Financial analysis of an enhanced vaccination program to control brucellosis in cattle in different farming systems in Rwanda.

The study examines the impact of environmental and social ESG factors on the cost of capital of companies in the context of sustainable development. Its purpose is to assess how the integration of ESG indicators influences financial efficiency, particularly the weighted average cost of capital (WACC), and to identify industry- and region-specific differences in this impact. The methodology is based on a quantitative analysis of panel data from publicly listed companies for the period 2015–2023, using ESG ratings from MSCI and Sustainalytics. The results demonstrate that stronger ESG performance is generally associated with a lower cost of both equity and debt capital, contributing to improved long-term financial sustainability. The most pronounced effects are observed in capital-intensive sectors such as industry, energy, and mining, where environmental standards reduce credit risks. Regionally, the strongest ESG impact is found in the European Union and North America due to advanced regulatory frameworks. The study proposes methodological approaches and practical tools for integrating ESG factors into financial analysis and corporate strategic planning.

The article substantiates the need for a transition from reactive to proactive management of economic security of oil and gas sector enterprises in the context of geopolitical instability and sanctions pressure. The purpose of the study is to develop theoretical and methodological approaches to the formation of a risk indicator system for oil and gas sector enterprises. The methodological basis includes a systematic approach, methods of financial and economic analysis, comparative analysis of methodological approaches (threshold, expert, factorial), methods of systematization and classification. The results of the study: a multi-level classification of economic security factors is substantiated, a comparative analysis of methodological approaches to assessment is carried out, a unified set of risk indicators for five key functional areas (financial, production-technological, personnel, environmental, legal) is formed, an indicative assessment system and a combined method for establishing threshold values (benchmarking, retrospective analysis, expert assessments) are proposed. Conclusions: the developed risk indicator system, adapted to the specifics of oil and gas enterprises and taking into account the dynamic nature of threats, can be integrated into existing risk management systems of fuel and energy companies to increase their sustainability. Further research directions are related to the creation of a software product for monitoring automation and the development of scenario models for forecasting the level of economic security.

This study aimed to analyze the profitability ratio performance of PT Bank Mandiri (Persero) Tbk during the 2020–2025 period using a quantitative descriptive approach. The data used were secondary data obtained from the company’s annual financial statements and analyzed using key profitability ratios, including return on assets (ROA), return on equity (ROE), net profit margin (NPM), and operating expenses to operating income (BOPO). The analytical method involved presenting the data in tabular form and conducting trend analysis to describe the dynamics of the company’s financial performance. The results indicated that ROA and ROE experienced significant growth until 2023, followed by a decline in the 2024–2025 period. NPM showed highly volatile fluctuations, with a sharp increase up to 2023 and a substantial decline thereafter. Meanwhile, BOPO showed a downward trend, indicating improved operational efficiency, although a slight increase occurred at the end of the period. The study concluded that Bank Mandiri’s profitability performance was dynamic and influenced by both internal and external factors. This research contributed to the understanding of banking financial performance analysis based on profitability ratios in a longitudinal context.

