Search Results (121)

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring. Full article

With the advancement of network communication technology and Internet of Everything (IoE) technology, which connects all edge devices to the internet, the network traffic generated in various platform environments is rapidly increasing. The increase in network traffic makes it more difficult for the detection system to analyze and detect malicious network traffic generated by malware or intruders. Additionally, processing high-dimensional network traffic data requires substantial computational resources, limiting real-time detection capabilities in practical deployments. Artificial intelligence (AI) algorithms have been widely used to detect malicious traffic, but most previous work focused on improving accuracy with various AI algorithms. Many existing methods, in pursuit of high accuracy, directly utilize the extensive raw features inherent in network traffic. This often leads to increased computational overhead and heightened complexity in detection models, potentially degrading overall system performance and efficiency. Furthermore, high-dimensional data often suffers from the curse of dimensionality, where the sparsity of data in high-dimensional space leads to overfitting, poor generalization, and increased computational complexity. This paper focused on feature engineering instead of AI algorithm selections, presenting an approach that uniquely balances detection accuracy with computational efficiency through strategic dimensionality reduction. For feature engineering, two jobs were performed: feature representations and feature analysis and selection. With effective feature engineering, we can reduce system resource consumption in the training period while maintaining high detection accuracy. We implemented a malicious network traffic detection framework based on Convolutional Neural Network (CNN) with our feature engineering techniques. Unlike previous approaches that use one-hot encoding, which increases dimensionality, our method employs label encoding and information gain to preserve critical information while reducing feature dimensions. The performance of the implemented framework was evaluated using the NSL-KDD dataset, which is the most widely used for intrusion detection system (IDS) performance evaluation. As a result of the evaluation, our framework maintained high classification accuracy while improving model training speed by approximately 17.47% and testing speed by approximately 19.44%. This demonstrates our approach’s ability to achieve a balanced performance, enhancing computational efficiency without sacrificing detection accuracy—a critical challenge in intrusion detection systems. With the reduced features, we achieved classification results of a precision of 0.9875, a recall of 0.9930, an F1-score of 0.9902, and an accuracy of 99.06%, with a false positive rate of 0.65%. Full article

This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article

The increasing economic engagement of China in Africa through foreign direct investment (FDI) and trade has raised concerns about its environmental consequences, particularly resource depletion. While the existing literature highlights the role of FDI and trade in resource exploitation, the varying impacts across governance contexts remain underexplored. This study investigates how Chinese FDI and trade influence resource depletion in Africa, integrating institutional and resource curse perspectives to explain divergent outcomes. Using dynamic panel data models and the system generalized method of moments (SGMM) to address endogeneity, we analyze data from 28 African countries between 1998 and 2022. The results show that Chinese FDI significantly accelerates resource depletion—particularly total resources, energy, and forests—especially in low-governance countries. In contrast, Chinese trade exhibits a limited relationship with depletion, with significant effects only on energy resources in weak institutional settings. Notably, neither FDI nor trade has significant effects in high-governance countries, underscoring the protective role of strong institutions. The study contributes to the literature by demonstrating how governance quality mediates the environmental impacts of Chinese economic engagements. It offers policy insights for African nations, emphasizing institutional strengthening to align foreign investments and trade with sustainable resource management. These findings call for balanced economic policies that prioritize environmental sustainability alongside economic growth. Full article

Mining has significantly shaped Eastern European economies, particularly during their transition from centrally planned to market-oriented systems. While abundant natural resources can lead to a “resource curse” that hinders economic growth, they also offer opportunities for sustainable development if managed effectively. This study investigates the dynamics of mineral resource rents in Eastern Europe, shaped by economic freedom, urbanization, educational achievement, and international trade, from 1990 to 2021. Using methods such as MMQR, AMG Robustness Analysis, CCEMG, fixed effects, cointegration, Granger causality, and unit root tests, the study provides a comprehensive analysis of these relationships. The findings reveal that educational achievement reduces reliance on mineral resource rents by fostering human capital and supporting economic diversification. Urbanization similarly decreases resource dependency by promoting innovation and technological advancement. Trade openness also shows a negative link with mineral rents, suggesting that global integration facilitates shifts toward more advanced, technology-driven sectors. Economic freedom presents mixed results, highlighting the need for strong governance to ensure sustainable and equitable outcomes. This study is novel in integrating these factors into a unified framework, specifically applied to Eastern Europe’s post-communist transition, a region often overlooked in global resource studies. The results contribute most directly to Sustainable Development Goal 4 on Quality Education by demonstrating how human capital development reduces resource dependence and promotes economic resilience, and to Sustainable Development Goal 8 on Decent Work and Economic Growth, by showing that trade openness and economic diversification can drive sustainable economic progress. Ultimately, the study offers actionable insights for balancing economic growth with environmental and social sustainability in transitional economies. Full article

Mobile edge computing (MEC) systems empowered by energy harvesting (EH) significantly enhance sustainable computing capabilities for mobile devices (MDs). This paper investigates a multi-user multi-server MEC network, in which energy-constrained users dynamically harvest ambient energy to flexibly allocate resources among local computation, task offloading, or intentional task discarding. We formulate a stochastic optimization problem aiming to minimize the time-averaged weighted sum of execution delay, energy consumption, and task discard penalty. To address the energy causality constraints and temporal coupling effects, we develop a Lyapunov optimization-based drift-plus-penalty framework that decomposes the long-term optimization into sequential per-time-slot subproblems. Furthermore, to overcome the curse of dimensionality in high-dimensional action, we propose hierarchical deep reinforcement learning (DRL) solutions incorporating both Q-learning with experience replay and asynchronous advantage actor–critic (A3C) architectures. Extensive simulations demonstrate that our DRL-driven approach achieves lower costs compared with conventional model predictive control methods, while maintaining robust performance under stochastic energy arrivals and channel variations. Full article

