Search Results (83)

Over the last decade, there has been a significant increase in banks’ investment in technology, alongside a substantial rise in CEO compensation. Research on executive compensation has primarily focused on traditional performance metrics, such as return on assets and return on equity, as well as governance factors. Investigating the nexus between fintech adoption and CEO compensation introduces a new perspective on the determinants of CEO pay and how technological transformation influences executive remuneration structures. This study investigated the relationship between Chief Executive remuneration and fintech adoption among banks listed on the Johannesburg Stock Exchange. There is a lack of literature on the impact of technology adoption on CEO compensation in developing and emerging economies. The quantitative longitudinal study, conducted over 15 years from 2010 to 2024, collected secondary data from the annual reports of six banks and the IRESS database. A panel data fixed effects regression analysis was employed to analyze the data. CEO compensation included both salary and total compensation. Fintech variables used for the study included automated teller machines, mobile banking, and internet banking. The findings revealed a positive relationship between CEO salary and the rollout of ATMs and mobile banking, while an inverse relationship was noted between salary and internet banking. Similarly, total compensation showed an inverse relationship with the adoption of ATMs and internet banking, whereas mobile banking had a positive effect on total compensation. Understanding how technology impacts CEO compensation can help remuneration committees ensure that CEO pay is linked to the value that infrastructure investments bring to an organization, rather than simply the number of innovations introduced. This understanding will also help solve the principal-agent problem, as it will ensure technology innovations that enhance firm performance are rewarded. In the context of emerging markets, the study’s findings suggest that organizations should recognize and formalize pay linked to digital transformation, rather than focusing solely on short-term financial metrics. This also suggests the need to develop guidelines for executive remuneration disclosure related to the technology sector. The close connection between fintech adoption and technological and regulatory risks highlights the need to balance incentive structures that reward innovation with risk-adjusted performance measures. Full article

The growing problem of antimicrobial resistance emphasizes the urgent need for new and effective natural antimicrobial agents. This study assessed the antibacterial activity of twenty essential oils and one absolute against Escherichia coli and examined the relationship between their chemical composition and biological activity. The chemical profiles of the samples were determined using gas chromatography–mass spectrometry (GC–MS), and the resulting data were analysed using principal component analysis (PCA), discriminant analysis (DA), and partial least squares (PLS) methods to explore associations between composition and antibacterial activity. The results showed substantial variability among the tested essential oils, with those from Thymus vulgaris, Aniba rosaeodora, Syzygium aromaticum, Pimenta dioica, and the absolute of Evernia prunastri exhibiting the strongest activity. GC–MS analysis identified thymol, eugenol, and methyl atrarate as key bioactive constituents associated with strong antibacterial effects, while linalool, limonene, and α-terpineol were linked to moderate activity. Multivariate analyses provided further insight but were limited by data variability, highlighting compositional diversity rather than clear group separation. Overall, the findings demonstrate that essential oils are a promising source of natural antimicrobial agents and emphasise the importance of linking chemical composition with biological function to understand their potential therapeutic applications. Full article

We explore a rigorous formulation of agent-based SIR epidemic dynamics as a discrete-state Markov process, capturing the stochastic propagation of infection or an invading agent on networks. Using indicator functions and corresponding marginal probabilities, we derive a hierarchy of evolution equations that resembles the classical BBGKY hierarchy in statistical mechanics. The structure of these equations clarifies the challenges of closure and highlights the principal problem of systemic complexity arising from stochastic but generally not fully chaotic interactions. Monte Carlo simulations are used to validate simplified closures and approximations, offering a unified perspective on the interplay between network topology, stochasticity, and infection dynamics. We also explore the impact of lockdown measures within a networked agent framework, illustrating how SIR dynamics and structural complexity of the network shape epidemic with propagation of the COVID-19 pandemic in Northern Italy taken as an example. Full article

