An Alternative Safety Profiling Algorithm (ASPA) to transform next generation risk assessment into a structured and transparent process_suppl

The advent of biotechnology has provoked a wide array of reactions ranging from optimism to profound scepticism. Despite an enthusiastic reception from some in the USA and elsewhere, biotechnology has encountered significant resistance in many parts of the world. In particular, biotechnology has raised concerns about the impact of the introduction of genetically modified organisms (GMOs) on human health and the environment. In addition to the scientific controversy relating to the assessment of potential risks of consumption of GMO products or the introduction of GMOs into the environment, there are also societal controversies causing some civil society groups. This chapter aims to explore the level of participation of civil society, identify commonalities and assess transparency in the decision-making processes in three international institutions related to biotechnology: the bodies constituted under the Cartagena Protocol on Bio-Safety, the Codex Alimentarius, and the World Trade Organization. Within the context of these institutions, this paper examines three issues: the normative structure for accommodating participation and inputs from civil society; transparency in the decision-making processes; and the role civil society plays in decision-making and regime formation.

The European Food Safety Authority (EFSA) was asked by its Management Board in 2015 to assess the impact of measures to promote trust and confidence in EFSA's risk assessment process under the Transparency and Engagement in Risk Assessment (TERA) project. EFSA commissioned this study in 2015 to develop a methodology that could be applied to the measures identified in the TERA project and conduct a full appraisal on a sub-set of measures. Thirty-five measures were defined and preliminary costs and benefits were identified through desk research and interviews with 41 EFSA staff members. A full impact appraisal was undertaken for nine measures based on an additional 50 interviews with EFSA staff members, Scientific Committee and Panel Members, Member State representatives, industry representatives, non-governmental organisation (NGOs) and consumer group representatives, and academics. Multi-criteria Mapping (MCM) was used to assess seven of the measures. The results show that measures on framing the mandate, consultation on risk assessment methods, consultation on data, publishing meeting minutes, and transparency through the weight of evidence approach were generally assessed optimistically and there was agreement among interviewees on the implementation approach. These measures could be pursued by EFSA. Commenting on opinions could result in increased transparency and use of evidence, but there was no agreement on the implementation approach. EFSA could conduct a feasibility study for this measure. Publishing application information and proactive release of data were supported by most respondent groups but industry stakeholders expected this measure to perform poorly compared with the other measures. Pre-submission engagement with applicants received widely divergent views about implementation and likely impacts. EFSA should investigate this issue through active engagement with industry to determine whether there is sufficient support to warrant the expenditure. Overall, MCM offers an open and transparent way to compare the different conditions and framings that are likely to affect performance of the measures should they be implemented in line with the TERA objectives.

Artificial Intelligence plays a key role in the Mortgage Industry, assisting financial institutions to elevate efficiency, accuracy, and ‎personalization in loan management. The transformation in the strategies and focus of financial institutions is significantly enhanced by AI. ‎The traditional style of the Mortgage process is rigid, chances to inefficiencies, and is subject to human biases. This paper presents a ‎comprehensive framework derived with AI, aiming to optimize the mortgage cycle. Our approach to integrating machine learning models ‎enhances borrower profiling, credit risk assessment, and loan personalization. We can also improve the model in the future with regulatory ‎compliance and security support in the process of the mortgage industry. The architecture that is proposed here consists of data acquisition ‎and preprocessing, borrow profiling, and risk assessment. The learning techniques that are provided by AI-powered risk assessment models ‎help to improve loan default predictions and ensure fair and transparent lending processes. In addition, the reinforcement learning and ‎clustering techniques enable loan personalization to be more dynamic, improve borrower satisfaction, and create an inclusive environment for ‎the borrowers. Studies also suggest that privacy-preserving methods such as federated learning and security measures based on blockchain‎, safeguard borrower data. With the Implementation of AI AI-driven mortgage optimization process, financial institutions may achieve minimal ‎risk, high operational efficiency, and offer customized lending solutions. This research work surely contributes to the growing AI ‎applications in the financial sector and can provide a scalable framework for mortgage processing‎.

