Search Results (295)

Artificial Intelligence (AI) and Generative AI are transformative yet double-edged technologies with evolving risks. While research emphasises trustworthy, fair, and responsible AI by focusing on its “what” and “why,” it overlooks practical “how.” To bridge this gap in financial services, an industry at the forefront of AI adoption, this study employs a qualitative approach grounded in existing Responsible AI and Corporate Digital Responsibility (CDR) frameworks. Through thematic analysis of 15 semi-structured interviews conducted with professionals working in finance, we illuminate nine non-technical barriers that practitioners face, such as sustainability challenges, trade-off balancing, stakeholder management, and human interaction, noting that GenAI concerns now eclipse general AI issues. CDR practitioners adopt a more human-centric stance, emphasising consensus-building and “no margin for error.” Our findings offer actionable guidance for more responsible AI strategies and enrich academic debates on Responsible AI and AI-CDR symbiosis. Full article

Customer churn prediction has become crucial for businesses, yet it poses significant challenges regarding privacy preservation and prediction accuracy. In this paper, we address two fundamental questions: (1) How can customer churn be effectively predicted while ensuring robust privacy protection of sensitive data? (2) How can large language models enhance churn prediction accuracy while maintaining data privacy? To address these questions, we propose FairRAG, a robust architecture that combines differential privacy, retrieval-augmented generation, and LLMs. Our approach leverages OPT-125M as the core language model along with a sentence transformer for semantic similarity matching while incorporating differential privacy mechanisms to generate synthetic training data. We evaluate FairRAG on two diverse datasets: Bank Churn and Telco Churn. The results demonstrate significant improvements over both traditional machine learning approaches and standalone LLMs, achieving accuracy improvements of up to 11% on the Bank Churn dataset and 12% on the Telco Churn dataset. These improvements were maintained when using differentially private synthetic data, thus indicating robust privacy and accuracy trade-offs. Full article

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when optimizing for different trustworthiness dimensions? By examining frameworks such as the NIST AI Risk Management Framework (AI RMF), the AI Trust Framework and Maturity Model (AI-TMM), and ISO/IEC standards, this study bridges theoretical insights with practical applications. We identify major risks across the AI lifecycle stages and outline various metrics to address challenges in system reliability, bias mitigation, and model explainability. This study includes a comparative analysis of existing standards and their application across industries to illustrate their effectiveness. Real-world case studies, including applications in healthcare, financial services, and autonomous systems, demonstrate approaches to applying trust metrics. The findings reveal that achieving trustworthiness involves navigating trade-offs between competing metrics, such as fairness versus efficiency or privacy versus transparency, and emphasizes the importance of interdisciplinary collaboration for robust AI governance. Emerging trends suggest the need for adaptive frameworks for AI trustworthiness that evolve alongside advancements in AI technologies. This paper contributes to the field by proposing a comprehensive review of existing frameworks with guidelines for building resilient, ethical, and transparent AI systems, ensuring their alignment with regulatory requirements and societal expectations. Full article

Accurate assessment of biomass fuel properties is essential for quality control and fair market pricing, particularly when dealing with variable moisture content (MC) in agricultural residues. This study investigates the use of near-infrared (NIR) spectroscopy to predict the lower heating value (LHV) and ash content of sugarcane leaf pellets while minimizing the interference caused by moisture variability. Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. The optimal model for LHV prediction was developed using GA-PLS regression (Method II), provided a coefficient of determination (R²) of 0.80, a root mean square error of calibration (RMSEc) of 595.80 J/g, and a ratio of performance to deviation (RPD) of 1.74, indicating fair predictive performance. The ash content model showed moderate accuracy, with a maximum R² of 0.61 and an RPD of 1.40. These findings suggest that the variables selected via GA in Method II were not relevant to MC; as Method II provided the best result, this indicates a low impact of MC, which may influence model construction in the future. Moreover, the findings also highlight the potential of NIR spectroscopy, combined with appropriate spectral preprocessing and wavelength optimization, as a rapid, non-destructive tool for evaluating biomass quality, enabling more precise control in bioenergy production and biomass trading. Full article