We introduce Causal Incentive Design (CID), a framework that applies causal inference to canonical single-stage principal–agent problems (PAPs) characterized by bilateral private information. Within CID, the operating rules of PAPs are formalized using an additive-noise causal graphical model (CGM). Incentives are modeled as interventions on a function space variable, $\Gamma$, which correspond to policy interventions in the principal–follower causal relation. The causal inference target estimand $V(\Gamma )$ is defined as the expected value of the principal’s utility variable under a specified policy intervention in the post-intervention distribution. In the context of additive-Gaussian independent noise, the estimand $V(\Gamma )$ decomposes into a two-layer expectation: (i) an inner Gaussian smoothing of the principal’s utility regression; and (ii) an outer averaging over the conditional probability of the follower’s action given the incentive policy. A Gauss–Hermite quadrature method is employed to efficiently estimate the first layer, while a policy-local kernel reweighting approach is used for the second. For offline selection of a single incentive policy, a Functional Causal Bayesian Optimization (FCBO) algorithm is introduced. This algorithm models the objective functional $\gamma \mapsto V\left(\gamma \right)$ using a functional Gaussian process surrogate defined on a Reproducing Kernel Hilbert Space (RKHS) domain and utilizes an Upper Confidence Bound (UCB) acquisition functional. Consequently, the policy value $V\left(\gamma \right)$ becomes an interventional query that can be answered using offline observational data under standard identifiability assumptions. High-probability cumulative-regret bounds are established in terms of differential information gain for the proposed FBO algorithm. Collectively, these elements constitute the central contributions of the CID framework, which integrates causal inference through identification and estimation with policy search in principal–agent problems under private information. This approach establishes a causal decision-making pipeline that enables commitment to a high-performing incentive in a single-shot game, supported by regret guarantees. Provided that the data used for estimation is sufficient, the resulting offline pipeline is appropriate for scenarios where adaptive deployment is impractical or costly. Beyond the methodological contribution, this work introduces a novel application of causal graphical models and causal reasoning to incentive design and principal–agent problems, which are central to economics and multi-agent systems. Full article

This entry delineates artificial intelligence (AI) ethics and the field’s core ethical challenges, surveys the principal normative frameworks in the literature, and offers a historical analysis that traces and explains the shift from ethical monism to ethical pluralism. In particular, it (i) situates the field within the trajectory of AI’s technical development, (ii) organizes the field’s rationale around challenges regarding alignment, opacity, human oversight, bias and noise, accountability, and questions of agency and patiency, and (iii) compares leading theoretical approaches to address these challenges. We show that AI’s development has brought escalating ethical challenges along with a maturation of frameworks proposed to address them. We map an arc from early monisms (e.g., deontology, consequentialism) to a variety of pluralist ethical frameworks (e.g., pluralistic deontology, augmented utilitarianism, moral foundation theory, and the agent-deed-consequence model) alongside pluralist governance regimes (e.g., principles from the Institute of Electrical and Electronics Engineers (IEEE), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the Asilomar AI principles). We find that pluralism is both normatively and operationally compelling: it mirrors the multidimensional problem space of AI ethics, guards against failures (e.g., reward hacking, emergency exceptions), supports legitimacy across diverse sociotechnical contexts, and coheres with extant principles of AI engineering and governance. Although pluralist models vary in structure and exhibit distinct limitations, when applied with due methodological care, each can furnish a valuable foundation for AI ethics. Full article

