Alessandro Morabito,1 Piera Maiolino,2 Stefania D’Auria,3 Roberta D’Aniello,2 Claudia Sandomenico,1 Agnese Montanino,1 Marina Casale,2 Giuliano Palumbo,1 Vincenzo Sforza,1 Raffaele Costanzo,1 Giovanna Esposito,1 Giuseppe Caropreso,4 Anna Manzo,1 Arturo Capasso,5 Bruno Barba,2 Carlo Pannone,3 Loredana Campitiello,3 Antonio Nardone,6 Maria Triassi,6 Simona Damiano,1 Cira Antonietta Forte,1 Amalia Rocco,1 Gianfranco De Feo,7 Maura Tracey,8 Giacomo Pascarella7 1Thoracic Department, Division of Thoracic Medical Oncology, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Napoli, Italy; 2Department of Strategic Health Services, Pharmacy, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Napoli, Italy; 3Department of Strategic Health Services, Hospital Direction, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Napoli, Italy; 4Department of Precision Medicine, Division of Medical Oncology, Università of Campania “Luigi Vanvitelli”, Napoli, Italy; 5Department of Finance and Management, Wroclaw School of Banking Wyzsza Szkoła Bankowa, Wrocalw, Poland; 6Department of Public Health, Università Federico II, Napoli, Italy; 7Department of Scientific Directorate, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Napoli, Italy; 8Department of Strategic Health Services, Rehabilitative Medicine Unit, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Napoli, ItalyCorrespondence: Giacomo Pascarella, Department of Scientific Directorate, Istituto Nazionale Tumori “Fondazione G. Pascale”, IRCCS, Via Mariano Semmola 53, Napoli, 80131, Italy, Tel +39 08117770219, Fax +39 0817702938, Email g.pascarella@istitutotumori.na.itBackground: This study aims to identify and reduce risks that could negatively impact patient safety and organizational aspects related to the different phases of anticancer drug therapy for lung cancer patients in the Day Hospital (DH) care.Methods: From April 2023 until February 2024, a team of multi-disciplinary healthcare professionals of the National Cancer Institute of Naples, Italy, used a modified Delphi approach to identify the care process, the main activities and related risk factors. The severity of these harms and the probability of their occurrence were assessed by applying a 5× 5 semi-quantitative ISO 31000:2018 (ISO 31000) risk matrix. Multiple improvement actions were identified and adopted by the team to reduce the risks to acceptable levels.Results: Nine main activities, 19 correlated potential risks (10 risks for patient safety domain; 53.0%) (9 risks for organizational area; 47.0%) and 19 mitigation measures were identified. The highest risk levels were recognized in the organizational area for: (i) DH Outpatient Visits, due to delays in patients check-in or lab test results or problems with the prescription software; (ii) anticancer drugs administration, for the unavailability of chemotherapy chairs or lack of dedicated nursing staff. Conversely, risk levels for patient safety area were low overall, because several control measures were already in place. Once the mitigation measures were implemented, a new semi-quantitative risk analysis was performed. Risk levels for organizational area changed from a 44.4% to 0.0% in high level, from 44.4% to 67.0% in moderate level, and from 11.2% to 33.0% in minor level. Risk levels for safety areas did not modify for high level (10.0%), but changed from 50.0% to 10.0% in moderate level and increased from 40.0% to 80.0% in minor level.Conclusion: ISO 31000 risk management framework applied to lung cancer DH care could improve both organizational and safety objectives in oncology.Keywords: risk management and assessment, risk matrix, chemotherapy risk, ISO 31000:2018, Day Hospital, lung cancer
Pavel Koptev, Vishnu Kumar, Konstantin Malkov
et al.
Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is managed through the buyer's irrevocable payment undertaking (IPU), which commits to full payment without deductions. However, IPUs can hinder supply chain finance adoption, particularly among sub-invested grade buyers. A newer, data-driven methods use real-time dynamic credit limits, projecting dilution for each buyer-supplier pair in real-time. This paper introduces an AI, machine learning framework and evaluates how that can supplement a deterministic algorithm to predict invoice dilution using extensive production dataset across nine key transaction fields.
