Search Results (159)

We examine whether board activity (B_ACTIV), board size (B_SIZE), board independence (BIND), and board tenure (BOARD_TEN) are associated with the performance of South African real estate investment trusts (REITs) over the period 2013 to 2025. The REIT framework provides a rigorous setting to evaluate corporate governance theory, as statutory distribution mandates constrain payout discretion and contracted-income business models limit managerial opportunism, suggesting that governance effects concentrate within specific performance channels. We estimate dynamic panel models using a two-step system GMM framework with collapsed instruments, year fixed effects, Windmeijer-corrected standard errors, and firm-level controls for firm size (SIZE), leverage (LEV), and asset growth (GROWTH) to address endogeneity, unobserved heterogeneity, and performance persistence. We evaluate robustness through an endogenous-regressor specification, a bootstrap bias-corrected LSDVC estimator, and outlier-adjusted estimations. The sample comprises 30 JSE-listed REITs. We evaluate performance across funds from operations per share (FFO_PS), dividend yield (DIV_YIELD), return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), and earnings per share (EPS). Our findings reveal that B_SIZE exhibits a statistically significant negative association with accounting profitability, where each additional director corresponds to a 1.0 percentage point reduction in ROE and a 0.32 percentage point reduction in ROA. The ROE effect remains robust across every identification strategy, including specifications treating board composition as endogenous and estimations winsorizing the dependent variables. Because firm SIZE remains statistically insignificant while LEV and GROWTH display their expected theoretical signs, the B_SIZE effect is isolated from firm scale. BIND demonstrates a directionally positive but specification-sensitive association with returns and payouts, whereas BOARD_TEN shows no robust association with any performance metric, and B_ACTIV effects attenuate once endogeneity is addressed. Overall, governance effects concentrate in operating efficiency and payout measures while remaining absent from per-share metrics, reflecting the precise channels through which boards exercise authority. Our findings caution against board expansion in this sector, highlight board scale as a transparent governance screen for investors, and demonstrate that meeting frequency and tenure benchmarks offer no reliable performance signal. Full article

Academic departments allocate scarce research resources under deep uncertainty as today’s choices about laboratories, early-career scholars, infrastructure, and emerging topics shape future publications, citations, grants, and scientific capacity. Unfortunately, the connection between the two is delayed, noisy, and interdependent. Economic portfolio optimization offers an established framework for such decisions, as it evaluates investments not only by expected return but also by risk, diversification, and dependence among assets. Nevertheless, research opportunities are not financial assets: their returns are multidimensional, scientometric, cumulative, and shaped by spillovers, strategic priorities, and institutional inertia. In this study, we formulate departmental research planning as a Departmental Scientometric Portfolio Optimization problem, and propose Departmental Epistemic Portfolio Optimization, an integrative decision-support heuristic that allocates resources using expected scientometric return, uncertainty, covariance, spillovers, diversity, concentration, strategic alignment, and adjustment costs. Using in silico experiments covering stable, emerging-field, monoculture, and budget-shock environments, we show that DEPO is competitive with classical portfolio and heuristic baselines while preserving high portfolio diversity, low topic concentration, and relatively smooth allocation dynamics in several settings. In the emerging-field environment, DEPO allocated sustained resources to uncertain and under-observed opportunities, demonstrating the operation of its exploration mechanism; however, this behavior did not produce a clear cumulative-return or oracle-regret advantage over the strongest feasible baselines for all tested conditions. Overall, the results suggest that departments may benefit from portfolio-based decision support when they seek to make explicit trade-offs among performance, exploration, diversity, concentration control, and institutional stability. Full article

This study investigates the possibilities for recently listed Romanian Information and Communications Technology (ICT) firms to select optimal sets of financial strategies under adverse macroeconomic conditions, with high and persistent inflation, high volatility, high cost of financing and liquidity constraints that influence investment decisions and financial resilience. Using a stochastic investment model with the Tobin’s Q factor in a dynamic framework equipped with a generalized Wiener process, this study offers an intuitive approach for simultaneously assessing corporate market value under financial constraints and formulating optimal decisions based on liquidity management. In this research, the numerical simulations in Python for a set of 16 scenarios resulting from combining technological and macroeconomic variables were performed, with a series of parameters held constant. The results highlight the decisive role of financial restrictions and macroeconomic volatility in shaping the investment behavior, as well as the importance of adjusting the timing of investments together with liquidity mechanisms capable of improving financial resilience. The main contribution of this study is the simplicity with which one can assess the impact of the risk-free rate and volatility on the main parameters of the set of strategies (earnings dynamics, liquidity risk, cost of capital, liquidation value, opportunity cost associated with holding cash) and to assess the integrated perspective on financial resilience. This procedure is simple and scalable and can also represent a practical tool to support management and investment decisions in volatile and turbulent conditions. Full article

