Communicating Biodiversity Data Restriction Rationales: Balancing Specificity with Practical and Ethical Considerations

Skin cancer rates are on the rise globally. School sun safety programmes are recommended by the World Health Organization to reduce the risk of future skin cancer at the population level; however, these are encouraged but not mandated in Wales. To explore current sun protection practices and sun safety education in primary schools in Wales and whether these are linked to the existence of a formal sun safety policy. An online survey to all 1241 Welsh primary schools asking about sun safety practices, education and formal policies was undertaken. In total, 471 (38.0%) schools responded. A minority (22/469, 4.7%) of responding schools reported they had sufficient shade for most activities. In the spring and summer terms, almost two-thirds of schools encourage hat wearing (304/469, 64.8%) and sunscreen (296/468, 63.2%). Although nearly all schools reported that parents were encouraged to apply sunscreen to students before school (449/469, 95.7%), there was wide variation in other sunscreen application practices. Less than one-third of schools (129/445, 29.0%) reported that they include sun protection education in the curriculum in every year group, with 11.7% (52/445) including this in certain years only. Schools with a formal policy were more likely to report more comprehensive sun protection practices, including having sufficient shade [odds ratio (OR) 1.51, 95% confidence interval (CI) 1.04-2.19; P = 0.03], having spare hats for pupils to wear (OR 1.59, 95% CI 1.07-2.37; P = 0.02), providing guidance for staff (OR 5.87, 95% CI 3.05-11.28; P < 0.001), encouraging them to model sun safe behaviours (OR 1.82, 95% CI 1.18-2.80; P = 0.007) and teaching sun protection education as part of the curriculum in every year group (OR 2.56, 95% CI 1.76-3.71; P < 0.001). With respect to sunscreen, the existence of a formal policy did not seem to affect a school's practice. Although in most cases, the existence of a formal policy suggests more comprehensive sun protection practices and education in schools, sun protection measures and education need improvement across the primary school sector in Wales to reverse rising skin cancer rates.

Due to the development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, the finance industry is undergoing a substantial transformation. These technologies have the potential to revolutionize several facets of finance, such as risk management, trading, fraud detection, and decision-making processes. Financial institutions must comprehend the future implications of AI and ML in finance in order to remain competitive and reap the benefits of these technologies. This paper examines the future prospects of AI and ML in finance in detail. A systematic review of existing literature, academic journals, industry reports, and case studies pertaining to AI and ML in finance constitutes the research methodology. The findings of the literature review are synthesized in order to identify key future trends, challenges, and opportunities for AI and ML in finance. The results reveal a number of implications and advantages for the future of finance, which will be driven by AI and ML. These technologies have the potential to improve operational efficiency, automate processes, enhance risk assessment and management, personalize customer experiences, and facilitate more precise decision-making in the finance industry. The future of finance will be characterized by greater collaboration between humans and machines, with AI and ML algorithms augmenting rather than replacing human decision-making.AI and ML technologies have transformative implications for the future of finance and financial institutions. The adoption of these technologies will allow financial institutions to utilise data-driven insights, automate routine tasks, and improve customer experiences. To ensure responsible and accountable use of AI and ML in finance, however, ethical considerations such as fairness, transparency, and data privacy must be addressed. Policymakers and industry stakeholders should work together to create regulatory frameworks that foster innovation while preserving consumer protection and market stability. Future research should concentrate on addressing the challenges and opportunities associated with the future of AI and ML in finance, such as ethical and regulatory considerations, the interpretability of AI models, and data quality and governance.