These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte Carlo methods, numerical optimization, root-finding, and time-series models commonly used in finance and macro-finance. Particular attention is paid to translating theoretical concepts into reproducible code, emphasizing vectorization, numerical stability, and interpretation of outputs. The notes progressively bridge theory and practice through worked examples and exercises covering asset pricing intuition, risk measurement, forecasting, and empirical analysis. By focusing on clarity, minimal prerequisites, and hands-on computation, these lecture notes aim to serve both as a pedagogical entry point for non-programmers and as a practical reference for applied researchers seeking transparent and replicable quantitative methods in finance.
الملخص:تمثلت أهم أهداف هذا البحث في قياس تأثير الإدارة الالكترونية لعلاقات العملاء بأبعادها (التخصيصية، أمن المعاملات والخصوصية، تعدد طرق الدفع، حل المشكلات، والتغذية الراجعة الكترونيًا) على تحقيق التميز التسويقي لشركات التأمين بــ ج. م. ع، وكذلك التعرف على تأثير خصائص الشركة (نوع النشاط وأسلوب التأمين) على مستوي تطبيق الإدارة الالكترونية لعلاقات العملاء وتحقيق التميز التسويقي لشركات التأمين بــ ج. م. ع. ولتحقيق هذه الأهداف قام الباحثان باستقصاء عبر الانترنت لعينة قوامها (174) مفردة من مسؤولي إدارة علاقات العملاء بشركات التأمين، وتم استخدام أسلوب النمذجة البنائية Structural Equation Modeling ومن أهم النتائج التي تم التوصل إليها: أن الإدارة الالكترونية لعلاقات العملاء تؤثر ايجابيًا على التميز التسويقي. وأن تعدد طرق الدفع هي البعد الأكثر تأثيراً على التميز التسويقي لشركات التأمين، كما أن هناك فروقًا جوهرية بين مستويات تطبيق الإدارة الالكترونية لعلاقات العملاء وفقًا لأسلوب التأمين، وتم تقديم مجموعة من التوصيات يمكن من خلالها رفع مستويات التميز التسويقي لشركات التأمين بــ ج. م. ع.
IntroductionAir pollution poses a threat to public health and socio-economic stability, significantly increasing the disease burden on the population and causing heavy economic impacts, especially in terms of medical expenses. Quantifying this economic impact is crucial for formulating effective public health strategies. This study aims to deeply explore the direct impact of air pollution on specific medical expenses.MethodsThis study utilized the medical data of inpatients in a certain city in southwest China from 2014 to June 2019, the data of key air pollution indicators such as PM2.5 and AQI collected through local monitoring stations, and the data of land use types in this city.Results(1) Air pollution has significantly increased the total hospitalization costs for patients and is a key factor leading to the rise in their medical expenses.Taking the 7-day moving average as an example, a 10 μg/m³ increase in PM2.5 led to a 0.5% rise in total hospitalization costs, equivalent to about 42 yuan per individual. (2) Air pollution has significantly increased the amount of medical insurance reimbursement for patients rather than their out-of-pocket expenses, suggesting that patients tend to mitigate related expenses through insurance reimbursement. (3) Green space area can effectively alleviate the increasing effect of air pollution on hospitalization costs, while industrial land has the opposite effect.The mitigating effect of green spaces on air pollution is most prominent among middle-aged and older adults groups and is more significant under conditions of low wind speed and precipitation.DiscussionAir pollution has exerted economic pressure on both individual patients and the medical security system. The research results can provide important references for optimizing the allocation of medical resources and strengthening health protection to reduce the health and economic burden of air pollution.
Mustafa Raza Rabbani, Madiha Kiran, Zakir Hossen Shaikh
Abstract This study presents a detailed literature review on financing for renewable and sustainable energy through bibliometric analysis and scientific mapping, utilizing the Scopus database from 2000 to 2023. Using network analysis techniques, it identifies eight main clusters, each focusing on different aspects of financing renewable energy and their geographic and technical contexts. The study highlights the most frequently cited articles, notable authors, key institutions, affiliations, and journals in sustainable energy finance. A random effects model meta-analysis was also conducted to assess the overall effect size of each research stream. Findings indicate that the literature on renewable energy finance has expanded since 2000 and exhibits considerable diversity. The study pinpoints five major themes suitable for discussion and exploration of new research questions: (i) the role of Fintech in renewable energy finance, (ii) the regulatory framework governing renewable energy finance, (iii) the economic feasibility of renewable energy in emerging markets, (iv) the influence of private and public finance on renewable energy development, and (v) the relationship between renewable energy finance and sustainable development goals. The insights from this study aim to inspire and equip readers as they embark on their inquiries into the connections between energy investment, policy, finance, and behavioral sciences. Following identifying research gaps, this paper outlines potential future research directions. It serves as a thorough resource on current trends in sustainable energy investments and recommends viable research topics, thus benefiting researchers, professionals, and policymakers alike.