Digital technology development has drastically changed accounting procedures in a variety of industries and sizes of businesses. Computerized accounting systems that increase productivity accuracy speed and financial reporting are gradually replacing manual bookkeeping-based traditional accounting methods. Tally ERP and Microsoft Excel are two of the most popular accounting programs in India. Both programs are frequently used for financial analysis reporting auditing bookkeeping taxation budgeting payroll management and decision making tasks. But even with their widespread use choosing the best software for various accounting tasks remains a challenge for businesses and accounting experts. With a focus on efficiency flexibility compliance support operational effectiveness and data accuracy the current study compares Tally and Microsoft Excel in accounting practices. Using data from both primary and secondary sources the study used a descriptive research design. Structured questionnaires were used to gather primary data from accountants accounting professionals business owners commerce students finance executives and software users. Convenience sampling was used to select a total sample size of 100 respondents. Secondary data pertaining to computerized accounting systems was gathered from books journals research articles and internet resources. For data interpretation SPSS software was used to apply statistical techniques like percentage analysis descriptive statistics correlation analysis Chi-square test independent sample t-test ANOVA and regression analysis. According to the study's findings Tally ERP is highly favored for inventory management GST compliance accounting efficiency automation of financial reporting audit support and a decrease in manual errors. In addition respondents thought Tally was faster more accurate and safer than Microsoft Excel. However it was discovered that Microsoft Excel offered greater flexibility when it came to financial analysis forecasting personalized reporting and financial modeling. The study also showed that in order to improve accounting and analytical performance businesses are increasingly using Tally and Excel together. Tally and Microsoft Excel both have significant and complementary roles in contemporary accounting procedures according to the study's findings. While Excel is very helpful for reporting and analysis Tally is better suited for structured accounting operations and compliance management. Regarding the adoption of digital accounting software training and technological advancements in financial management systems the study offers significant implications for businesses accountant’s educational institutions and policymakers.

This report describes the implementation, workflow structure, and outcomes of a centralized, investigational drug service (IDS)-led process for concomitant medication reviews in oncology clinical trials. This initiative aimed to improve standardization and enhance protocol compliance by transitioning initial concomitant medication review responsibilities to IDS pharmacists. A dedicated IDS pharmacist role was established to support centralized concomitant medication reviews, ensuring a standardized and efficient process. This initiative included developing a structured request system, electronic documentation, and protocol-specific concomitant medication review guides to enhance consistency. Over a 24-month evaluation period, IDS pharmacists completed 1,052 concomitant medication reviews, averaging 43.8 reviews per month. Drug-drug interactions were identified in 34.3% of cases, with the highest prevalence observed in phase 1 trials. IDS review times remained consistent, with a mean system-tracked time of 1.31 hours per review. However, delays in medication reconciliation emerged as the primary bottleneck, often prolonging review completion. A financial analysis confirmed the program's sustainability, demonstrating that the billable fee-for-service model effectively supported its implementation. Despite these successes, challenges remained, including variability in protocol interpretation and limited guidance on herbal and supplement use. The IDS-led concomitant medication review model improved workflow standardization while supporting operational efficiency and financial sustainability. This approach provides a scalable framework that can be adapted to other clinical trial programs and expanded to include ongoing medication monitoring.

Cryptocurrency markets have gained significant global attention due to their decentralized nature and high financial value. Among various cryptocurrencies, Bitcoin is the most widely traded and exhibits highly volatile price behavior. Accurate analysis and prediction of Bitcoin price trends are challenging because the market is influenced by rapid trading activities, large data streams, and complex temporal patterns. This paper presents a streaming data collection and analysis system for Bitcoin using the Long Short-Term Memory (LSTM) deep learning algorithm. The proposed system continuously collects real-time Bitcoin market data from online cryptocurrency exchanges through streaming APIs. The collected data is then preprocessed and analyzed using an LSTM-based predictive model capable of learning long-term dependencies in time-series data. The LSTM network processes sequential historical price data to forecast future market trends and provide analytical insights into Bitcoin price movements. The system integrates data acquisition, preprocessing, deep learning-based prediction, and visualization modules to create an efficient cryptocurrency analysis framework. The proposed approach focuses on improving prediction accuracy by combining real-time streaming data with advanced neural network models. This system can assist researchers, financial analysts, and investors in understanding cryptocurrency market behavior and making informed trading decisions. The proposed design demonstrates the feasibility of integrating streaming data technologies with deep learning models for real-time financial market analysis. Keywords— Cryptocurrency, Bitcoin, Streaming Data, LSTM Algorithm, Deep Learning, Time-Series Prediction, Financial Data Analysis.