Students with disabilities, particularly those needing additional support or intervention to manage emotions and behaviors, build healthy relationships, and navigate social and academic demands, face heightened risks of high school pushout that can be traced back to their transition into high school. Project Elevate (PE) is a multi-component intervention that strategically invests in early coordinated student, family, and school supports to prevent barriers associated with high school pushout, such as a lack of continuity of effective services across school sites. This mixed-methods pilot study examined the implementation of PE with three 8th-grade students and their parents during their last term in middle school. This study includes quantitative pre–post descriptive analyses of multi-informant reports of students’ social, emotional, and behavioral skills, as well as descriptive analyses of weekly teacher- and parent-reported behavior and student attendance. Qualitative analysis using the Framework Method was applied to student and parent interviews and open-ended responses on a satisfaction questionnaire to understand their experience receiving PE support. Session case notes were also used as contextual data to describe implementation processes and contextualize findings. Results indicated improvements in student attendance and reductions in home-based behavioral concerns, with mixed findings across school-based outcomes. Students and parents reported high satisfaction with the intervention, highlighting the value of individualized support, goal setting, and strengthened communication with schools. Findings from this intervention development pilot study provide preliminary evidence regarding the implementation and perceived value of PE. Results also highlight the importance of culturally responsive, relationship-centered practices that affirm student strengths and support access to educational opportunities. Further investigation of PE in larger studies is warranted. Full article

This case study aimed to benchmark the healthiness, equity, and environmental sustainability of a large, urban Australian university food environment through two audits conducted in 2022 and 2025. Two cross-sectional audits were completed at a large urban university campus using the Uni-Food tool, which assesses 68 best practice indicators across three components: policy, campus facilities, and food retail outlets. Four assessors independently conducted the audits with excellent inter-rater reliability (Cohen’s Kappa = 0.89). Final scores out of 100 were calculated using weighted domains. Descriptive and inferential statistics were used to compare changes over time. In 2025, the university achieved a score of 52%, up from 48% in 2022, indicating medium compliance with best practice standards. Findings highlight that scores differed modestly but there were persistent gaps in university food policy and practice. Specifically, the policy component remained low (48%), demonstrating strong overall planning but a lack in food retail policy and monitoring systems. The campus component scored moderately (63%), with various nutrition knowledge-building opportunities and environmental sustainability initiatives available but heavy promotion of unhealthy foods at campus events. The food retail component scored lowest overall (36%), especially as there was a lack of adequate nutrition information provided at food outlets. Continued investment in policy development, campus-wide strategies, and food retail innovation is essential to create healthier, more equitable, and environmentally sustainable food environments in tertiary settings. Full article

The efficacy of healthcare performance evaluation systems depends on their design and implementation, as well as on their perceived value and integration into daily practice. This study explores the acceptability of a healthcare performance evaluation system, used by health and administrative professionals in four rural healthcare settings in Ethiopia, Tanzania, and Uganda, three years after its implementation. In-depth semi-structured interviews were conducted, either in person or via video conference, with 17 professionals involved in system design and implementation. The analysis of qualitative data drew on Sekhon’s Theoretical Framework of Acceptability, using content analysis to identify themes across seven dimensions of acceptability. Key findings show that participants’ perceptions of acceptability of the performance evaluation system are influenced by data disclosure and reputational effect, the system’s understandability, alignment with their mission to improve quality of care, perceived usefulness, experienced opportunity costs, and intervention burden. The key features of the performance evaluation system are the most critical factors contributing to its acceptability, but the administrative burden, which includes professionals’ need to invest more time and change work habits to use the new system, poses some challenges and may hinder the medium- to long-term effectiveness of the intervention. Full article