A Decision Support System (DSS) is a supporting tool enhancing a decision-making process. Decision-makers are frequently faced with the problem to take decisions on very complex issues, which requires large data inputs, and a timely decision process. DSSs provide decision makers with a tool to reduce uncertainties, and to simplify and speed-up the decision process as well as to avoid subjectivism induced by the decision-maker and to guarantee transparency of a decision process. The DSS approach has been introduced in the ballast water management (BWM) field and the need primarily arose with the introduction of the selective BWM approach. More precisely, it was recognised that a supporting tool is needed to aid transparency and consistency when deciding on BWM requirements to achieve better environmental protection and lessen burden on vessels. The DSS process starts with communication and data input, continues with risk assessment, BWM decisions, vessel’s action(s), and ends with monitoring and review processes. Throughout the entire decision process information needs to be exchanged with outer (e.g., vessel, other ports) and inner sources (e.g., vessel’s particulars, compliance history), and therefore needs to be supported by adequate communication processes and data management. When required BWM measures were not conducted properly the BWM DSS endpoints range from situations where unmanaged ballast water can be discharged to cases where vessels may be turned away. The chapter provides a detailed step-by-step DSS model which may be used by administrations and other authorities involved in the decision making processes.

A unified approach to selection and qualification of sites for geological storage of CO2 is being developed by key industry players in the CO2QUALSTORE Joint Industry Project. The partners represent companies with diverse roles within CCS; oil and gas companies (BP, BG Group, Petrobras, Shell and StatoilHydro); energy companies (DONG Energy, RWE Dea and Vattenfall); technical consultancy and service providers (Schlumberger and Arup); the IEA Greenhouse Gas R&D Programme; and two Norwegian public enterprises (Gassnova and Gassco). The project is coordinated by DNV, a major provider of independent verification and risk management consultancy services. CO2QUALSTORE aims to produce a guideline that shall help project developers pass internal milestones during the site selection stage of the project life cycle and simultaneously demonstrate compliance with regulations and stakeholder expectations. A primary objective of the qualification workflow is to assist operators, authorities, verifiers and other stakeholders in assuring that storage sites are qualified following a transparent, consistent and cost-effective process. The guideline should lay the groundwork for a risk-based approach where monitoring programs and contingency measures are derived from preceding risk assessments. Furthermore, the guideline aims to facilitate the following processes; identifying, characterizing and selecting the sites that are well suited for CO2 geological storage among a list of candidates;defining qualification criteria and providing the evidence that the site and project will function reliably according to these qualification criteria;verifying that the selected site and its planned management procedures comply with given standards and regulations, and that the available data and management procedures as well as the contingency (remediation) plans provide sufficient confidence that the site will provide long-term storage of CO2. In this paper we present results and conclusions from five satellite projects that test and evaluate critical components of the main steps of the proposed qualification framework. The main objectives of these projects are described below. Site selection offshore Norway: Document the ongoing site selection process for storage of CO2 from two gas-fired power plants, and assess if the documentation provided at the key milestones corresponds with the guideline framework.Site characterisation criteria: Develop site characterisation criteria for geological storage of CO2 in saline aquifers or hydrocarbon reservoirs, and evaluate how to convert the criteria into recommendations.Site verification: Show how monitoring and verification has been carried out on projects in the injection phase, and evaluate how the verification process could be improved and completed.Site selection onshore Germany: Apply the qualification framework to the process of site selection for two planned onshore storage projects in Germany, and identify documentation necessary for regulatory approval processes in compliance with the German mining law, the EC CCS directive, and the upcoming German CCS law (KSPG).Risk assessment and communication with stakeholders: Perform a critical review of risk assessment work conducted for the In Salah project in Algeria and use lessons learned from the In Salah project as a basis for demonstrating effective communication of risk assessment for a CO2 storage site between an operator and stakeholders.