Event-driven time series forecasting constitutes a fundamental challenge across multiple scientific domains. Existing deep learning approaches rely on the assumption of historical pattern continuity and thus exhibit inadequate responsiveness to exogenous shocks, while mainstream text-enhanced methods encode documents as singular dense vectors, causing the compression of fine-grained semantic information such as entity stances, event types, and causal propagation relationships. To address these limitations, this study proposes a tripartite methodological framework building upon multimodal graph neural networks and the Temporal Fusion Transformer architecture. The first component employs large language models to perform hierarchical semantic extraction that decomposes unstructured text into four structured layers comprising entity stance quantification, event ontology mapping, inter-event causal chain reasoning, and aggregate sentiment indicators, thereby preserving prediction-relevant information that would otherwise be lost through flattened vectorization. The second component constructs heterogeneous information graphs containing multiple node types and relation types to explicitly model cumulative effects, propagation effects, and synergistic effects among events through relational graph convolutional networks with relation-aware attention mechanisms. The third component utilizes semantic analysis to achieve rapid environmental state identification, enabling dynamic recalibration of feature importance weights according to prevailing conditions and thereby addressing the non-stationarity inherent in event-driven prediction tasks. Experimental validation using crude oil price forecasting as a representative scenario demonstrates that the proposed framework achieves 17.7% improvement over baseline methods in overall prediction accuracy, with improvement reaching 34.7% during event-intensive periods, while ablation studies confirm independent contributions from each component. The proposed methodology possesses domain generality and can be transferred to other event-driven prediction tasks including epidemic spread forecasting, supply chain risk assessment, and financial market analysis.

We conducted a Health Labour Market Analysis (HLMA) to evaluate the alignment of health workforce supply with population health needs and fiscal sustainability through 2030. A quantitative study design using secondary data was based on the World Health Organization (WHO) HLMA Framework and the 2021 WHO HLMA Guidebook. Quantitative data were gathered from the Human Resources Information System (HRIS), professional associations, training institutions, and national accounts, supplemented by grey literature and stakeholder consultations. Workforce supply and demand projections for 2024-2030 considered an attrition rate of 3.5%, a 20% unemployment rate for new graduates, and an 80% absorption rate. Financial analyses were aligned with Gross Dometic Product (GDP) and fiscal projections from the World Bank, International Monetary Fund (IMF), and the National Bank of Ethiopia. Data quality assurance included multi-level validation using a standardized Ministry of Health (MOH) tool, with outlier checks and stakeholder verification. The supply of health professionals is projected to increase steadily, reaching approximately 74,693 nurses, 30,980 midwives, and 25,576 general practitioners by 2030. Despite these gains, significant shortages persist relative to Essential Health Services Package (EHSP)-based requirements, particularly among medical specialists, nurses, anesthetists, and laboratory professionals. Financial analysis indicates that cumulative fiscal space is projected at USD 945 million by 2030, while the cost of employing the available workforce is estimated at USD 1.08 billion, and the EHSP-aligned requirement at USD 1.8 billion. This results in an annual financing gap of USD 20-30 million for workforce absorption and over USD 800 million relative to service needs. Ethiopia's HLMA highlights gaps between workforce supply, health needs, and fiscal capacity. Despite an increase in graduates, unemployment persists. Improving Human Resource for Health (HRH) governance, expanding fiscal resources, and ensuring fair deployment are vital for effective workforce utilization and advancing universal health coverage.