1 Introduction to the Challenge of Cost and Value Management in Projects. 1.1 Importance of Cost and Value Management in Projects. 1.2 Keys to Effective Project Cost Management. 1.3 Essential Features of Project Value Management. 1.4 Organization of the Book. 2 Project Needs Assessment, Concept Development, and Planning. 2.1 Needs Identification. 2.2 Conceptual Development. 2.3 The Statement of Work. 2.4 Project Planning. 2.5 Project Scope Definition. 2.6 Work Breakdown Structure. 3 Cost Estimation. 3.1 Importance of Cost Estimation. 3.2 Problems of Cost Estimation. 3.3 Sources and Categories of Project Costs. 3.4 Cost Estimating Methods. 3.5 Cost Estimation Process. 3.6 Allowances for Contingencies in Cost Estimation. 3.7 The Use of Learning Curves in Cost Estimation. 4 Project Budgeting. 4.1 Issues in Project Budgeting. 4.2 Developing a Project Budget. 4.3 Approaches to Developing a Project Budget. 4.4 Activity-based Costing. 4.5 Program Budgeting. 4.6 Developing a Project Contingency Budget. 4.7 Issues in Budget Development. 4.8 Crashing the Project: Budget Effects. Crashing Project Activities-Decision Making. 5 Project Cost Control. 5.1 Overview of the Project Evaluation and Control System. 5.2 Integrating Cost and Time in Monitoring Project Performance: The S-Curve. 5.3 Earned Value Management. 5.4 Earned Value Management Model. 5.5 Fundamentals of Earned Value. 5.6 EVM Terminology. 5.7 Relevancy of Earned Value Management. 5.8 Conducting an Earned Value Analysis. 5.9 Performing an Earned Value Assessment. 5.10 Managing a Portfolio of Projects with Earned Value Management. 5.11 Important Issues in the Effective Use of Earned Value Management. 6 Cash Flow Management. 6.1 The Concept of Cash Flow. 6.2 Cash Flow and the Worth of Projects. 6.3 Payment Arrangements. 7 Financial Management in Projects. 7.1 Financing of Projects Versus Project Finance. 7.2 Principles of Financing Projects. 7.3 Types and Sources of Finance. 7.4 Sources of Finance. 7.5 Cost of Financing. 7.6 Project Finance. 7.7 The Process of Project Financial Management. 8 Value Management. 8.1 Concept of Value. 8.2 Dimensions and Measures of Value. 8.3 Overview of Value Management. 8.4 Value Management Terms. 8.5 Need for Value Management in Projects. 8.6 The Value Management Approach. 8.7 The VM Process. 8.8 Benefits of Value Management. 8.9 Other VM Requirements. 8.10 Value Management Reviews. 8.11 Relationship between Project Value and Risk. 8.12 Value Management as an Aid to Risk Assessment. 8.13 An Example of How VM and Risk Management Interrelate. 9 Change Control and Configuration Management. 9.1 Causes of Changes. 9.2 Influence of Changes. 9.3 Configuration Management. 9.4 Configuration Management Standards. 9.5 The CM Process. 9.6 Control of Changes. 9.7 Change Control Procedure and Configuration Control. 9.8 Responsibility for the Control of Changes. 9.9 Crisis Management. 9.10 An Example of Configuration Management. 10 Supply Chain Management. 10.1 What Is Supply Chain Management? 10.2 The Need to Manage Supply Chains. 10.3 SCM Benefits. 10.4 Critical Areas of SCM. 10.5 SCM Issues in Project Management. 10.6 Value Drivers in Project Supply Chain Management. 10.7 Optimizing Value in Project Supply Chains. 10.8 Project Supply Chain Process Framework. 10.9 Integrating the Supply Chain. 10.10 Performance Metrics in Project Supply Chain Management. 10.11 Project Supply Chain Metrics and the Supply Chain Operations Reference (SCOR) Model. 10.12 Future Issues in Project Supply Chain Management. 11 Quality Management in Projects. 11.1 Definition of Quality in Projects. 11.2 Elements of Project Quality. 11.3 Total Quality Management (TQM) in Projects. 11.4 Quality Management Methods for a Project Organization. 11.5 Quality Standards for Projects. 12 Integrating Cost and Value in Projects. 12.1 The Project Value Chain. 12.2 Project Value Chain Analysis. 12.3 Sources and Strategies for Integrating Cost and Value in Projects. 12.4 Integrated Value and Risk Management. 12.5 The Project Cost and Value Integration Process. Index.