The evolution in the requirements for assessing the benefits and risks of medicinal products has resulted in changes in the evaluation processes. Beyond the separate assessment of benefits and risks, the emphasis is now on the balance between the two, having to justify the potential harms in view of the efficacy claims. In a changing society where the demand is for transparency of such decision-making processes, there is now a major challenge to adequately communicate relevant information to stakeholders. The articulation of benefit–risk decisions remains both a responsibility and an opportunity.

The article carries out a comprehensive and thorough study of legal mechanisms for preventing corruption risks in the process of implementing financial autonomy of higher education institutions in Ukraine. A detailed analysis of the current legislation regulating the sphere of higher education, the financial activities of universities and the anti-corruption policy of the state was carried out, which allowed us to identify key problems and systemic threats that higher education institutions face in their management activities. Special attention is paid to the processes of forming university budgets, distributing financial resources, concluding civil law contracts, managing property and carrying out public procurement, which are the most risky areas in terms of potential corruption manifestations. The work examines in detail the functioning of internal control mechanisms and internal and external audit, assesses their ability to counteract financial abuse and ensure compliance of HEIs with the principles of integrity. The problems of practical implementation of the requirements of anti-corruption legislation in the university environment are summarized, in particular, the imperfection of financial decision-making procedures, insufficient transparency of individual management processes, and the limitations of monitoring and control tools for the use of budgetary and own funds of universities. A separate block of the study is devoted to the international experience of legal regulation of financial autonomy and prevention of corruption in the sphere of higher education. The practices of European countries and recommendations of leading international organizations are analyzed, which allows formulating proposals for adapting the most effective models in the domestic legal field. The author justifies the need to strengthen the institutional control system, improve regulatory acts, introduce mandatory regular assessments of corruption risks, and develop open platforms for university financial reporting. The results of the study have practical significance for improving state anti-corruption policy and increasing the level of integrity in the higher education sector of Ukraine.

Assessing reservoir performance risk in CO2 storage projects

Quantitative uncertainty assessments and the distribution of risk are under scrutiny and significant criticism has been made of null hypothesis testing when careful consideration of Type I (false positive) and II (false negative) error rates have not been taken into account. An alternative method, equivalence testing, is discussed yielding more transparency and potentially more precaution in the quantifiable uncertainty assessments. With thousands of chemicals needing regulation in the near future and low public trust in the regulatory process, decision models are required with transparency and learning processes to manage this task. Adaptive, iterative, and learning decision making tools and processes can help decision makers evaluate the significance of Type I or Type II errors on decision alternatives and can reduce the risk of committing Type III errors (accurate answers to the wrong questions). Simplistic cost-benefit based decision-making tools do not incorporate the complex interconnectedness characterizing environmental risks, nor do they enhance learning, participation, or include social values and ambiguity. Hence, better decision-making tools are required, and MIRA is an attempt to include some of the critical aspects.

This paper introduces a digital taxation model for Nigeria, aimed at standardizing tax collection through advanced technology integration. The model addresses the critical need for modernizing Nigeria’s tax administration to enhance efficiency, accuracy, and compliance in tax collection processes. By leveraging digital technologies, the proposed model seeks to streamline tax administration, reduce manual errors, and improve overall tax revenue management. The model advocates for the implementation of a comprehensive digital tax infrastructure, including the development of an integrated tax management system, digital payment platforms, and real-time data analytics tools. This approach emphasizes the use of application programming interfaces (APIs) for seamless interaction between tax authorities, financial institutions, and taxpayers, ensuring a unified and efficient tax collection system. Blockchain technology is proposed to enhance transparency and traceability, providing an immutable ledger for all transactions, which helps in mitigating fraud and increasing taxpayer confidence. The digital taxation model also includes automated compliance monitoring and reporting systems that utilize artificial intelligence (AI) for predictive analytics and risk assessment. These systems are designed to identify discrepancies, flag potential non-compliance issues, and provide actionable insights to both tax authorities and businesses. Additionally, the model emphasizes the importance of taxpayer education and support, advocating for user-friendly digital platforms and resources to facilitate smooth adoption and compliance. Expected outcomes of the model include increased tax revenue through improved compliance and reduced tax evasion, enhanced operational efficiency for tax authorities, and a more transparent and equitable taxation process. By aligning with global best practices and integrating cutting-edge technologies, the digital taxation model aims to transform Nigeria’s tax administration landscape, fostering a more robust and reliable financial system.