SEHLP: A summary-enhanced large language model for financial report sentiment analysis via hybrid LoRA and dynamic prefix tuning

GRA-FIN: New method of large graphs reduction for financial transactions and financial crime analysis

This study presents an integrated, multi-criteria spatial assessment for the sustainable siting of OWFs around the island of Crete, combining expert-derived weights, a GIS-based weighted overlay analysis, and an updated socio-environmental exclusion framework. Sixteen evaluation criteria were incorporated, reflecting environmental, technical, economic, and socio-political dimensions, each adapted to the unique geographic and ecological conditions of the Mediterranean island environment. Results confirm the robustness of the original multi-criteria model while refining local suitability by incorporating real environmental evidence. In particular, areas characterised by circalittoral rocky substrates and low biodiversity (e.g., Agios Nikolaos, Chersonissos) align with previously identified high-suitability zones. In contrast, sites under archaeological or ecological protection (e.g., Zakros, Elounda–Spinalonga) were validated as unsuitable. Several areas along the northern and eastern coasts emerged as comparatively favourable due to the combination of strong wind potential and relatively short distances to existing transmission infrastructure and coastal access points. Approximately 493 km 2 of marine areas were classified as moderately to highly suitable. Detailed engineering, geotechnical, financial, and grid-integration analyses would be required to assess practical feasibility and project-scale implementation. Nevertheless, the analysis also underscores the importance of integrating ecological sensitivity into planning processes, particularly for migratory birds and species that rely on soaring–gliding flight. Overall, the findings indicate that Crete has significant spatial potential for offshore wind development within a strategic planning framework, particularly given advances in floating wind technologies and ongoing grid interconnection projects. • Integrated AHP–GIS framework for offshore wind siting in island environments • Multi-stakeholder weighting captures technical, environmental and social constraints • Suitability mapping identifies priority offshore wind zones around Crete • Field-based ROV surveys validate seabed conditions and refine spatial decisions • Methodology supports adaptive offshore wind planning in the Mediterranean

Colorectal cancer (CRC) remains the second leading cause of cancer-related death in the United States, with screening disparities disproportionately affecting socioeconomically disadvantaged and underrepresented populations. Although outreach-based CRC screening programs using fecal immunochemical testing (FIT) have been previously described, many have focused on patients already engaged with primary care. The ACCESS (Advancing Colorectal Cancer Equity through Systematic Screening) initiative evaluates a community-based FIT distribution strategy designed to reach individuals outside traditional primary care pathways. To describe the implementation, feasibility, screening outcomes, follow-up colonoscopy completion, and exploratory financial considerations of the ACCESS initiative at Temple University Hospital (TUH). ACCESS distributed FIT in non-traditional, high-traffic community settings to average-risk individuals aged 45-75 years, consistent with United States Preventive Services Task Force (USPSTF) guidelines. The program was designed to reach individuals facing barriers to routine preventive screening. Among 799 FIT distributed, 293 results were reported, yielding a response rate of 36.7%. Forty-eight FIT were positive, corresponding to a positivity rate of 16.4%. Individuals without a primary care visit in the preceding year had a higher FIT positivity rate than those with a recent primary care visit, although this difference did not reach statistical significance (26.5% vs. 15.1%; p = 0.064). Among men, positivity was 41.4% in those without a recent primary care visit compared with 16.0% in those with a recent visit (p = 0.056). Follow-up colonoscopy was completed at TUH in 29.2% of positive FIT cases, while follow-up outside Temple Health may not have been fully captured. Exploratory financial analysis showed higher average reimbursement per FIT-prompted colonoscopy CPT code compared with non-FIT-prompted colonoscopy codes ($1171.62 vs. $1084.60). ACCESS demonstrates the feasibility of community-based FIT outreach outside traditional primary care pathways and provides insight into screening outcomes, follow-up challenges, and financial considerations within an urban safety-net health system.