Skin cancer rates are on the rise globally. School sun safety programmes are recommended by the World Health Organization to reduce the risk of future skin cancer at the population level; however, these are encouraged but not mandated in Wales. Our study aimed to explore current sun protection practices and sun safety education in primary schools in Wales, and whether these are linked to the existence of a formal sun safety policy. To do this, we distributed an online survey to all 1241 Welsh primary schools asking about sun safety practices, education and formal policies. In total, 471 (38.0%) schools responded to our survey, with the characteristics of responding schools generally matching the profile of schools in Wales. A minority (22, 4.7%) of responding schools reported they had sufficient shade for most activities. In the spring and summer terms almost two-thirds of schools encourage hat wearing (304, 64.8% of available data) and suncream (296, 63.2%). While nearly all schools reported that parents were encouraged to apply suncream to students before school (449, 95.7%), there was wide variation in other suncream application practices. Less than one-third of schools (129, 29.0%) reported that they include sun protection education in the curriculum in every year group, with 11.7% (52) including this in certain years only. Schools with a formal sun safety policy were more likely to report more comprehensive sun protection practices, including having sufficient shade [odds ratio (OR) 1.51, 95% confidence interval (CI) 1.04–2.19; P = 0.03], having spare hats for pupils to wear (OR 1.59, 95% CI 1.07–2.37; P = 0.02), providing guidance for staff (OR 5.87, 95% CI 3.05–11.3; P &amp;lt; 0.001), encouraging them to model sun safe behaviours (OR 1.82, 95% CI 1.18–2.80; P = 0.007) and teaching sun protection education as part of the curriculum in every year group (OR 2.56, 95% CI 1.76–3.71; P &amp;lt; 0.001). The existence of a formal policy did not seem to affect a school’s practice with respect to suncream. While in most cases, the existence of a formal policy suggests more comprehensive sun protection practices and education in schools in Wales, sun protection measures and education need improvement across the primary school sector to reverse rising skin cancer rates.

This qualitative study investigates the management of supplier risks in e-commerce through relationship management strategies, focusing on trust-building, communication practices, digital technology integration, risk assessment methodologies, challenges, innovation initiatives, and ethical considerations. The research aims to provide nuanced insights into how e-commerce companies navigate the complexities of global supply chains to mitigate operational, financial, and reputational risks associated with supplier relationships. Through semi-structured interviews with 20 key stakeholders in the e-commerce sector, data were collected and analyzed using thematic analysis. The findings underscored the foundational role of trust in establishing resilient supplier relationships, facilitated by transparent communication, consistent performance evaluation, and mutual respect for contractual obligations. Effective communication practices, supported by digital platforms and technologies such as AI, blockchain, IoT, and cloud computing, enhanced operational transparency and decision-making capabilities across supply chains. The study identified diverse approaches to risk assessment, from qualitative evaluations to quantitative models utilizing predictive analytics and scenario planning. However, participants highlighted challenges including geopolitical uncertainties, trade disruptions, regulatory changes, and cultural barriers, necessitating adaptive strategies and diversified sourcing options. Innovation initiatives such as joint product development and technology adoption were crucial in enhancing supply chain agility and competitive advantage. Ethical considerations emerged as a critical aspect, influencing supplier selection criteria, CSR initiatives, environmental sustainability practices, and labor standards compliance. Integrating ethical guidelines into supplier contracts reinforced corporate values and enhanced brand reputation. Overall, this study contributes practical implications for e-commerce practitioners seeking to enhance supply chain resilience, mitigate supplier risks, and sustain competitive advantage in a dynamic global marketplace.

