Search Results (128)

This case study evaluates the efficiency of STEM-based sustainability initiatives at the University of Brasilia (UnB) using a Bootstrap Data Envelopment Analysis (DEA) approach. Twenty projects were analyzed based on input variables—team size, budget, and workload—and output variables—number of beneficiaries and published papers. The results indicate higher efficiency in the Mathematics and Civil Engineering departments, while Energy Engineering showed the lowest performance. A strong correlation (r = 0.78) was observed between budget and publication volume, but no significant relationship was found between the inputs and number of beneficiaries. SDG 4 (Quality Education) was the most frequently addressed, whereas SDG 16 (Peace, Justice, and Strong Institutions) and SDG 14 (Life Below Water) received less attention. The study identifies key areas for improvement, emphasizing the need for more balanced resource allocation and contextual awareness over sustainability priorities. It also offers an adaptive and replicable framework to other faculties or institutions seeking to optimize sustainability efforts through the lens of resource allocation optimization. Full article

Quadratic programming (QP) formulations are widely used in optimal investment portfolio selection, a central problem in financial decision-making. In practice, asset allocation decisions operate at two interconnected levels: the strategic level, which allocates the budget across major asset classes, and the tactical level, which distributes the allocation within each class to individual securities or instruments. This study evaluates the Frank–Wolfe (FW) algorithm as a computationally alternative to a QP formulation implemented in CVXPY and solved using OSQP (CVXPY–OSQP solver) for tactical investment portfolio optimization. By iteratively solving a linear approximation of the convex objective function, FW offers a distinct approach to portfolio construction. A comparative analysis was conducted using a tactical portfolio model with a small number of stock assets, assessing solution similarity, computational running time, and memory usage. The results demonstrate a clear trade-off between the two methods. While FW can produce portfolio weights closely matching those of the CVXPY–OSQP solver at lower and feasible target returns, its solutions differ at higher returns near the limits of the feasible set. However, FW consistently achieved shorter execution times and lower memory consumption. This study quantifies the trade-offs between accuracy and efficiency and identifies opportunities to improve FW’s accuracy through adaptive iteration strategies under more challenging optimization conditions. Full article

In the technology-assisted review (TAR) area, most research has focused on ranking effectiveness and active learning strategies within individual topics, often assuming unconstrained review effort. However, real-world applications such as legal discovery or medical systematic reviews are frequently subject to global screening budgets. In this paper, we revisit the CLEF eHealth TAR shared tasks (2017–2019) through the lens of budget-aware evaluation. We first reproduce and verify the official participant results, organizing them into a unified dataset for comparative analysis. Then, we introduce and assess four intuitive budget allocation strategies—even, proportional, inverse proportional, and threshold-capped greedy—to explore how review effort can be efficiently distributed across topics. To evaluate systems under resource constraints, we propose two cost-aware metrics: relevant found per cost unit (RFCU) and utility gain at budget (UG@B). These complement traditional recall by explicitly modeling efficiency and trade-offs between true and false positives. Our results show that different allocation strategies optimize different metrics: even and inverse proportional allocation favor recall, while proportional and capped strategies better maximize RFCU. UG@B remains relatively stable across strategies, reflecting its balanced formulation. A correlation analysis reveals that RFCU and UG@B offer distinct perspectives from recall, with varying alignment across years. Together, these findings underscore the importance of aligning evaluation metrics and allocation strategies with screening goals. We release all data and code to support reproducibility and future research on cost-sensitive TAR. Full article

Background/Objectives: This study investigates the impact of Disease-Specific Care Certification (DSCC) on total medical costs associated with joint replacement surgeries in Taiwan. Methods: Using retrospective inpatient data from a regional hospital, we analyzed 660 cases of primary total knee replacement (DRG20903), total hip replacement (DRG20904), and unicompartmental knee replacement (DRG20905) classified under Taiwan’s Tw-DRG system. The dataset covered a 24-month period before certification and a 17-month period after certification, allowing for a comparison of cost changes associated with DSCC implementation. Results: While total medical costs increased slightly following certification, the differences across DRG categories were not statistically significant. However, significant increases were observed in rehabilitation costs (all DRGs), surgical costs (DRG20904 and DRG20905), anesthesia costs (DRG20904), and injection-related costs (DRG20905), indicating increased investment in standardized postoperative care. In contrast, blood transfusion and special materials costs significantly decreased in DRG20905, possibly reflecting improved care coordination and resource optimization. Additionally, the proportion of patients with prolonged hospital stays (≥11 days) declined significantly, suggesting potential efficiency gains. Conclusions: These findings imply that DSCC may facilitate better resource allocation and clinical standardization without substantially increasing overall medical expenditures, offering valuable insights for hospital administrators and policymakers operating under global budgeting systems. Full article