The purpose of this study is to provide a theoretical justification and substantive characterization of ESG factor integration into corporate financial analysis for Ukrainian enterprises navigating post-conflict reconstruction and European integration. The research applies general scientific methods: a systemic approach to structure ESG integration components, comparative analysis to contrast conventional financial models with those incorporating non-financial indicators, and classification-analytical techniques to capture how regulatory shifts affect corporate sustainability policies. It is determined that ESG integration covers environmental metrics (emissions, resource use), social dimensions (workforce welfare, community relations), and governance structures (board oversight, ethical conduct), with digitalization serving as a key enabler for responsible management. Institutional changes, including EU alignment requirements, green finance rules, energy security policies, and transparency mandates, create both opportunities to attract responsible investment and additional compliance demands. Four optimization instrument groups are systematized as follows: strategic foresight with scenario modeling, data-driven resource allocation, integrated risk management frameworks, and multi-stakeholder partnerships. Effective ESG integration demands a shift from periodic reporting to continuous monitoring of sustainable development, from experience-based decisions to analytics grounded in non-financial indicators, and from isolated actions to cluster-based collaboration. Integration strategies such as vertical coordination, horizontal alliances, innovation clusters, value-chain partnerships, and digital platforms reduce transaction costs and achieve economies of scale. Further evolution of ESG frameworks requires sustained institutional reforms, climate adaptation, harmonization with EU sustainability standards, and comprehensive monitoring systems that cover financial, operational, and environmental KPIs. Ukrainian enterprises adopting systematic ESG integration will enhance competitiveness in domestic and international markets, ensure long-term resource efficiency, attract post-conflict investments, and contribute to national economic security objectives while advancing sustainable development goals through responsible management practices. Keywords: ESG, sustainable development, non-financial indicators, responsible management, investments, corporate financial analysis.

Forecasting the stock market is a difficult task because of the volatile price movements and complex temporal dependencies. Established models frequently do not realize these unstable trends, which leads to changeable forecasting. This paper introduces a comfortable AI-driven framework that incorporates a sequence-to-prediction transformer model with LLM-based decision-making for accurate and understandable stock price prediction. The prediction starts with a transformer-enabled deep learning approach, which forecasts closing prices for the NIFTY Consumer Durables index based on multi-head attention mechanisms for trend identification. The predicted results are then provided to two decision-making approaches: Neuro-Symbolic AI and an Advanced AI Agent Architecture, to check for accuracy as well as interpretability in financial forecasting. The NSAI model is focused on predictions that are derived from rule-based reasoning, and thus, it ensures that they are in line with actual market behaviors and risk factors. On the other hand, the design of Advanced AI Agent Architecture aimed at enhancing the quality of decisions by utilizing the LLM-based information, bringing in external financial information feeds and memory-based historical data for the trading decision making process. With these AI methods, the model can adapt to changes in market more effectively and, thus, provide more accurate forecasts. This study goes a step further in the development of financial markets analysis by combining deep learning with symbolic reasoning, thereby verifying that the stock market price predictions are not only correct but also interpretable and useable by investors and traders. Through the use of AI-driven stock market strategies, this research work provides a more flexible and explainable way of stock market forecasts, thus it significantly advances the financial market analysis which in turn can be utilized for a better investment method.

Financial analysis increasingly depends on the ability to transform heterogeneous textual evidence into reliable, verifiable, and actionable knowledge. However, adoption in finance requires generated outputs to be not only accurate, but also traceable and auditable. This work presents an audit-oriented LLM-RAG architecture for financial document intelligence. Rather than proposing a new foundation model, the contribution is a reproducible pipeline that integrates financial document processing, hybrid retrieval, evidence-grounded generation, structured validation, and persistent audit artifacts within a state-machine-based workflow. Designed for analyst-facing use, the system produces structured answers linked to explicit evidence while preserving the intermediate artifacts needed to inspect, reproduce, and validate each result. Experiments on AI-FinanceQA, a benchmark of heterogeneous financial documents and analyst-style questions, show that hybrid retrieval with reranking improves evidence selection over single-signal baselines and that the selected LLM backend achieves a compliance-oriented score of Scomp=0.9527. Additional experiments on FinQA confirm that targeted evidence selection improves numerical robustness and semantic alignment compared with uncontrolled context expansion. Overall, the proposed architecture provides an evidence-grounded and audit-oriented framework that supports human review rather than replacing expert financial judgment.

