Search Results (1,099)

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

This study examines the factors influencing the adoption of enterprise resource planning (ERP) systems within Romanian organizations. The objective is to develop a comprehensive framework for ERP adoption decisions, thereby advancing the field of knowledge and offering managerial insights. To accomplish this research goal, a questionnaire is envisioned, employing various research hypotheses, and distributed to a representative sample. Quantitative econometric regression analysis is employed, considering potential factors such as user training and education, competitive pressures, user involvement and participation, decentralized ERP features, top management support, data quality, the quality of the ERP system, cost and budget considerations, and business process reengineering. Of the 12 factors analyzed, 9 were found to be relevant in terms of influence on the decision to adopt ERP systems, in the context of the Romanian market. The other three factors were found to be irrelevant, thus obtaining results partially different from other areas of the world. By validating the hypotheses and answering the research questions, this work addresses a research gap regarding the lack of a comprehensive understanding of the influencing factors that shape the adoption process of ERP systems in Romania. 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

A shallow quaternary sedimentary aquifer within the river alluvial deposits of eastern Uzbekistan is increasingly vulnerable to the impacts of climate change and anthropogenic activities. Despite its essential role in supplying water for domestic, agricultural, and industrial purposes, the aquifer system remains poorly understood. This study employed a three-dimensional MODFLOW-based groundwater flow model to assess climate change impacts on water budget components under the SSP5-8.5 scenario for 2020–2099. Model calibration yielded RMSE values between 0.25 and 0.51 m, indicating satisfactory performance. Simulations revealed that lateral inflows from upstream and side-valley alluvial deposits contribute over 84% of total inflow, while direct recharge from precipitation (averaging 120 mm/year, 24.7% of annual rainfall) and riverbed leakage together account for only 11.4%. Recharge occurs predominantly from November to April, with no recharge from June to August. Under future scenarios, winter recharge may increase by up to 22.7%, while summer recharge could decline by up to 100%. Groundwater storage is projected to decrease by 7.3% to 58.3% compared to 2010–2020, indicating the aquifer’s vulnerability to prolonged dry periods. These findings emphasize the urgent need for adaptive water management strategies and long-term monitoring to ensure sustainable groundwater use under changing climate conditions. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. 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

Advanced technologies, such as robotic process automation, blockchain, and machine learning, increase audit efficiency. Nonetheless, some Supreme Audit Institutions (SAIs) have not undergone digital transformation. This research aimed to develop a comprehensive framework for supreme audit institutions to adopt and integrate emerging technologies into their auditing processes using a hybrid theoretical approach based on the TAM (Technology Acceptance Model) and TOE (Technology–Organisation–Environment) models. The framework was informed by insights from nineteen highly experienced experts in the field from eight countries. Through a two-round Delphi questionnaire, the experts provided valuable input on the key factors, challenges, and strategies for successful technology adoption by public sector audit organisations. The findings of this research reveal that technology adoption in SAIs starts with solid management support led by the chief technology officer. They must evaluate the IT infrastructure and readiness for advanced technologies, considering the budget and funding. Integrating solutions like the SAI of Ghana’s Audit Management Information System can significantly enhance audit efficiency. Continuous staff training is essential to build a positive attitude toward new technologies, covering areas like data algorithm auditing and big data analysis. Assessing the complexity and compatibility of new technologies ensures ease of use and cost-effectiveness. Continuous support from technology providers and monitoring advancements will keep SAIs aligned with technological developments, enhancing their auditing capabilities. Full article

Against the background of intensifying human–land contradictions, evaluation of land use benefits and identification of obstacles are crucial for sustainable land management and socioeconomic development. Taking Henan Province as an example, this research employed the entropy weight method and TOPSIS model to assess the land use benefits across its cities from 2011 to 2020, a period of rapid land use transformation, analyzed their spatiotemporal evolution, and identified key obstacles via an obstacle degree model. The results showed the following. (1) The social land use benefits consistently exceeded the ecological and economic benefits, with steady improvements observed in both the individual and comprehensive benefits. Spatially, the benefits showed a “one city dominant” pattern, decreasing gradually from the central region to the south, north, east, and west, with this spatial gradient further intensifying over time. (2) Economic factors were the primary obstacles, with significantly higher obstruction degrees than social or ecological factors. The main obstacles were the general budget revenue of government finance per unit land area, domestic garbage removal volume, and total retail sales of social consumer goods per unit land area. (3) The policy implications focus on strengthening regional differentiated development by leveraging Zhengzhou’s core role to boost the land-based economic benefits, integrating social–ecological strengths with agricultural modernization, and promoting “core–periphery linkage” to narrow gaps through targeted industrial and infrastructure strategies. This study could provide region-specific insights for sustainable land management in agricultural provinces undergoing rapid urbanization. Full article

Land Value Capture (LVC) is increasingly being emphasized as a key mechanism for financing mass transit systems, promoted as a sustainability-oriented policy tool amid tightening public budgets. China has adopted a development-led approach to value capture through the “Rail plus Property (R + P)” model, drawing inspiration from the Hong Kong experience. The Shenzhen Metro’s “R + P” strategy has been widely acclaimed as the key to its reputation as “the only profitable transit company in mainland China without subsidies.” This paper questions this assumption and argues that the Shenzhen model is neither sustainable nor replicable, as its past performance depended on two exceptional conditions: an ascending phase of a real-estate cycle and unique institutional concessions from the central state. To substantiate this argument, we contrast Shenzhen’s value capture strategy with that of Nanjing—a provincial capital operating under routine institutional conditions, with governance and spatial structures broadly reflecting the prevailing urban development model in China. Using a comparative framework structured around three key dimensions of LVC—urban governance, risk management, and the transit company’s shift toward real estate—this paper reveals how distinct urban political economies give rise to contrasting value capture approaches: one expansionary, prioritizing short-term profit and rapid scale-up while downplaying risk management (Shenzhen); the other conservative, shaped by institutional constraints and characterized by reactive, incremental adjustments (Nanjing). These findings suggest that while LVC instruments offer valuable potential as a funding source for public transit, their long-term viability depends on early institutional embedding that aligns spatial, fiscal, and political interests, alongside well-developed project planning and capacity support in real estate expertise. Full article

Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS^® version 23, to identify, assess, and prioritize key risks. Surveys and interviews revealed seven management factors (budget, schedule, safety, productivity, contracting, quality, and environment) and 18 critical risks, including design errors and procurement delays. The model quantifies risk impacts, provides a regression equation for risk classification, and supports effective mitigation strategies. Based on this model, decision-making can be facilitated for the implementation of effective mitigation strategies. It also promotes continuous improvement, optimizing economic resources and minimizing environmental impacts, addressing a research gap in Colombia’s petrochemical sector and paving the way for broader industrial applications. Full article

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (${10}^{-2}$ probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. 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