intercropping system. With regard to economic returns, sunflower + greengram intercropping system along with the application of RDF + Poultry manure compost @ 6t ha-1 + Sulphur @ 45kg ha-1 + AM Fungi @12 kg ha-1 (M2S4) recorded the highest net return of Rs. 121604 with a BCR of 2.74.
Characterizing the Influence of Zinc, Iron and Silicon on Growth Dynamics and Yield Optimization in Transplanted Rice
Authors: Dharshini K. Akshaya , P. Sudhakar  , S. Manimaran  , D. Venkatakrishnan
Published: February 2025

A field experiment was conducted during the kuruvai season (June – September) of 2023 at the Experimental Farm, Department of Agronomy, Faculty of Agriculture, Annamalai University, Cuddalore district, Tamil Nadu, India, to characterize the influence of zinc, iron and silicon on growth dynamics and yield optimization of transplanted rice (Oryza sativa L.). The experiment was laid out in a randomized block design with three replications and ten treatments, combining various forms of zinc, iron, and silicon. The results revealed that growth and yield attributes of rice viz., plant height, leaf area index, dry matter production, productive tillers per square meter, number of grains per panicle, test weight and grain yield (5512 kg/ha) and straw yield (7726 kg/ha) were recorded significantly higher with application of RDF along with soil application of 25 kg of ZnSO4 and FeSO4 with 50 kg diatomaceous earth.
Revolutionizing Patient Outcomes with AI-Powered Generative Models: A New Paradigm in Specialty Pharmacy and Automated Distribution Systems
Authors: Chava Karthik
Published: February 2025

Artificial intelligence (AI) large language models (LLMs) have been advanced with rapid advancements in science and machine learning techniques, increasingly leading to a wide range of high-level intelligent applications. Due to improved “understanding” of the context of writing prompts, AI LLMs for natural languages have been able to generate diverse and sophisticated organic text with remarkable fluency. LLMs are able to provide exceptional dialogue, vaccine material design, and academic text generation. The implementation of AI LLMs for pharmaceutical applications, however, such as prompt-driven language generation tasks in the pharmacy domain, have scarcely been attended to.

Recently, pharmaceutical patient specialists co-developed a new system, DrugSymphony, which was a language model based system of AI LLMs to enhance prompt-driven writing in the area of pharmacy. Two key complementary strategies around the specialized knowledge graphs and patient healthcare narratives can boost DrugSymphony, leading to inputs that mimic human knowledge comprehension. Through several evaluations utilizing patient sets in the real world, DrugSymphony is found to enhance well-defined tasks of writing, specifically medication program generation. It is hoped that this study will contribute to further research into machine-generated writing in the pharmacy field. At the time, however, the research sheds light on the effectiveness of LLMs in addressing real-world, professional-language, prompt-driven writing tasks, so pharmacy and pharmacotherapy specialists can maximally benefit from their potential functionality.

Recently, the implementation of advanced AI techniques for creating text has significantly improved the scope of potential applications in a broad range of sectors. The LLMs automatically generate organic text in response to a prompt of incoming text, with unprecedented fluidity and naturalness. The use of GPT has a great deal of flexibility; no specific instructions are often needed other than natural language text prompts. On this account, the beneficiaries include not only industry-insider professionals but also non-experts who may easily engage in building system prototypes for a wide range of applications. This includes technical reports, medical and science-related publications, company memoranda, marketing products, and so forth. GPT applications, on the other hand, come with the number of limitations: proprietary program formulation and large model costs.
Machine Learning and Generative Neural Networks in Adaptive Risk Management: Pioneering Secure Financial Frameworks
Authors: Malempati Murali
Published: February 2025