A statistical process monitoring technique requires robust procedures to detect changes other than mean shifts, especially in skewed and heavy tailed financial process. In this paper, the authors develop U-statistic based control charts to deal with complex distributional shifts in monitoring process. Such technique can be useful due to the distribution free property of U-statistics together with their well-established asymptotic properties. Based on these, the following distribution sensitive monitoring statistics have been constructed through Gini coefficient, log scale variability, Theil’s entropy measure, and Atkinson’s indices. To examine their behaviors, a comprehensive Monte Carlo simulation has been carried out by considering four distributions; Gamma, Weibull, Lognormal and Uniform distributions. According to the outcomes of the simulation, it was found that many statistics follow the normality in terms of their behaviors in relation to the sample size. On the contrary, systematic deviations were also found in some cases; mainly Atkinson type indices and entropy-based statistics were found to show the deviation depending upon the high sensitivity parameter and heavy tailenders, respectively. These results will be used to construct the control limits and probability limits by simulations in non-normal distributions. According to this knowledge, Shewhart-type control chart based on U-statistics is developed for detecting various types of distributional changes. By comparing with each other, it has been concluded that all of these statistics give good results in this case. Further, an example is also provided through financial process data.

Portfolio optimization is a fundamental task in financial decision-making, aimed at achieving the best tradeoff between maximizing returns and minimizing risks. A robust optimization model can provide investors with more stable profits by addressing key risk factors effectively. Traditional approaches often assume a normal distribution for financial data, which inadequately accounts for asymmetries and heavy tails. This study introduces the Tail Sharpe Ratio (TSR), a novel criterion designed to address these limitations by incorporating the tail conditional expectation (TCE) and tail variance (TV) under generalized skew-elliptical (GSE) distributions. The TSR criterion provides investors with a robust tool to manage portfolios while considering extreme risks and large potential losses in the tails of return distributions. By leveraging the q-quantile to control tail risks, TSR enhances the precision of risk assessment and portfolio selection. The paper also derives an explicit closed-form solution for optimal portfolio weights using TSR, which significantly simplifies computations, especially for portfolios with extensive assets. Empirical evaluations using real stock data demonstrate that TSR outperforms traditional metrics like Sharpe Ratio (SR) and Mean-Variance (MV) by offering greater risk control. Furthermore, TSR and TMV are shown to be more conservative in minimizing losses, emphasizing their utility for risk-averse investors. This research highlights the importance of adopting asymmetric distribution models in financial analysis, ensuring better alignment with real-world data characteristics, and improving portfolio resilience against extreme market fluctuations. It helps investors avoid portfolios that look good on average but could suffer massive losses in crises.

This study examines the post-merger wealth effects and strategic entrepreneurial behaviour in Indian corporations, with special reference to the merger between HDFC Bank and HDFC Ltd. Mergers and acquisitions are widely used as strategic tools to enhance financial performance, improve market competitiveness, and create shareholder value. The study analyses the impact of the HDFC merger on profitability indicators such as Earnings Per Share (EPS), Return on Assets (ROA), Return on Equity (ROE), Return on Capital Employed (ROCE), and Net Profit Margin (NPM) to evaluate shareholder wealth effects. In addition, the study explores how the merger influences strategic entrepreneurial behaviour by improving operational efficiency, innovation capability, and strategic flexibility. Using comparative financial analysis, trend analysis, and statistical tools, the study identifies the financial and strategic outcomes of the merger. The findings highlight that the merger strengthens long-term value creation and enhances the strategic capabilities of the merged entity in the competitive Indian financial sector. Keywords:- Mergers and Acquisitions, Shareholder Wealth, Strategic Entrepreneurial Behaviour, Profitability Ratios, HDFC Bank, HDFC Ltd, Financial Performance

A 340B View of Pluvicto (Lutetium Lu 177 Vipivotide Tetraxetan): Financial Misalignment in Radioligand Therapy.