Offering fertility preservation (FP) prior to gonadotoxic therapy, including cancer care and gender-affirming treatment, is now considered standard of care. Periodically, parents and children disagree about whether to pursue FP. However, it is unknown how often this occurs and how disagreement is handled when it arises. Moreover, there is no clear guidance on how to resolve these difficult situations. The purpose of this scoping review is to provide an overview of available research evidence about parent-child disagreement regarding FP in order to establish that disagreement occurs in practice, understand the basis for disagreement and explore suggestions for how such disputes could be resolved. Based on our findings, we offer a discussion of the ethical principles at stake when disagreement occurs, which can be used to guide clinicians' approaches when these challenging scenarios present. A comprehensive literature search was run in several databases, including PubMed/Medline, Embase and the Cochrane Library. The search was performed in February 2021 and updated in August 2021. Articles were included in the final review if they discussed how parents or children wanted their views on FP taken into account, presented evidence that parent-child discordance regarding FP exists, discussed how to handle disagreement in a particular case or offered general suggestions for how to approach parent-child discordance about FP. Studies were excluded if the patients were adult only (age 18 years and older), pertained to fertility-sparing treatments (e.g. gonad shielding, gonadopexy) rather than fertility-preserving treatments (e.g. testicular tissue cryopreservation, ovarian tissue cryopreservation, oocyte cryopreservation or sperm cryopreservation) or explored the views of clinicians but not patients or parents. Meta-synthesis was used to synthesize and interpret data across included studies and thematic analysis was used to identify common patterns and themes. In total, 755 publications were screened, 118 studies underwent full-text review and 35 studies were included in the final review. Of these studies, 7 discussed how parents or children wanted their opinions to be incorporated, 11 presented evidence that discordance exists between parents and children regarding FP, 4 discussed how disagreement was handled in a particular case and 21 offered general suggestions for how to approach parent-child disagreement. There was a range of study designs, including quantitative and qualitative studies, case studies, ethical analyses and commentaries. From the thematic analysis, four general themes regarding FP disagreement emerged, and four themes relating to the ethical principles at stake in parent-child disagreement were identified. The general themes were: adolescents typically desire to participate in FP decision-making; some parents prefer not to involve their children; minors may feel more favorably about FP than their parents; and transgender minors and their parents may have unique reasons for disagreement. The ethical principles that were identified were: minor's best interest; right to an open future; minor's autonomy; and parental autonomy. This study offers an overview of available research on the topic of parent-child disagreement regarding FP and discusses the ethical considerations at stake when disagreement occurs. The findings can be used to inform guidance for clinicians presented with FP disagreement in practice.

Social networks are a popular medium for building and maintaining a professional network. Many studies exist on general communication and connection practices within these networks. However, studies on expertise search suggest the existence of subgroups centered around a particular profession. In this paper, we analyze commonalities and differences between these groups, based on a set of 94,155 public user profiles. The results confirm that such subgroups can be recognized. Further, the average number of connections differs between groups, as a result of differences in intention for using social media. Similarly, within the groups, specific topics and resources are discussed and shared, and there are interesting differences in the tone and wording the group members use. These insights are relevant for interpreting results from social media analyses and can be used for identifying group-specific resources and communication practices that new members may want to know about.

Knowledge of variation in population processes (e.g. population growth) across broad spatiotemporal scales is fundamental to population ecology and critical for conservation decision‐making. Count data from rigorous surveys (e.g. surveys with probabilistic sampling design and distance sampling information) can inform population processes but are often limited in space and time. Participatory science data cover broader spatiotemporal extents but are prone to bias due to limited to no sampling design and lack of distance sampling information, hindering their capability of informing population processes. Here, we developed an integrated dynamic N‐mixture model that jointly analyses rigorous survey and participatory science data to inform population growth at broad spatiotemporal extents. The model contains a flexible scaling parameter that allows fixed and random effects to account for biases and errors in participatory science data. We conducted simulations to evaluate the inference performance of this model across a broad range of spatial and temporal overlap between rigorous survey and participatory science data. We also conducted a case study of Baird's Sparrow (Centronyx bairdii), a species of conservation concern, to illustrate the application of the integrated model with rigorous survey data from the Integrated Monitoring in Bird Conservation Regions programme and participatory science North American Breeding Bird Survey and eBird data. Simulations showed that the integrated model improved precision without biasing parameter estimates, in comparison with a model informed by rigorous survey data alone. The case study further demonstrated the utility of the integrated model for quantifying range‐wide, long‐term population processes and environmental drivers despite limited spatiotemporal extent of rigorous survey data. In particular, we found that population growth rate peaked under medium temperature, which were only apparent in the integrated model. The integrated model developed in this study is useful for understanding wildlife population processes at broad spatiotemporal scales with count data. The flexible structure of this model, in particular the scaling parameter, makes it highly adaptable to a broad range of ecological systems and survey procedures. These properties make this modelling approach highly relevant for both population ecology and conservation practice.