The article examines the theoretical and conceptual foundations of economic and organizational support for business processes in financial and industrial companies operating under conditions of structural transformation, digitalization, and growing economic uncertainty. Business processes are interpreted not merely as regulated operational sequences, but as an integrated economic system that connects production, financial, investment, and managerial contours of value creation. Such an approach enables analysis of business processes as carriers of economic efficiency, cost formation, productivity, and financial sustainability, rather than solely as objects of operational regulation. The study systematizes the evolution of scientific approaches to business process classification and reveals the limitations of traditional functional and linear typologies when applied to complex financial and industrial companies characterized by diversified activities and multi-level corporate structures. Attention is paid to the economic nature of business processes and their role in generating financial results, managing costs, coordinating resource flows, and ensuring long-term organizational performance. A multi-criteria typology of business processes is substantiated, based on their contribution to value creation, managerial horizon, functional and economic contour, level of formalization, and potential for empirical verification. It is argued that integrating process architecture with economic mechanisms for controlling, cost management, performance measurement, and risk assessment provides a methodological basis for improving process efficiency, transparency, and organizational resilience. The article demonstrates that economic support of business processes should be considered a core element of corporate governance, ensuring coherence between operational activities and financial outcomes. The results contribute to the development of a conceptual framework for analyzing business processes from an economic perspective and provide a theoretical foundation for further empirical research into process efficiency and value creation in financial and industrial companies. Keywords: business processes, financial and industrial companies, process approach, business process typology, process orientation, economic support, process analytics.

The article focuses on optimizing the marketing strategy of organic market operators through the implementation of innovative technologies as a crucial tool to enhance their competitiveness in the context of economic digitalization and changing market challenges. Since organic market operators face numerous challenges, including high competition, dynamic consumer preferences, and the need to adapt to business digitalization, innovative technologies have become a key instrument for effective product promotion, strengthening market positions, and achieving sustainable development. The advantages and disadvantages of using innovative technologies in the marketing activities of organic market operators were analyzed. This analysis led to the conclusion that innovative technologies in marketing offer significant opportunities for organic market operators. However, their implementation requires thorough planning, preparation, and risk assessment to minimize drawbacks and maximize potential benefits. The article examines the most effective marketing strategies utilized in the organic product market, emphasizing the unique features of the organic market. Special attention is paid to the organic nature and quality of products, transparency of production processes, and responsibility toward consumers. It has been determined that marketing strategies in the organic product market are not only a means of product promotion but also a tool for creating value for consumers, building long-term relationships, and achieving sustainable development. Based on the conducted research, innovative technologies for optimizing marketing strategies in the organic product market have been developed. Particular attention is given to areas and technologies such as digital marketing and automation, e-commerce and mobile applications, blockchain, innovative advertising approaches, sustainable marketing, and the Internet of Things (IoT). As a result, innovative technologies not only optimize marketing strategies but also create additional value for consumers, providing a competitive advantage for organic market operators. These technologies serve as a tool to ensure sustainable development that meets the challenges of the modern market.