Diabetes mellitus (DM) continues to be a global world health problem. Despite medical advances, both DM and chronic kidney disease (CKD) remain global health issues with high mortality and limited options to prevent end-stage renal failure. Current therapies encompass five classes of drugs: (1) angiotensin-converting-enzyme inhibitors (ACEI) or angiotensin II receptor blockers (AIIRB); (2) sodium-glucose-transporter 2 (SGLT2) inhibitors; (3) glucagon-like peptide-1 receptor agonists (GLP-1 RA); and (4) an antagonist of type 1 endothelin receptor (ET1R) with proven efficacy to reduce albuminuria and proteinuria. (5) The mineralocorticoid receptor antagonist (MRA) finerenone has been tested in RCTs as a kidney protective agent. In our review, we summarize many of the principal trials that have generated evidence in this regard. Many novel agents—many of them proven not only for DM management but also for the treatment of obesity with or without DM or heart failure (HF)—are now in development and may be added to the five classical pillars: other non-steroidal MRA (balcinrenone); aldosterone synthase inhibitors (baxdrostat and vicadrostat); other GLP-1 RA (tirzepatide, survodutide, retatrutide, and cagrilintide); ET1 R antagonists, (zibotentan); and soluble guanylate cyclase activators (avenciguat). These new agents aim to slow disease progression further and reduce cardiovascular risk. Future strategies rely on integrated, patient-centered approaches and personalized therapy to curb renal disease and its related complications. Full article

Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery. Full article

Lipopolysaccharide (LPS), an essential structural molecule in the outer membrane of Gram-negative bacteria, is recognized as a principal trigger of inflammatory responses and oxidative stress. Thus, the control and clearance of LPS is essential to inhibit LPS-induced excessive inflammation, oxidative stress, and liver injury. In recent years, some native bioactive peptides, such as human β-defensin 126 (DEFB126) and thymopentin (TP5), have been reported to have inhibitory effects against LPS-induced inflammation and oxidative stress. However, the cytotoxicity, weak stability, and poor biological activity have hindered their practical application and clinical development. The development of novel hybrid peptides is a promising approach for overcoming these problems. In this study, we designed a novel hybrid peptide [DTP, DEFB126 (1-39)-TP5] that combines the active center of DEFB126 and full-length thymopentin (TP5). Compared to the parental peptides, DTP has a longer half-life, lower cytotoxicity, and greater anti-inflammatory and antioxidant activity. The anti-inflammatory and antioxidant effects of DTP were demonstrated in a murine LPS-induced sepsis model, which showed that DTP successfully inhibited the indicators associated with LPS-induced liver injury; decreased the contents of TNF-α, IL-6, and IL-1β; increased the level of glutathione (GSH); and improved the activities of catalase (CAT) and superoxide dismutase (SOD). Furthermore, our study revealed that the anti-inflammatory and antioxidant activities of DTP were associated with LPS neutralization, blockade of LPS binding to the Toll-like receptor 4/myeloid differentiation factor 2 (TLR4/MD-2) complex, reduction in reactive oxygen species content, and inhibition of the activation of the nuclear factor kappa-B (NF-кB) signaling pathway. These results elucidate the structural and functional properties of the peptide DTP, reveal its underlying molecular mechanisms, and shed light on its potential as a multifunctional agent for applications in agriculture, food technology, and clinical therapeutics. Full article

This paper proposes a Distributed Adaptive Regularization Algorithm (DARN) for solving composite non-convex and non-smooth optimization problems in multi-agent systems. The algorithm employs a three-phase iterative framework to achieve efficient collaborative optimization: (1) a local regularized optimization step, which utilizes proximal mappings to enforce strong convexity of weakly convex objectives and ensure subproblem well-posedness; (2) a consensus update based on doubly stochastic matrices, guaranteeing asymptotic convergence of agent states to a global consensus point; and (3) an innovative adaptive regularization mechanism that dynamically adjusts regularization strength using local function value variations to balance stability and convergence speed. Theoretical analysis demonstrates that the algorithm maintains strict monotonic descent under non-convex and non-smooth conditions by constructing a mixed time-scale Lyapunov function, achieving a sublinear convergence rate. Notably, we prove that the projection-based update rule for regularization parameters preserves lower-bound constraints, while spectral decay properties of consensus errors and perturbations from local updates are globally governed by the Lyapunov function. Numerical experiments validate the algorithm’s superiority in sparse principal component analysis and robust matrix completion tasks, showing a 6.6% improvement in convergence speed and a 51.7% reduction in consensus error compared to fixed-regularization methods. This work provides theoretical guarantees and an efficient framework for distributed non-convex optimization in heterogeneous networks. Full article