Optimizing healthcare resources in neurodegenerative diseases requires balancing diagnostic performance with cost constraints. We introduce AHN-BudgetNet—a tiered, cost-aware assessment framework for Parkinson’s disease motor severity prediction—evaluated on 1387 simulated PPMI subjects via patient-level GroupKFold validation. Our analysis tested seven tier combinations encompassing demographic, self-reported, and clinical features. The baseline (T0) yields AUC = 0.65 (95% CI [0.629, 0.681]) at no cost. Self-assessments (T1) alone achieved an AUC = 0.69 (95% CI [0.643, 0.733]) at USD 75, with an efficiency of 1.07. The combined T0 + T1 set reached AUC = 0.75 (95% CI [0.729, 0.772]) at USD 75, with efficiency 1.43. T2 alone obtained AUC = 0.53 (95% CI [0.517, 0.542]) at USD 300 and efficiency 0.07. The full T0 + T1 + T2 set achieved the highest performance—AUC = 0.76 (95% CI [0.735, 0.774])—at USD 375, with efficiency 0.54, reflecting diminishing returns beyond T1. High-cost tiers (T3/T4) could not be empirically validated due to over 88% missing data, emphasizing the value of accessible assessments. Gaussian Mixture on Tier 0 features yielded a silhouette score of 0.54, compared to 0.53 for K-means, confirming that patient-reported outcomes can support clinical stratification. Our results underpin evidence-based resource allocation: budgets USD ≤ 75 prioritize T1, while budgets USD ≤ 375 justify a comprehensive assessment. This confirms that structured tier prioritization supports robust, resource-efficient diagnosis in resource-limited clinical environments. Full article

The study presented in this paper is intended to be a contribution to the practical implementation of the “sustainability” concept, often misunderstood at times and incorrectly applied. The first sections describe a systematic procedure for a rigorous definition of “sustainability” and of “sustainable development” based on thermodynamics. A concept tightly connected with “sustainability” is “resource thriftiness”, i.e., the reduction in the anthropic extraction of irreplaceable supplies of fossil ores and fuels contained in the Earth’ crust and the reduction in the load posed on the environment by discharges, collectively referred to as “environmental conservation”: this is another concept that must be embedded in the definition of sustainability. An environmentally friendly society ought to concentrate on minimising such consumption by implementing an efficient and rational conversion of primary resources to final commodities while maintaining acceptable life standards. A thermodynamics-based approach can help identify the boundaries of the “sustainable region”: if sustainable development depends on a balance between primary input and final consumption, the internal allocation of the latter among citizens becomes a relevant parameter. The study presented in this paper introduces a direct link between demographics and pro capite final exergy use, showing how the age distribution of a society strongly impacts primary consumption. The paper presents some considerations about the quantitative link between the so-called “demographic pyramids” and the exergy budget of a country, with specific examples based on currently available data. Full article

With the proliferation of LEO satellite constellations and increasing demands for on-orbit intelligence, satellite networks generate massive, heterogeneous, and privacy-sensitive data. Ensuring efficient model collaboration under strict privacy constraints remains a critical challenge. This paper proposes HiSatFL, a cross-domain adaptive and privacy-preserving federated learning framework tailored to the highly dynamic and resource-constrained nature of satellite communication systems. The framework incorporates an orbital-aware hierarchical FL architecture, a multi-level domain adaptation mechanism, and an orbit-enhanced meta-learning strategy to enable rapid adaptation with limited samples. In parallel, privacy is preserved via noise-calibrated feature alignment, differentially private adversarial training, and selective knowledge distillation, guided by a domain-aware dynamic privacy budget allocation scheme. We further establish a unified optimization framework balancing privacy, utility, and adaptability, and derive convergence bounds under dynamic topologies. Experimental results on diverse remote sensing datasets demonstrate that HiSatFL significantly outperforms existing methods in accuracy, adaptability, and communication efficiency, highlighting its practical potential for collaborative on-orbit AI. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