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of emergency department (ED) presentation, hospitalisation, and preventable healthcare utilisation worldwide. Although guidelines advocate coordinated, preventative, and community-based management, care within ED settings often remains reactive and crisis-driven. Nurses occupy a central role in COPD management; however, the experiential dimensions of nursing practice and its contribution to improving patient outcomes are insufficiently understood. Objectives: To explore the lived experiences of patients, nurses and medical officers regarding COPD presentations to the ED, with particular focus on the nursing role in assessment, coordination, education, and identification of unmet and comorbid care needs. Methods: A qualitative phenomenological approach was undertaken across three regional Australian EDs. Purposive sampling recruited patients presenting with acute exacerbations of COPD and nursing and medical officers involved in their care. Semi-structured interviews were conducted and transcribed verbatim. Data were analysed using Braun and Clarke’s thematic analysis framework, supported by reflexive discussion and audit trails to enhance rigour. Results: Six interrelated themes were identified: (1) nursing within a “crisis first” model of care; (2) holistic assessment and translation of complexity; (3) education and care coordination as preventative nursing work; (4) relational care and therapeutic connection; (5) nurses as sentinels for undiagnosed comorbidities, particularly obstructive sleep apnoea; and (6) system pressures constraining optimal nursing practice. Participants consistently described nurses as the clinicians who stabilised acute episodes, interpreted contextual risks, coordinated services, and provided relational and educational support, yet whose preventative contributions were limited by time and organisational demands. Conclusions: ED nurses function as critical integrators between acute stabilisation and chronic disease management for patients with COPD. Formalising nurse-led assessment, education, coordination, and sleep-disordered breathing screening may reduce avoidable ED presentations and enhance patient-centred outcomes. Investment in structured nursing models represents a key opportunity for improving COPD care delivery. Full article

Humans face environmental deterioration. Scholars have identified carbon dioxide as one of the culprits, and they emphasize carbon offset. Researchers are investigating carbon offset investments. Some researchers have encouragingly deployed multivariate variational mode decomposition methods, but they have not fully optimized them. Some researchers have opportunely assessed capital asset pricing models, but they have not fully justified them. We devise multiple-objective portfolio selection models, fully optimize them, and dominate carbon offset indexes. We extend the classical methodology of advancing from portfolio selection to capital asset pricing models into the methodology of advancing from multiple-objective portfolio selection to multiple-objective capital asset pricing models. Specifically, we explore multiple-objective capital asset pricing models by numerically verifying many tangent lines (instead of the traditionally singular tangent line) and suggesting a tangent plane (instead of tangent lines). For multiple-objective zero-covariance capital asset pricing models, we numerically compute a set of zero-covariance portfolios (instead of the traditionally singular zero-covariance portfolio) and suggest picking an advantageous zero-covariance portfolio. We consider the second-level indicators of carbon offset and generalize three-objective portfolio selection to k-objective portfolio selection. As for contributions, first, this paper’s methodology is to logically advance from multiple-objective portfolio selection to multiple-objective capital asset pricing models, whereas the literature typically covers multiple-objective portfolio selection alone and barely covers multiple-objective capital asset pricing models. Second, this paper numerically demonstrates some difficulties and proposes hypothetical solutions in the process of obtaining multiple-objective capital asset pricing models. Full article

Pharmaceutical regulation plays a central role in protecting public health by governing clinical trials, market authorization, and post-marketing safety monitoring throughout the medicine life cycle. While substantial literature describes established systems, particularly the United States Food and Drug Administration (FDA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and the European medicines regulatory network coordinated by the European Medicines Agency (EMA) together with national competent authorities, comparative analyses that integrate both mature authorities, emerging regulators and transnational harmonization networks remain limited. This narrative review draws on primary regulator/network documentation and targeted peer-reviewed literature to compare core regulatory functions across jurisdictions, including approval pathways and evidentiary expectations, inspection and good manufacturing practice (GMP) oversight, transparency practices, and pharmacovigilance and risk-management approaches. Across regions, we observe increasing convergence in scientific expectations through initiatives such as the International Council for Harmonisation (ICH) and reliance and work-sharing models, alongside persistent differences in legal mandates, resourcing, timelines, and data requirements. These differences are most consequential for complex products (e.g., advanced therapies) and in crisis settings, where emergency or conditional authorizations amplify the need for strong lifecycle monitoring, real-world evidence governance, and cross-border communication. We conclude by outlining opportunities to strengthen regulatory resilience and equity through fit-for-purpose harmonization, investment in enabling infrastructure, and future work on interoperable data systems, signal detection, and coordinated post-marketing evaluation. Full article