Corruption in public procurement remains a challenge to good governance, especially in developing nations. Blockchain technology has been espoused as a new paradigm for achieving sustainable public procurement practices for effective service delivery and, by extension, promoting sustainable development. Given the potential of blockchain technology, its implementation has been slow in developing countries. Additionally, there is an inadequate decision support framework to prioritize corruption-prone stages of the public procurement cycle for strategic blockchain integration at the most critical corruption-prone stages of the public procurement cycle given the scarce resources available in developing countries. Therefore, we employed a matured theory that is the principal-agent theory to identify key agency problems related to public procurement in developing countries. An interview with 25 experts and a thorough review of Ghana’s Auditor General produced seven public procurement cycle stages. Further, a survey was designed for experts and stakeholders to prioritize the identified procurement stages under the agency problems through the Analytic Hierarchy Process (AHP). Our results revealed that tender evaluation was the most critical stage susceptible to corruption, followed by contract management and procurement planning in the public procurement stages. Additionally, for the relative importance of the criteria, information asymmetry was ranked first, followed by moral hazard, and then adverse selection. This study offers a targeted framework for blockchain deployment in public procurement from an African country perspective. The outcome of this study provides insights for policymakers and procurement practitioners to know the most critical stages of public procurement stages and leverage blockchain technology given the scarcity of resources in developing countries to aid sustainable public procurement. The proposed blockchain framework can enhance service delivery, citizens’ trust, and international donor confidence in partnership and funding for public procurement projects in developing countries. Full article

Background: The close relationship between humans and petsraises health concerns due to the potential transmission of antimicrobial-resistant (AMR) bacteria and genes. Bacterial otitis is an emerging health problem in dogs, given its widespread prevalence and impact on animal welfare. Early detection of resistance is vital in veterinary medicine to anticipate future treatment challenges. Objective: This study aimed to determine the prevalence of AMR bacteria involved in 12,498 cases of otitis in dogs from the Iberian Peninsula and the evolution of AMR patterns over an 11-year period. Methods: Data was provided by the Veterinary Medicine Department of a large private diagnostic laboratory in Barcelona. Antimicrobial susceptibility testing was performed using the standard disk diffusion method and minimum inhibitory concentration (MIC) testing. Results: The frequency of the principal bacterial agents was 35% Staphylococcus spp. (principally S. pseudointermedius), 20% Pseudomonas spp. (P. aeruginosa), 13% Streptococcus spp. (S. canis), and 11% Enterobacterales (Escherichia coli and Proteus mirabilis). Antimicrobial susceptibility testing revealed P. aeruginosa (among Gram-negatives) and Enterococcus faecalis (among Gram-positives) as the species with the highest AMR to multiple antimicrobial classes throughout the years. According to the frequency and time evolution of multidrug resistance (MDR), Gram-negative bacteria like P. mirabilis (33%) and E. coli (25%) presented higher MDR rates compared to Gram-positive strains like Corynebacterium (7%) and Enterococcus (5%). The AMR evolution also showed an increase in resistance patterns in Proteus spp. to doxycycline and Streptococcus spp. to amikacin. Conclusions: This information can be useful for clinicians, particularly in this region, to make rational antimicrobial use decisions, especially when empirical treatment is common in companion animal veterinary medicine. In summary, improving treatment guidelines is a key strategy for safeguarding both animal and human health, reinforcing the One Health approach. Full article