The largest cities in Russia, just like other megacities in the world, are facing the challenges of a new reality. Fiscal policy actively helps to counter these challenges, among other things. The purpose of the study is to identify fiscal tools for large cities to respond to global challenges. The methodology consisted in the fact that, based on a unique database compiled by the authors on the budgets of the ten largest cities in Russia from 2011 to 2021, a comparative study of theirparameters was carried out (tax and non-tax revenues by types, intergovernmental fiscal transfers by types, expenses by industry classification), their structures and dynamics. The results obtained indicate that the cities are very different and a lot of budget parameters, in principle, do not depend on the budget policy of the city, as they are determined by regional legislation, for example, transferred tax deduction standards or transferred spending powers. At the same time, it is concluded that cities retain the ability to pursue an independent budget policy, for example, by managing the structure of expenditures, which was transformed in a certain way during periods of crisis. That is an element of scientific novelty. To a greater extent, this statement is true for such a megacity as Moscow, which really showed a high degree of independence in times of crisis. Other large cities generally do not have sufficient independence in terms of opportunities to replenish their budgets and spend funds, and they have to rely on transfers from budgets at a higher level. The practical significance for higher-level government bodies lies in confirming the thesis about the need to implement a differentiated budget policy for cities that fall into different groups according to the level of budgetaryprovision.
Online retail platforms such as Amazon and Tmall have the ability to create personalized recommendations based on the consumer’s browsing history, purchase history, and preferences by investing in data analytics capability. In practice, manufacturers may encroach on the retail market through the agency channel that sells products directly to online consumers in addition to wholesale products to retail platforms through the reselling channel. In this study, we develop a game-theoretic model to study the interplay between the manufacturer’s encroachment and the online retail platform’s data analytics capability investment. Our outcomes reveal that the conditions for the manufacturer to encroach become more lenient if the platform invests in data analytics capability, and we show that the investment in data analytics capability can lead to a Pareto improvement and the manufacturer can free ride on the platform’s investment. Moreover, we found that the manufacturer’s encroachment always creates more incentives for the platform to enhance the investment level in data analytics capability. Our research in this study provides useful insights for managers to make encroachment decisions and data analytics capability investment decisions with the manufacturer who sells through the online retail platform.
Richard Hardiman, Anders Branth Pedersen, Anne Jensen
et al.
This paper analyses the degree and types of public participation in Nature-Based Solutions (NBS) projects in China. The paper is based upon the premise that NBS affect multiple aspects of the city as a place for the daily lives and activities of citizens and that NBS implementation can benefit from citizen and stakeholder involvement. NBS thus offer a platform for stimulating engagement between the local government and the public. Case studies are examined through a literature review, site visits, and interviews with researchers, local officials and NGOs in China. The paper indicates that there has been significant progress since the 1990s in formal requirements of public participation through Chinese legislation promoting the inclusiveness of the public in environmental and NBS decision-making, and acknowledgment of the importance of NGOs, however actual implementation of soliciting public opinion and involvement in NBS project design has been more limited. The case studies suggest that the level of involvement of the public in NBS activities and decision-making is the reciprocal of the size of the project, where there is a high-level of involvement in the smaller local projects, but minimal involvement in larger-scale NBS projects. We find that Public Private Partnerships (PPPs) have a significant potential to help finance NBS projects providing the project can forecast low risk and positive revenue for investors, therefore this model would merit further exploration. However, PPPs may also involve limited public participation by citizens and stakeholders beyond private companies and may therefore require targeted efforts to address local communities’ needs and interests. Local people are the most important (and willing) actors and opinionators in projects that directly affect their lives, livelihoods and well-being. The findings highlight the important role of NGOs in promoting and facilitating public participation, and accompanying co-benefits, in several of the Chinese case studies. Our study also suggests that symbiosis between local governments and the citizens could be invoked by local community-based organisations (e.g. Community Resident Committees or similar) that can act as a liaison point and catalyst to public participation in NBS projects, although significant training would also be required.