In this survey, we provide a comprehensive classification of GPU task scheduling approaches, categorized by their underlying algorithmic techniques and evaluation metrics. We examine traditional methods—including greedy algorithms, dynamic programming, and mathematical programming—alongside advanced machine learning techniques integrated into scheduling policies. We also evaluate the performance of these approaches across diverse applications. This work focuses on understanding the trade-offs among various algorithmic techniques, the architectural and job-level factors influencing scheduling decisions, and the balance between user-level and service-level objectives. The analysis shows that no one paradigm dominates; instead, the highest-performing schedulers blend the predictability of formal methods with the adaptability of learning, often moderated by queueing insights for fairness. We also discuss key challenges in optimizing GPU resource management and suggest potential solutions. Full article

Trade fairs are not only commercial platforms but also catalysts for urban development, city branding, and international engagement. This longitudinal study analyzes data from trade fair exhibitors from China, the United Arab Emirates (UAE), and Germany to examine how cultural differences influence their experiences, satisfaction, and destination loyalty within the urban landscape of Thessaloniki. By adopting Social Exchange Theory (S.E.T.) as a framework, this research applies a mixed-methods approach, combining surveys and in-depth interviews conducted over multiple years (2017–2024) at the 82nd, 86th, and 88th Thessaloniki International Fair (T.I.F.). The empirical material consists of 226 survey responses (116 from China, 44 from the UAE, and 84 from Germany) and 52 semi-structured interviews, analyzed using descriptive and non-parametric statistics, alongside thematic interpretation of qualitative data. Findings reveal distinct exhibitor expectations. These cultural distinctions shape their perceptions of Thessaloniki’s infrastructure, services, and overall urban experience, influencing their likelihood to revisit or recommend the city. This study underscores the long-term role of trade fairs in shaping urban economies and offers insights into how cities can leverage international exhibitions for sustainable urban growth. Policy recommendations highlight the need for tailored infrastructural improvements, strategic city branding initiatives, and cultural adaptations to enhance exhibitor engagement and maximize the economic impact of global events. Full article

Occupational exposures in the agricultural industry globally have been associated with heightened risk for several diseases. Reports written in South Africa in the last decade have raised awareness of the harsh occupational conditions and human rights abuses suffered by farmworker communities in the wine industry. Despite receiving “fair trade” labels upon reentry into the global market in the 1990s, the working conditions on wine farms in South Africa have remained unchanged and exploitative for centuries. Farmworkers remain dependent on substandard farm housing, have insecure land tenure rights, are exposed to toxic pesticides, are denied access to benefits and unionization, and endure long working hours in harsh environmental conditions with low pay. These occupational conditions are linked to interacting disease clusters: metabolic syndrome, problematic drinking, and communicable diseases including tuberculosis, HIV, and COVID-19. This milieu of interacting diseases with deleterious outcomes is an under-considered occupational syndemic that will likely worsen given both the lasting impacts of COVID-19 and more recent shifts in global public health funding. Full article

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness. Full article

Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical methods enabling this fusion, such as efficient low-rank adaptation (LoRA) for fine-tuning large models and memory-efficient Split Federated Learning (SFL) for collaborative edge training. However, this integration faces significant hurdles: the resource limitations of IoT devices, unreliable network communication, data heterogeneity, diverse security threats, fairness considerations, and regulatory demands. While other surveys cover pairwise combinations, this review distinctively analyzes the three-way synergy, highlighting how IoT, LLMs, and FL working in concert unlock capabilities unattainable otherwise. Our analysis compares various strategies proposed to tackle these issues (e.g., federated vs. centralized, SFL vs. standard FL, DP vs. cryptographic privacy), outlining their practical trade-offs. We showcase real-world progress and potential applications in domains like Industrial IoT and smart cities, considering both opportunities and limitations. Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. Addressing these areas is essential for responsibly harnessing this powerful technological blend. Full article