Amendments to the European pharmacovigilance legislative framework are expected to come into force in 2011, following the adoption of the proposed amendments to Directive 2001/83/EC on the community code relating to medicinal products for human use (hereinafter referred to as the Directive) and to Regulation (EC) No. 726/2004 laying down community procedures for the authorization and supervision of medicinal products for human and veterinary use and establishing a European Medicines Agency (EMA) [hereinafter referred to as the Regulation]. The Regulation shall apply 18 months after publication in the Official Journal of the European Union. The amendments to the Directive and the Regulation will induce changes in the EU in terms of evaluation of risk associated with medicinal products as well as the framework on how the EU takes harmonized regulatory action on drug safety. In this review, the text agreed between the European Parliament and Council is examined and compared with the pharmacovigilance legislative framework currently in force. We argue that the new legislation has improved numerous uncertainties in current legislative framework and provides for the following: (i) clear roles, responsibilities and obligations for the key responsible parties; (ii) rationalization of EU decision making on drug safety issues in order to deliver measures that are equally and fully implemented for all relevant products across the community with a view to preventing unnecessary patient exposure to risks; (iii) strengthening medicine safety transparency and communication so that the understanding and trust of patients and health professionals in the safety of medicines will improve, as well as the penetration of key warnings; (iv) strengthening companies' pharmacovigilance systems, allowing companies to improve their systems constantly while reducing administrative burden; (v) ensuring the proactive and proportionate collection of high-quality data relevant to the safety of medicines through risk management and structured data collection in the form of Post-Authorization Safety Studies (PASS), together with rationalized single-case and periodic reporting of suspected adverse drug reactions (ADRs); (vi) involvement of stakeholders in pharmacovigilance through direct patient reporting of suspected ADRs and inclusion of patients and healthcare professionals in decision making; and (vii) simplification of the current community pharmacovigilance procedures with consequent efficiency gains for both the pharmaceutical industry and medicines regulators. For the first time, companies can be made legally liable to carry out PASS and Post-Authorization Efficacy Studies. The amendments to the Regulation and to the Directive will strengthen the European network on pharmacovigilance. A Pharmacovigilance Risk Assessment Committee (PRAC) based at the EMA will be set up, which will be responsible for all matters related to pharmacovigilance at an EU level. Three European databases will be strengthened (EudraVigilance, EudraPharm and the European Pharmacovigilance issues Tracking Tool) as well as the setting up of an EU safety portal to better inform the public on all safety issues being discussed at an EU level. Public hearings at the PRAC will improve transparency in the decision-making process, whilst details and results of all PASS agreed to by the PRAC will also be made publically available.

Correlation-oriented complex system structural risk assessment using Copula and belief rule base

The rapid advancement of Artificial Intelligence (AI) has fundamentally reshaped contemporary security systems, leading to significant improvements in surveillance, threat detection, and incident response across both physical and digital domains. By leveraging machine learning algorithms, neural networks, and predictive analytics, AI enhances the speed and precision of security operations while reducing human error and reliance on purely manual decision-making. In physical security contexts, AI-driven systems analyze real-time video feeds to detect anomalous behavior, identify individuals, and track suspicious movements with high accuracy. In digital security, AI supports malware detection, network intrusion prevention, and phishing mitigation through continuous learning from evolving threat patterns. Moreover, the integration of AI with big data analytics enables comprehensive risk assessment by processing large volumes of heterogeneous data, thereby facilitating early threat identification and proactive mitigation. Natural language processing techniques further contribute by detecting potential insider threats through the analysis of communication patterns and textual data. This paper adopts a qualitative, literature-based methodology grounded in thematic analysis. This paper draws on secondary data obtained from peer-reviewed journal articles, policy reports, and authoritative industry publications related to AI applications in security systems. The analysis synthesizes existing scholarship to identify dominant trends, operational benefits, and critical challenges associated with AI-driven security frameworks.The central objective of this paper is to examine how AI is transforming modern security systems while critically assessing the ethical, legal, and governance challenges that accompany its adoption. The findings indicate that although AI significantly enhances efficiency, predictive capability, and system resilience, persistent concerns related to data privacy, algorithmic bias, transparency, and over-reliance on automated decision-making continue to pose serious risks. This paper underscores the necessity of robust regulatory frameworks, transparent AI decision processes, and sustained human oversight to ensure responsible and trustworthy AI deployment. Ultimately, the paper argues that AI should be viewed not as a replacement for human judgment, but as an augmentative tool that strengthens security systems when embedded within ethical, legal, and organizational safeguards.