Large language models (LLMs), built upon Transformer architectures, have demonstrated remarkable performance in a wide range of natural language processing tasks. However, their practical deployment—especially in long-context scenarios—is often hindered by the computational and memory costs associated with managing the key–value (KV) cache during inference. Optimizing this process is therefore crucial for improving LLM efficiency and scalability. In this study, we propose a novel entropy-guided KV caching strategy that leverages the distribution characteristics of attention scores within each Transformer layer. Specifically, we compute the entropy of attention weights for each head and use the average entropy of all heads within a layer to assess the layer’s contextual importance. Higher-entropy layers—those exhibiting broader attention dispersion—are allocated larger KV cache budgets, while lower-entropy (sink-like) layers are assigned smaller budgets. Instead of selecting different key–value tokens per head, our method selects a common set of important tokens per layer, based on aggregated attention scores, and caches them uniformly across all heads within the same layer. This design preserves the structural integrity of multi-head attention while enabling efficient token selection during the prefilling phase. The experimental results demonstrate that our approach improves cache utilization and inference speed without compromising generation quality. For example, on the Qwen3 4B model, our method reduces memory usage by 4.18% while preserving ROUGE score, and on Mistral 0.1v 7B, it reduces decoding time by 46.6%, highlighting entropy-guided layer analysis as a principled mechanism for scalable long-context language modeling. Full article

Modern database systems require autonomous CPU scheduling frameworks that dynamically optimize resource allocation across heterogeneous workloads while maintaining strict performance guarantees. We present a novel hierarchical deep reinforcement learning framework augmented with graph neural networks to address CPU scheduling challenges in mixed database environments comprising Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), vector processing, and background maintenance workloads. Our approach introduces three key innovations: first, a symmetric two-tier control architecture where a meta-controller allocates CPU budgets across workload categories using policy gradient methods while specialized sub-controllers optimize process-level resource allocation through continuous action spaces; second, graph neural network-based dependency modeling that captures complex inter-process relationships and communication patterns while preserving inherent symmetries in database architectures; and third, meta-learning integration with curiosity-driven exploration enabling rapid adaptation to previously unseen workload patterns without extensive retraining. The framework incorporates a multi-objective reward function balancing Service Level Objective (SLO) adherence, resource efficiency, symmetric fairness metrics, and system stability. Experimental evaluation through high-fidelity digital twin simulation and production deployment demonstrates substantial performance improvements: 43.5% reduction in p99 latency violations for OLTP workloads and 27.6% improvement in overall CPU utilization, with successful scaling to 10,000 concurrent processes maintaining sub-3% scheduling overhead. This work represents a significant advancement toward truly autonomous database resource management, establishing a foundation for next-generation self-optimizing database systems with implications extending to broader orchestration challenges in cloud-native architectures. Full article

Multimodal transportation is of strategic significance in improving transportation efficiency, reducing costs and achieving low-carbon development, all of which contribute to sustainable transportation. However, in actual operation, it often encounters multiple uncertain challenges such as demand, transportation time and carbon trading price, making it difficult for traditional fixed-parameter route optimization to meet the requirements of complex situations. Based on robust optimization and Box uncertainty set, this paper constructs a hybrid robust stochastic optimization model of multimodal transportation routes with uncertain demand, transportation time and carbon trading price, designs a hybrid algorithm, and verifies the effectiveness and rationality of the model through a numerical example. The results indicate that different types of uncertainty influence the routing decisions through distinct mechanisms. That is, demand uncertainty mainly affects capacity allocation and cost structure, transportation time uncertainty increases time penalties, and carbon trading price uncertainty drives preference for low-emission modes. Compared with the single genetic algorithm and the simulated annealing algorithm, the hybrid algorithm has better performance in terms of cost and stability. The hybrid robust stochastic optimization model can handle the multimodal transportation route selection problems where the probability distribution of parameters is unknown well. It is beneficial for decision-makers to adjust the uncertain budget level according to their preferences to formulate scientific transportation plans, so as to achieve sustainable transportation development. Full article