The CORUS-XUAM project defined three two-way U-space corridors linking Frankfurt Airport’s Terminal 2 on the city outskirts with the city-center Trade Fair. These corridors avoid the approach cones of the northern and central runways and bypass hospital no-fly zones and large buildings. In our previous studies, we first used fast-time simulations to evaluate the U-space routing and its operating concept, based on historical air traffic data. Included were arriving and departing airplanes as well as police, and medical helicopters throughout the city. The focus was on the limitations of the airspace, avoiding conflicts with other airspace users and between the airtaxis using a different corridor or delaying the departure, as well as determining the throughput potential of such a corridor system. Building on our previous studies, this study incorporates higher-fidelity traffic simulation data and an updated demand analysis for the airtaxi shuttle service. Our new sizing analysis reveals that ground operations typically, not airspace capacity, constitute the primary bottleneck. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This study was carried out at 10 randomly selected fringe villages of Buxa Tiger Reserve (BTR) in the Terai region of West Bengal, India through personal interviews with 100 randomly selected respondents. The study documented 102 non-timber forest products (NTFPs) that were utilized throughout the year. In the local weekly market, 28 NTFPs were found to be traded by the collectors. The study shows that without proper price mechanisms and marketing channels; the residents cannot obtain fair prices for their products. The study found only nine NTFPs that were prominently traded with the involvement of middlemen and traders along with the royalty imposed by the State Forest Department. The MSPs computed for these nine NTFPs were 25–200% higher than the prices the collectors were selling to the traders. The nationalization of NTFPs through MSPs will help their effective marketing, ensuring an adequate income for the collectors, which will lead to their sustainable harvest and conservation through participatory forest management. Introducing MSPs for NTFPs with an efficient procurement network can advance the economic status of the inhabitants. We recommend increasing the inhabitants’ capacity to collect, store, process, and market NTFPs with active policy, institutional, and infrastructural support. Full article

The cooperative interconnection of multi-microgrid systems offers significant advantages in enhancing energy utilization efficiency and economic performance, providing innovative pathways for promoting sustainable development. To establish a fair energy trading mechanism for electricity–hydrogen sharing within multi-energy multi-microgrid (MEMG) systems, this study first analyzes the operational architecture of MEMG energy sharing and establishes a multi-energy coordinated single-microgrid model integrating electricity, heat, natural gas, and hydrogen. To achieve low-carbon operation, carbon capture systems (CCSs) and power-to-gas (P2G) units are incorporated into conventional combined heat and power (CHP) systems. Subsequently, an asymmetric Nash bargaining-based optimization framework is proposed to coordinate the MEMG network, which decomposes the problem into two subproblems: (1) minimizing the total operational cost of MEMG networks, and (2) maximizing payment benefits through fair benefit allocation. Notably, Subproblem 2 employs the energy trading volume of individual microgrids as bargaining power to ensure equitable profit distribution. The improved alternating direction multiplier method (ADMM) is adopted for distributed problem-solving. Experimental results demonstrate that the cost of each MG decreased by 5894.14, 3672.44, and 2806.64 CNY, while the total cost of the MEMG network decreased by 12,431.22 CNY. Additionally, the carbon emission reduction ratios were 2.84%, 2.77%, and 5.51% for each MG and 11.12% for the MEMG network. Full article

The adoption of sustainable food products by consumers is often hindered by both perceived and actual barriers within retail environments. This study investigates the interaction between the objective availability of sustainable food, its in-store visibility, and consumer perceptions of and discourses about these aspects, specifically examining how these factors contribute to socio-spatial disparities in access within an urban context (Turin, Italy). The research combined qualitative interviews with 50 consumers—to understand their perceptions and purchasing criteria—with quantitative observations of the presence and presentation of products in 56 supermarkets and 28 open-air markets across different socio-economic areas. The findings indicate that while sustainable products are more widely available than commonly perceived, their visibility (shelf positioning, signage) is significantly lower in socio-economically disadvantaged areas. This “invisibility” creates a crucial perceptual barrier, particularly for consumers who rely on immediate environmental cues and efficient shopping strategies, thus limiting purchases despite the actual presence of the products. The study concludes that in-store presentation strategies are critical mediators of perceived availability, disproportionately affecting consumers in lower socio-economic contexts and highlighting an innovative dimension of food access inequality that calls for targeted interventions at both the retail and policy levels. Full article

The Shapley value is a fundamental concept in data science, providing a principled framework for fair resource allocation, feature importance quantification, and improved interpretability of complex models. Its fundamental theory is based on four axiomatic proper ties, which underpin its widespread application. To address the inherent computational challenges of exact calculation, we discuss model-agnostic approximation techniques, such as Random Order Value, Least Squares Value, and Multilinear Extension Sampling, as well as specialized fast algorithms for linear, tree-based, and deep learning models. Recent extensions, such as Distributional Shapley and Weighted Shapley, have broadened the applications to data valuation, reinforcement learning, feature interaction analysis, and multi-party cooperation. Practical effectiveness has been demonstrated in health care, finance, industry, and the digital economy, with promising future directions for incorporating these techniques into emerging fields, such as data asset pricing and trading. Full article