The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model’s accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy.
Alireza Mohammadshafie, Akram Mirzaeinia, Haseebullah Jumakhan
et al.
Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance applications. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards. While PPO and SAC engage in significant trades with a limited number of stocks, DDPG and TD3 adopt a more balanced approach. Furthermore, SAC and PPO tend to hold positions for shorter durations, whereas DDPG, A2C, and TD3 display a propensity to remain stationary for extended periods.
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components' relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners.
As an introduction to a Special Issue of International Journal of Theoretical and Applied Finance in Honour of the Memory of Thomas Robert Hurd we present a brief synopsis of Tom Hurd's scientific career and a bibliography of his scientific publications.
The purpose of the research is to study certain aspects of transformation of corporate relations and corporate law, in the light of blurring of company boundaries and development of digital communications and startup culture. The results and conclusions are obtained based on general scientific and private scientific methods of research. Research studies examples of quasi-corporate and quasi-public structures created through (1) deferred share transfer agreements (SAFE), (2) crowdfunding agreements, (3) public market intermediary companies (SPAC), (4) exchanges for private companies. With the emergence of cross-border hybrid corporate structures at intersection of debt and equity, public and private financing, the current debates on the objectives of corporate law and the purpose of the corporation acquire a new perspective, as the very boundaries of the corporation become fluid. At the same time, decision-making procedures are mediated by third parties and digital technologies, with the prospect of conflict of interest, and are regulated, among other things, by standards emerging so to say from below. In the digital era, flexibility in the choice of elements of the structure allows to detach from the current state corporate regulation and attach to it in the right place and at the right time. It is concluded that the explosive growth of hybrid structures forms new areas of development of legal regulation by rejecting the dichotomy of soft and hard law. Among the vectors of transformation, the growing importance of private law unifications, the development of theoretical apparatus based on the concept of transnational law, the emergence of its subsystems, including lex corporatoria are noted. Among the tasks of lex corporatoria the formation of standards and customs in the field of corporate finance is also postulated as a regulatory framework for functioning of the hybrid structures under study.
Climate mitigation decisions today affect future generations, raising questions of intergenerational equity. Integrated assessment models (IAMs) rely on discounting to evaluate long-term policy costs and benefits. Using the DICE model, we quantify how optimal pathways distribute abatement and damage costs across cohorts. Unconstrained optimization creates intergenerational inequality, with future generations bearing higher costs relative to GDP. Extending the model with stochastic discount rates, we show that discount-rate uncertainty significantly amplifies this inequality. We consider two independent extensions: the financing of abatement costs and the modeling of nonlinear financing costs under large damages. Both extensions can materially improve intergenerational equity by distributing mitigation efforts more evenly. As an illustration, we present a modified DICE model whose optimal pathway limits generational costs to 3 % of GDP, leading to more equitable effort sharing. Our proposed model extensions are model-agnostic, applicable across IAMs, and compatible with alternative intergenerational equity metrics.
The debate surrounding the provision of welfare by state institutions has been widely discussed in the field of political economics since the 1930s. Related research also focuses on welfare supply at an international system level. This article assesses whether international cooperation in the area of sharing remote sensing data leads to the supply of global public goods, which to date has not yet been discussed in related scholarly literature. The supply of global public goods is assessed within the GEO international regime and leads to the use of the non-rivalrous GEOSS, which can be accessed by every socio-economic group in every UN member country including future generations. However, providing the benefit of GEOSS is not always favourable because of the low number of financially participating consumers.
We present a simple unifying treatment of a broad class of applications from statistical mechanics, econometrics, mathematical finance, and insurance mathematics, where (possibly subordinated) Lévy noise arises as a scaling limit of some form of continuous-time random walk (CTRW). For each application, it is natural to rely on weak convergence results for stochastic integrals on Skorokhod space in Skorokhod's J1 or M1 topologies. As compared to earlier and entirely separate works, we are able to give a more streamlined account while also allowing for greater generality and providing important new insights. For each application, we first elucidate how the fundamental conclusions for J1 convergent CTRWs emerge as special cases of the same general principles, and we then illustrate how the specific settings give rise to different results for strictly M1 convergent CTRWs.