As machine learning has evolved with generative neural networks, financial systems have always offered exploratory opportunities in model simulations. Adaptive risk management, risk pricing, and innovative financial instruments have employed the power of computing technology by integrating quantum computing or cryo computing. This essay focuses on a novel application within the finance domain, which involves the convergence of machine learning, generative neural networks, and financial systems. The technological challenge for a potential adopter is to examine and apply the appropriate methodologies within financial cyber infrastructures leading to the financial institution's success. Among other applications of financial system simulations, the market and investment integrations of artificial intelligence contribute to effective risk management and analytics with qualitative statistical representations of the chaotic quantitative environment.
Current risk management methodologies employed using financial regulation are based on a one-size-fits-all approach. This method does not look at individual business practices, and in this environment, an alternative has to be achieved. Such legislative approaches may replace the variance in adopting greater financial innovations, thus disrupting the business environment. In adopting new methodologies, machine learning and neural networks' adaptive nature integrates quantitative and qualitative risk issues, thus pioneering secure and stable business environments. The application of financial engineering and machine learning at the onset of a crisis and subsequent communications can integrate the building of bridges between inter-institutional and government regulation with industry practice standards.
Generative AI-Driven Frameworks for Streamlining Patient Education and Treatment Logistics in Complex Healthcare Ecosystems
Authors: Chakilam Chaitran
Published: February 2025

Healthcare ecosystems are increasingly compounded with a plethora of patients presenting with an array of diseases and relying on interdisciplinary treatment approaches. Patient scenarios frequently encompass complex comorbidities, and the provision of care must comply with intricate interplays between diagnostic test results and clinical decisions. Patients’ understanding of clinical procedures and treatment logistics is key for successful therapeutic procurement and favorable therapeutic outcomes. As a result, specialized patient education resources have been developed, providing personalized materials to guide patients through diagnostic and therapeutic procedures. Such resources usually include: (i) short training sessions with healthcare professionals; (ii) reading materials describing the medical background; and/or (iii) short educational videos illustrating the treatment process. Nonetheless, patient education materials are commonly oblivious to the underlying medical terminology and complex treatment algorithms, precluding patients from making well-informed choices regarding their therapeutic options. These challenges are propelled by the intricate prescription decisions, which necessitate a comprehensive knowledge of the disease specifics and the therapeutic property of drugs. To this end, MissingLink enables a personalized patient journey within the healthcare facility, aimed at educating patients in heterogeneous healthcare ecosystems with a program consisting of interdisciplinary treatment decisions. MissingLink can help streamline patient therapeutics by dispatching personalized educational materials tailored to the patient’s disease and treatment armamentarium. Inline missing terms, acronyms, drugs, proteins, or complex pathologies are automatically deciphered using explainable artificial intelligence (XAI) components, fostering better patient comprehension. In view of facilitating the comprehensive deciphering of complex educational videos, an Explainable Video Summarization framework is proposed for the concise generation of clinical tests and the underlying decision-making process carried out by clinicians.
The Role of Cloud Computing in Revolutionizing Business Banking Services: A Case Study on American Express’s Digital Financial Ecosystem
Authors: Burugulla Jai Kiran Reddy
Published: February 2025

The advent of cloud computing has brought a significant change in traditional business banking services. This essay intends to explore how the cloud revolutionizes banking operations while enriching the customer delight. Smart banks are already lengthening their business service capacity to include the richness of 24/7 online banking services for their commercial account customers. It creates a business service model being steadily spread out towards digital resonance.

Transference and history of business banking services in a different dimension for qualitative inquiry have been revealed to be speeding up by the introduction to an integral part of the cloud computing paradigm. Business clients need business services in such a way as not being easily comparable to individual retail banking customers. Better visibility and control over the cash flow are needed, setting up different authorization tiers based on roles on the business side, expedient payroll capabilities lighting quickly while travelling, place approving, and the like. It was the early 2000s when large money center banks began to gratify these high demand business requirements such as a compressive check manipulation solution. Such an exclusive facility service always required a long chain of face-to-face engagement between the banker and the business client and was accessible exclusively for those business clients domiciled in a location that only a large money center bank might afford. All other business clients, mostly the Small and Medium Businesses were soonly to have been gratifying the way they had been.
Transforming Travel Benefits through Generative AI: A Machine Learning Perspective on Enhancing Personalized Consumer Experiences
Authors: Challa Kishore
Published: February 2025