This study focuses on the challenges and opportunities of integrating industrial robots into robotic bricklaying systems (RBSs) for automation and digital transformation in the construction industry. A mobile RBS was designed, engineered, manufactured and commercially implemented for the first time in Poland. The RBS is designed to perform robotic bricklaying in situ in municipal, residential, and industrial buildings, where sustainable construction tasks are implemented. The details of the design solutions for the RBS, virtual simulation, and real robotic bricklaying processes are presented. The results of bricklaying using the RBS and the factors that influence the robotic bricklaying process are summarized. A 3D digital building information model (BIM) created using Autodesk Revit tools was used for simulated robotic bricklaying in the ABB RobotStudio 2025.5 program, from which they were transferred to the programming of the ABB IRB 4600 bricklaying robot. The laser programming method for the bricklaying robot, bricklaying procedures, and algorithms are also presented. The costs of human labor and robot construction were compared, and the return on investment (ROI) was calculated. RBS evaluations were performed in laboratory settings, on-site demonstrations, and commercial wall-laying in residential apartments. Full article

The decarbonization of the building stock by 2050, as set by the European Green Deal, calls for an unprecedented wave of energy renovations. Yet, reliable evidence on the real costs and performance of retrofit interventions remains scarce. This paper presents the results of a large-scale technical and economic analysis conducted on 34 residential buildings, all renovated under a national Italian programme supporting energy efficiency improvements. For each building, pre- and post-renovation energy performances were assessed using standardised procedures, while detailed investment cost data were collected for all implemented measures, including envelope insulation, HVAC system upgrades, and renewable integrations. By combining these datasets, the study evaluates the actual cost-effectiveness of different retrofit strategies, revealing the true financial effort required to achieve substantial energy improvements. The results highlight both the opportunities and limitations of current approaches, showing a significant gap between theoretical models and real outcomes. The findings contribute to the European debate on the economic sustainability of deep renovation policies. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

Although the concept of Active and Assisted Living (AAL) has been a prominent topic in academia and in industry for decades, the widespread adoption of related technologies remains well below expectations. The underlying causes are multifaceted. The installation and retrofitting of such systems typically require substantial financial investments, significant manual effort, and specialized expertise for setup and maintenance. Existing solutions lack flexibility and are difficult to tailor to the individual living situations and diverse needs of the primary target group, older adults. While state-of-the-art smart home platforms would, in principle, be capable of supporting a broad range of AAL functionalities and could be adapted to different usage contexts, much of the research in this domain has been conducted in artificial settings, such as laboratory environments or model houses, conditions that fail to fully capture the complexity and variability of real-world living environments of the elderly population. In this paper, we explore the potential, opportunities, and limitations of integrating low-cost hardware with open-source software components in residential environments representative of older adults’ everyday lives. Our work is based on a longitudinal case study conducted over several years in an actual household, focusing on delivering fundamental AAL functionality. By documenting the iterative development and real-world deployment of the system, this study offers practical insights into the feasibility and challenges of implementing on-site AAL support under realistic conditions. Full article

Agri-food supply chains are highly exposed to freshness deterioration, demand uncertainty, and information asymmetry. In practice, upstream suppliers may strategically misreport freshness-related information to influence downstream procurement decisions, which can amplify inefficiency and increase food loss and waste. This study develops an analytical framework that integrates (i) strategic freshness misreporting by an informed supplier, (ii) endogenous investment in blockchain-enabled traceability that improves information credibility at a cost, and (iii) contract design for supply chain coordination. We consider a two-echelon agri-food supply chain with stochastic demand and freshness-dependent valuation, and characterize equilibrium operational decisions under centralized and decentralized settings. The results reveal how misreporting reshapes optimal order quantities, wholesale prices, and profit allocation, and identify conditions under which misreporting increases expected waste and undermines sustainability performance. We then examine how traceability investment changes the incentives of both parties, leading to adoption thresholds and potential incentive misalignment under decentralization. Finally, we design revenue-sharing, cost-sharing, and combined contracts and derive parameter regions that coordinate the blockchain-enabled agri-food supply chain and generate Pareto improvements for both the supplier and the retailer. Numerical experiments illustrate the comparative statics and quantify the trade-offs among profitability, transparency, and waste reduction. Relative to existing blockchain-enabled agri-food supply chain models, the framework jointly endogenizes supplier misreporting of freshness, blockchain-based traceability investment, and contract parameters, thereby uncovering new adoption thresholds and coordination regions that tightly link transparency decisions to food loss and waste. The findings provide actionable guidance for using digital traceability and contract mechanisms to curb opportunism, enhance coordination, and support sustainable agri-food supply chains. Full article