The Space-Air-Ground Integrated Network (SAGIN) has emerged as a core architecture for future intelligent communication due to its wide-area coverage and dynamic heterogeneous characteristics. However, its high latency, dynamic topology, and privacy–security challenges severely constrain the application of Federated Learning (FL). This paper proposes a Privacy-Preserving Federated Learning framework for SAGIN (PPFL-SAGIN), which for the first time integrates differential privacy, adaptive transfer learning, and bi-level reinforcement learning to systematically address data heterogeneity, device dynamics, and privacy leakage in SAGINs. Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global–local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. Finally, experimental evaluations validate the proposed method. Results demonstrate that PPFL-SAGIN significantly outperforms baseline solutions such as FedAvg, FedAsync, and FedAsyncISL in terms of model accuracy, convergence speed, and privacy protection strength, verifying its effectiveness in addressing privacy preservation, device selection, and global aggregation in SAGINs. Full article

This paper aims to test the hypothesis of the existence of significant tax competition between communes, which mainly concerns the share of value-added tax (VAT) proceeds, by exploring the system for allocating intergovernmental transfers in Morocco and analyzing the determinants of VAT transfers to local authorities. It contributes to fiscal federalism by assessing the design of the decentralized system and intergovernmental transfers. It aims to explore and understand the variables determining decentralization in Moroccan Municipalities over the period 2014–2018, based on institutional, budgetary, and political justifications, as well as their influence on local tax efficiency, highlighting the importance of intergovernmental transfers and their impacts on local government autonomy. We find that VAT revenue transfer antecedents include factors such as public expenditure, fiscal potential, tax effort, and political alignment. The results of this study can help better understand the relationship between VAT and economic variables and guide government tax policies in an emerging economy. This paper offers original perspectives on the importance of an informed vision for government decision-makers to develop effective tax policies considering stringent local budget constraints, the need for VAT revenue autonomy across levels of government, and the need for meeting the redistributive goals of the current VAT system. Full article

The integration of the Smart Pavement Management Center aims to improve the efficiency and quality of road maintenance in New Taipei City. This paper explores the application of the I-ROAD Reporting System and analyzes its effectiveness in providing real-time road condition updates and alerts, which assist road authorities in making timely decisions. Additionally, the study establishes a comprehensive road maintenance lifecycle model, encompassing road construction, maintenance, repair, and milling. This model systematically manages road resources to reduce maintenance costs and extend the service life of roads. Through the optimization of inspection methods and the evaluation and selection of construction techniques, the most cost-effective solutions were identified to improve the efficiency and quality of maintenance work. Lastly, the paper discusses how to optimize the budget for road maintenance within financial constraints by applying scientific budget allocation and management strategies. This ensures the efficient use of funds and that road conditions are maintained at an optimal level. This study explores the use of a road management system for the analysis and optimization of future road maintenance projects. Within a limited budget, it aims to achieve optimal allocation and management of maintenance funds through scientific financial planning, ensuring efficient use of resources to maintain road conditions at an optimal level. Full article

Static voltage stability margin is an important index for measuring the stability of the operating point of the power system, and its stochastic characterization is important for instructing the operation of power systems with a high percentage of renewable energy. On the basis of computational efficiency and accuracy, the existing uncertainty representation methods of SVSM are divided into two categories in this paper, namely high-fidelity and low-fidelity models, and the disadvantages of both methods are discussed. On this basis, an uncertainty characterization method of SVSM in power systems with a high percentage of renewable energy is proposed, based on the multi-fidelity model to achieve high-precision estimation of the moments and probabilistic distribution of SVSM. For moment estimation, an optimal input sample allocation method combining the characteristics of high- and low-fidelity models is proposed to achieve unbiased estimation of the moments of the SVSM with a pre-given computational budget. For probabilistic distribution estimation, a method based on the starting distribution is proposed to improve the estimation accuracy by using prior information provided by the multi-fidelity model. Finally, the effectiveness of the proposed method is verified by simulation calculations of a 118-bus power system. Full article