With continuing advancements in generative artificial intelligence (AI), the opportunity arises to change how travel benefits are designed and curated. Generative AI models could be trained to produce customized travel benefits shaped through interactive dialogues with consumers based on their individual preferences and wants. Furthermore, the benefits could be augmented with personalized auxiliary information generated through AI across various textual, numeric, and visual formats. These possibilities could entrench the role of travel service providers as AI-curated travel solution architects and change how consumers interact with the services and benefits. In light of this vision, this research investigates how machine learning generative AI technologies could reshape the design and delivery of personalized travel benefits to consumers. Each of the four essential components in the generative AI pipeline that realizes this transformation is analyzed. First, the design and modeling of personalized travel benefit solutions are examined using a novel bi-agent generative model framework that simulates consumers’ benefit-acquiring behavior and travel benefit co-designing behavior. Second, the large-scale enrichment of the co-designed travel benefits using diverse auxiliary information generated through AI is analyzed. Third, the implementation of travel benefit consumer interaction with embedded generative AI capabilities is explored. Finally, the technology infrastructure for realizing the AI-centric generative travel benefit services is discussed. The findings and developmental pathways could enlighten researchers and practitioners in travel and consumer service industries about using generative AI technologies to enhance personalized consumer experiences in services.
Association between enteral nutrition and the incidence of pneumonia in critical care adults: A literature review
Authors: Alenazi Ahmed O. , Alharbi Mashael , Alsaab Afnan A. , Aldossary Metrek A. , Khrnoob Salem T. , Alzoabi Faisal , Alhuwaiji Saleh M. , Alzahrani Abdulhadi , Alamro Amro A. , Alenazi Sultan L. , Alharbi Waad , Alahmadi Shouq , Aloufi Ghada A.
Published: February 2025

Background: To investigate the association between enteral feeding and the risk of pneumonia in critical care adult patients. Methods: An updated literature review of studies discussing between 2016 and 2023. CINAHL, Cochrane, Embase, PubMed, Scopus, and Web of Science databases were used to explore studies regarding our subject, and the association between enteral feeding and the risk of pneumonia in critical care adults. Results: Enteral nutrition (EN) has been linked to a significant reduction in pneumonia, as well as shorter ICU or hospital stays, and decreased mechanical ventilation duration in numerous studies. Conversely, pneumonia risk and other infections, as well as the fatality rate, have been found to increase with EN. Early initiation of EN within 48 hours, along with a low-calorie diet containing (< 600 Kcal/day) demonstrated a notable decrease in the occurrence of Ventilator-Associated Pneumonia (VAP) when contrasted with delayed EN and a high-calorie diet. EN was associated with a marked decrease in adverse events including aspiration pneumonia, emesis, and the escalation of respiratory support. Conclusion: EN is safe and effective in critically ill patients. Future research should investigate the monitoring of larger sample sizes to ascertain the most effective feeding methods in the intensive care unit.
A rapid evidence assessment of the research underpinning the validity of Structured Professional Judgement tools that assess risk of stalking
Authors: Goodchild Alex , Borngraeber Nancy
Published: February 2025

The Stalking Assessment and Management (SAM; Kropp et al., 2008) and the Stalking Risk Profile (SRP; Mackenzie et al., 2009) were published in 2008 and 2009 respectively and designed to assess risk of stalking behaviour. Based on generally accepted criteria of what constitutes a Structured Professionals Judgement (SPJ) tool (i.e. a combination of static and dynamic risk factors which also includes case formulation and risk management considerations; Hart et al., 2016), in my opinion, the SAM and SRP are the only stalking risk assessments which can be considered SPJ tools. The current REA was conducted to explore the evidence base underpinning both assessments. Overall, there was limited evidence to draw any conclusions on the interrater reliability, internal reliability, internal consistency, discriminant validity or predictive validity due to the lack of peer reviewed research for either the SAM or SRP. Only two studies evaluated the SRP, whereas seven evaluated the SAM. The findings of this REA must be interpreted with caution given the limited amount of relevant research identified and included in the analysis, specifically the lack of evaluative studies involving the SRP. There are also concerns about the quality of the studies included, which generally include small samples, retrospective research designs, a lack of adequate comparison groups and difficulties identifying cases of stalking and recidivism. Further research is required to provide robust empirical support for claims regarding the validity and reliability of both assessment tools.
Valores de referencia para TSH y T4 libre en la población de Medellín, Colombia
Authors: Rico Mesa Maria Antonia , León-Aldana Sara , Gómez Marta , Ocampo Liliana , Builes Silvana , Abad Verónica
Published: February 2025