This study explores the adoption and impact of artificial intelligence (AI) and machine learning (ML) in financial markets, utilizing a mixed-methods approach that includes a quantitative survey and a qualitative analysis of existing research papers, reports, and articles. The quantitative results demonstrate the growing adoption of AI and ML technologies in financial institutions and their most common applications, such as algorithmic trading, risk management, fraud detection, credit scoring, and customer service. Additionally, the qualitative analysis identifies key themes, including AI and ML adoption trends, challenges and barriers to adoption, the role of regulation, workforce transformation, and ethical and social considerations. The study highlights the need for financial professionals to adapt their skills and for organizations to address challenges, such as data privacy concerns, regulatory compliance, and ethical considerations. The research contributes to the knowledge on AI and ML in finance, helping policymakers, regulators, and professionals understand their benefits and challenges.

The integration of machine learning (ML) techniques in financial markets has revolutionized traditional trading and risk management strategies, offering unprecedented opportunities and challenges. This paper provides a comprehensive and critical review of the application of ML in algorithmic trading and risk management within the realm of financial markets. The review begins by exploring the evolution of algorithmic trading, highlighting the paradigm shift from traditional rule-based strategies to ML-driven approaches. Various ML algorithms, including neural networks, decision trees, and ensemble methods, are examined in the context of their application to predictive modeling, pattern recognition, and signal generation for trading purposes. The paper also delves into the challenges and limitations associated with the adoption of ML in financial markets. Issues such as overfitting, data bias, and model interpretability are discussed, emphasizing the importance of addressing these concerns to ensure robust and reliable trading systems. Furthermore, ethical considerations and potential regulatory implications of ML-driven trading strategies are considered in the context of market fairness and stability. In the realm of risk management, the review scrutinizes the role of ML in assessing and mitigating financial risks. The paper evaluates the effectiveness of ML models in identifying market trends, measuring portfolio risk, and optimizing asset allocation. Additionally, it examines the potential impact of ML on systemic risk and the need for adaptive risk management frameworks in dynamic market conditions. The synthesis of findings underscores the transformative impact of ML on financial markets, showcasing its potential to enhance trading strategies and risk management practices. However, the review also highlights the importance of addressing inherent challenges and ethical considerations to ensure the responsible and sustainable integration of ML in the financial domain. This critical review provides valuable insights into the current state of machine learning in financial markets, offering a foundation for future research directions and the development of best practices in algorithmic trading and risk management.

Risk assessment, communication and management has become a topic of increasing interest across many sectors of society: from stockbrokers to firefighters to politicians. One of the most complex areas of its application is ‘health’, with general practice arguably being one of the most complex contexts within that sector.1 The Oxford Dictionary defines ‘risk’ more broadly as ‘a situation involving exposure to danger’ and current clinical definitions often define ‘risk communication’ as ‘ the open two-way exchange of information and opinion about risk, leading to better understanding and better decisions about clinical management ’.2 ‘Risk management’ on the other hand, involves ‘the forecasting and evaluation of risks together with the identification of procedures to avoid or minimise their impact’. Better ‘risk communication’ is therefore one potential strategy for ‘risk management’.3 This issue of the BJGP focuses on Risk & Safety with several papers highlighting the complexity of risk assessment, communication and management in general practice. Spencer and colleagues 3 take a ‘systems-level’ approach to risk management through refinement of a set of prescribing safety indicators by a panel of GPs and a subset considered to be associated with high or extreme risk to patients. The authors plan to develop a computer-based toolkit enabling individual GP audits, citing the PINCER trial as a successful intervention for reducing prescribing errors.4 However, the PINCER intervention focused on only three prescribing indicators and …

The rapid development of Artificial Intelligence (AI) has raised concerns regarding the potential risks it may pose to humans, society, and the environment. Recent advancements have intensified these concerns, emphasizing the need for a deeper understanding of the technical, societal, and ethical aspects that could lead to adverse or harmful failures in decisions made by autonomous and intelligent systems (AIS). This paper aims to examine the ethical dimensions of risk management in AIS. Its objective is to highlight the significance of ethical considerations in mitigating risks associated with the development, deployment, and use of AIS. The paper provides an overview of various types of AI risks and risk management procedures aimed at mitigating the negative impacts of those risks. We employ a comprehensive risk management approach that combines technical expertise with ethical analysis to ensure alignment with human values and societal objectives. Through the analysis of AI risks and risk management procedures, we advocate for establishing effective mechanisms for ethical oversight and legal control to promote ethical and trustworthy AIS. The findings reveal key risks associated with transparency, accountability, privacy infringement, algorithmic bias, and unintended consequences. To address these challenges, we consider integrating ethical principles into risk management practices, transparent risk communication, continuous engagement with all stakeholders, establishing robust accountability mechanisms, and regular ethical oversight as imperative in ethically designing and operating AI systems. Given the diminished effectiveness of internal audits compared to external audits, we also recommend the implementation of regular monitoring mechanisms through independent external audits when evaluating risk management practices.