More and more attention has been paid to the environmental problems brought about by the development of the global economy. Based on the principal–agent theory, this paper constructs an incentive model for the government and polluting enterprises and explores the incentive problem of the government and polluting enterprises in undertaking environmental responsibility. At present, the research on the incentive of polluting enterprises focuses on the hypothesis of ‘rational man’, and less on the fairness preference of polluting enterprises. However, in other research fields, it has been proved that fairness preference has a great influence on the incentive mechanism. Fairness preference is introduced into the incentive model, and the incentive effect of polluting enterprises before and after considering fairness preference is compared and analyzed. This study found that the reward and punishment mechanism considering fairness preference can increase the behavior of polluting enterprises to assume environmental responsibility and limit the behavior of not assuming environmental responsibility. The stronger the fairness preference of polluting enterprises, the stronger the role of incentive mechanism; after considering the fairness preference, the government’s subsidies and penalties for polluting enterprises will increase with the increase in the fairness preference of polluting enterprises, and the expected benefits of polluting enterprises and the government will also increase; under the same incentive mechanism, the income of polluting enterprises with strong fairness preference is higher, but the government’s income is lower. Adopting the same incentive mechanism for different polluting enterprises will cause the loss of social benefits. After considering the fairness preference, the incentive strategy set up to a certain extent promotes the polluting enterprises to assume environmental responsibility and realize the coordinated development of the economy and the environment. Therefore, the government should set reasonable subsidy and punishment policies according to the fairness preference of polluting enterprises to encourage enterprises to fulfill their environmental responsibilities, improve environmental quality and reduce pollution. Full article

Weeds cause a decrease in the quantity and quality of agricultural production and economic damage to producers. The prolonged use of synthetic pesticides causes problems of environmental pollution, the possible alteration of agricultural products and problems for human health. For this reason, the scientific community’s search for products of natural origin, which are biodegradable, safe for human health and can act as valid alternatives to traditional herbicides, is growing. Essential oils can have useful implications in agriculture by acting as effective alternatives to chemical herbicides. In this work, the chemical composition of an EO from Artemisia herba-alba and its herbicidal properties were studied on two weeds (Lolium multiflorum and Trifolium pratense) and two crops (Brassica napus and Hordeum vulgare) and its environmental safety was also assessed using three model organisms: Chaoborus sp., Tubifex tubifex and Eisenia foetida. The principal component of the EO was camphor (26.02%), with α- and β-thujone (9.60 and 8.38%, respectively), 1,8-cineole (8.02%), piperitenone (5.29%) and camphene (4.95%) as the main components. The EO demonstrated variable phytotoxic effects with a dose-dependent manner, inhibiting both the germination and the radical elongation of the tested seeds, and was also found to be environmentally safe for the selected organisms. The results lay the foundation for considering this EO as a potential weed control agent. Full article

In the process of forest ecological benefit compensation, there are problems of information asymmetry and “misaligned incentives”, which will reduce the compensation efficiency. In order to improve the compensation efficiency, based on principal–agent theory, this study constructs incentive contract models for governmental forest ecological benefit compensation under three different scenarios, namely, information symmetry, single-sided information asymmetry, and double-sided information asymmetry. The study finds that the government can design different incentive contracts to motivate forest farmers with high and low forestry capabilities. And the government’s expected utility is influenced by the proportion of forest farmers with high and low forestry capabilities in reality. Due to the information gap between the government and forest farmers, it is inevitable that high-capability forest farmers will obtain an information rent. Under double-sided information asymmetry, the incentive coefficient for lower-capability forest farmers and their optimal actual public welfare forest conservation area decrease as the proportion of high-capability forest farmers increases. Furthermore, when the proportion of high-capability forest farmers exceeds a certain threshold, signing compensation contracts with low-capability forest farmers can harm the government’s interests. The research conclusions provide a scientific basis for the government to formulate differentiated incentive contracts for forest ecological benefits. This can effectively align forest farmers’ conservation behaviors with the improvement of public forest ecological benefits. As a result, it contributes to improving the efficiency of forest ecological benefit compensation. Full article

A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation. Full article