Contexto: los valores de referencia de hormona estimulante tiroidea (TSH) y tiroxina libre (T4L) varían de acuerdo con la población de estudio y, posiblemente, se afectan por factores intrínsecos y extrínsecos, incluida la latitud, el sexo, la raza, la edad y el estado nutricional de yodo, entre otros.
Objetivo: establecer los valores de referencia de TSH y T4L para la población de la ciudad de Medellín, Colombia.
Metodología: se diseñó un estudio observacional prospectivo, donde se incluyó a un total de 231 pacientes sanos entre 10 y 65 años de edad de la ciudad de Medellín, a quienes se les midieron los niveles de TSH y T4L en sangre periférica. La muestra mínima confiable calculada fue de 210 pacientes, para alcanzar un intervalo de confianza del 90?%, definiendo una p <0,05 como estadísticamente significativa.
Resultados: se encontró que, en la población sana de la ciudad de Medellín, los valores de TSH oscilaron entre 0,49 y 5,72 mUI/l y para T4L entre 0,65 y 1,19 ng/dl.
Conclusiones: para una población entre los 10 y 65 años, de la ciudad de Medellín, los valores mencionados previamente en los resultados no presentaron diferencias estadísticamente significativas por edad o sexo. Estos valores fueron definidos con el fin de tener un referente para la toma de decisiones con la clínica de cada de paciente.
Haptic Aesthetic Experiences of Drawing
Authors: Edström Ann-Mari
Published: February 2025

This article aims to acknowledge the bodily complexity of the drawing experience by regarding bodily movement as a teaching modality. Theoretically and methodologically, the article explores the didactic potential of variation in relation to phenomenography, artistic practice, and the Feldenkrais Method of movement. Two interdisciplinary drawing workshops were held, combining drawing with Feldenkrais intraventions. The participants’ drawings were analyzed, with the assumption that changes in the bodily experience of drawing would be discernable in the lines on the paper. The results indicate that the Feldenkrais intraventions affected the bodily experience of drawing. Changes in the bodily experience were discerned and described in terms of proprioceptive presence, with reference to Jennifer Fisher's concept of haptic aesthetics. Two kinds of presence were brought to the fore: a presence defined as being present in the moment, and a presence defined as an increased sensibility towards three-dimensional volume and space. Both kinds are subtle qualities of bodily performativity that tend to go unnoticed in educational settings, in favor of visual or more explicitly performative bodily expressions. A relational epistemological approach is put forth as a way to acknowledge performative qualities of the drawing experience in teaching and learning.
Basic Stochastic Framework for Designing and Understanding Real Estate Models
Authors: Park David
Published: February 2025

The real estate market offers significant opportunities for financial success, yet navigating its complexities—such as tax benefits and regulatory influences—can be challenging. Stochastic processes play a critical role in minimizing risk and optimizing returns in this domain. Despite its historically stable growth, real estate remains underutilized, particularly among lower-income households, partly due to the limitations of traditional financial models like Black-Scholes. These models often struggle to account for the volatility, interest rate fluctuations, and government policies that influence real estate markets. This paper demonstrates the efficacy of integrating advanced stochastic models, such as the Heston model and jump diffusion models, to address these limitations. Additionally, we explore diverse methods for validating these models and highlight key considerations in their design to ensure greater accuracy and practical relevance. These insights aim to enhance the robustness of real estate modeling, providing a pathway for improved financial decision-making.