Here, we discuss mining and summarizing landmarks' general themes as well as the local and temporal themes. General themes occur extensively in various landmarks, and include accommodations and other standard features. The local theme implies a specific theme that exists only at a certain landmark, such as a unique physical characteristic. The temporal theme corresponds to the location-time-representative pattern, which relates only to a certain landmark during a certain period-such as fleet week at the Golden Gate Bridge or red maple leaves in Kiyomizu-dera. Local themes are useful in landmark analysis for their discriminative and representative attributes. However, the ability to discover landmark diversity at different moments makes temporal themes equally important in landmark studies. Time dependent diversity shows complete viewing angles over time and complements local themes in landmark understanding. Furthermore, it provides more comprehensive and structured information for landmark history browsing and tourist decision making. We propose a probabilistic topic model called Multimodal Spatio-Temporal Theme Modeling (mmSTTM). The model considers both textual and visual contexts to learn general, local, and temporal themes, which span a low-dimensional theme space. The model also assigns all textual and visual keywords to each theme, along with a probability for each; a keyword with high weight assignment is meaningful for the theme, while low-weighted keywords are considered noise.

The current paper aims to: (1) examine clinical practice guidelines in suicide prevention across fields, organizations, and clinical specialties and (2) inform emerging standards in clinical practice, research, and training. The authors conducted a systematic literature review to identify clinical practice guidelines and resource documents in suicide prevention and risk management. The authors used PubMed, Google Scholar, and Google Search, and keywords included: clinical practice guideline, practice guideline, practice parameters, suicide, suicidality, suicidal behaviors, assessment, and management. To assess for commonalities, the authors reviewed guidelines and resource documents across 13 key content categories and assessed whether each document suggested validated assessment measures. The search generated 101 source documents, which included N = 10 clinical practice guidelines and N = 12 additional resource documents (e.g., non-formalized guidelines, tool-kits). All guidelines (100%) provided detailed recommendations for the use of evidence-based risk factors and protective factors, 80% provided brief (but not detailed) recommendations for the assessment of suicidal intent, and 70% recommended risk management strategies. By comparison, only 30% discussed standardization of risk-level categorizations and other content areas considered central to best practices in suicide prevention (e.g., restricting access to means, ethical considerations, confidentiality/legal issues, training, and postvention practices). Resource documents were largely consistent with these findings. Current guidelines address similar aspects of suicide risk assessment and management, but significant discrepancies exist. A lack of consensus was evident in recommendations across core competencies, which may be improved by increased standardization in practice and training. Additional resources appear useful for supplemental use.

Abstract: This chapter explores the transformative role of machine learning (ML) and deep learning (DL) in financial market predictions, focusing on strategies for effective risk and volatility management. Beginning with foundational ML models like linear regression, logistic regression, and decision trees, it introduces techniques for asset price and volatility prediction. Advanced DL models, including RNNs, LSTMs, CNNs, and GANs, are discussed for their unique capabilities in time-series analysis, sentiment-driven predictions, and market simulation. Emphasis is placed on ML/DL-based methods for volatility forecasting, portfolio optimization, and real-time sentiment analysis, showcasing their adaptability in high-frequency trading and adaptive risk strategies. The chapter also addresses challenges in model interpretability, data privacy, and algorithmic fairness, highlighting ethical considerations crucial for responsible AI implementation in finance. By reviewing cutting-edge advancements like quantum computing, federated learning, and hybrid models, it provides a forward-looking view of AI’s evolving role in enhancing predictive accuracy and resilience in volatile markets. Keywords: Machine learning, deep learning, financial market predictions, risk management, volatility management, asset price prediction, time-series analysis, RNN, LSTM, CNN, GAN, portfolio optimization, sentiment analysis, model interpretability, data privacy, algorithmic fairness, quantum computing, federated learning, hybrid models, AI in finance