This work proposes a power-efficient framework for adaptive video streaming in UAV-assisted wireless networks specially designed for disaster-hit areas where existing base stations are nonfunctional. Delivering high-quality videos requires higher video rates and more resources, which leads to increased power consumption. With the increasing demand of mobile video, efficient bandwidth allocation becomes essential. In shared networks, users with lower bitrates experience poor video quality when high-bitrate users occupy most of the bandwidth, leading to a degraded and unfair user experience. Additionally, frequent video rate switching can significantly impact user experience, making the video rates’ smooth transition essential. The aim of this research is to maximize the overall users’ quality of experience in terms of power-efficient adaptive video streaming by fair distribution and smooth transition of video rates. The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. The formulated problem is non-convex and difficult to solve with conventional methods. Therefore, to avoid the curse of complexity, the block coordinate descent method, successive convex approximation technique, and efficient iterative algorithm are applied. Extensive simulations are performed to verify the effectiveness of the proposed solution method. The simulation results reveal that the proposed method outperforms 95–97% over equal allocation, 77–89% over random allocation, and 17–40% over joint allocation schemes. Full article

Starting with the debate on the “resource curse”, the main aim of this paper is to evaluate the impact of natural resource rents on income/wealth inequality in the European Union (EU) during the period from 1990 to 2023. Excepting the Gini index, natural resources rents reduced other measures of income and wealth inequality, and the results indicate that growth has a masking mediating effect on the Gini index, but no mediation role of GDP was observed in the case of the top 1% income/wealth share. The income inequality based on the top 1% share significantly increased in Denmark after the discovery of oil and gas relative to the control group composed of Finland and Sweden. Other control variables are considered, and some policy recommendations are proposed to reduce income/wealth inequality. Full article

This study addresses the resource-constrained project scheduling problem with flexible structures and uncertain activity durations. The problem is formulated as a Markov decision process, with the optimal policy determined through stochastic dynamic programming. To mitigate the curse of dimensionality in large-scale problems, several approximate methods are proposed to derive suboptimal policies. In addition to traditional methods based on priority rules and metaheuristic algorithms, we focus on the application of rollout algorithms. To improve the computational efficiency of the rollout algorithms, only the best-performing priority rules are employed for action evaluation, and the common random numbers technique is also incorporated. Experimental results demonstrate that rollout algorithms significantly outperform priority rules and metaheuristics. The common random numbers technique not only enhances computational efficiency but also improves the accuracy of action selection. The post-rollout algorithm reduces computation time by 44.37% compared to the one-step rollout, with only a 0.02% performance gap. In addition, rollout algorithms perform more stably than other methods under different problem characteristics. Full article

Economic transformation is an effective strategy for resource-based regions to avoid the “resource curse”. In China’s high-quality development stage, using new-generation IT to guide economic structure adjustment, industrial upgrading, and technological innovation is of great practical significance. It also helps regions achieve ecological and high-quality development. Based on SDM, this paper takes smart eco-innovation as the driving force for high-quality transformation. The system constructed is built from five aspects: innovation, coordination, green development, openness, and sharing. Additionally, based on the interrelationships among the subsystems, this paper constructs causal loop diagrams and flow diagrams. Taking Daqing City in China as an example, it conducts a scenario simulation of high-quality transformation driven by smart eco-innovation. The finding shows that the combined policy effects of smart eco-innovation are the most significant for the high-quality transformation of resource-based regions. This study provides a new perspective. It explores the transformation of resource-based regions driven by a “smart eco-style” approach and provides references for such regions in China. Full article

Civilization begins with rivers, and so does pollution. Examining and deciphering the possible ecological curse effect of abundant river resources has a profound impact on sustainable economic development. This paper empirically examines the impact of urban rivers on the environmental performance of listed companies by constructing indicators of urban river length and density and measuring river resources in prefecture-level cities with a sample of listed companies in China from 2011 to 2022. It is found that the richer the urban rivers are, the lower the environmental performance of listed companies in the region, and the conclusion still holds after the robustness and endogeneity tests, proving that rivers as important natural resources also have the ecological curse effect. Further mechanism analysis reveals that urban river resources will reduce the production cost of enterprises and generate path dependence, which will have a crowding-out effect on the green innovation of enterprises; at the same time, abundant river resources will induce market failure, and institutional weakness accelerates the polluting behavior of enterprises. The research in this paper enriches the micro impacts and mechanisms of the ecological curse and provides useful references for river pollution management. Full article

Clustering is a powerful and efficient technique for pattern recognition which improves classification accuracy. In machine learning, it is a useful unsupervised learning approach due to its simplicity and efficiency for clustering applications. The curse of dimensionality poses a significant challenge as the volume of data increases with rapid technological advancement. It makes traditional methods of analysis inefficient. Sparse clustering is essential for efficiently processing and analyzing large-scale, high-dimensional data. They are designed to handle and process sparse data efficiently since most elements are zero or lack information. In data science and engineering applications, they play a vital role in taking advantage of the natural sparsity in data to save computational resources and time. Motivated by recent sparse k-means and k-means++ algorithms, we propose two novel Lasso-based k-means++ (Lasso-KM++) clustering algorithms, Lasso-KM1++ and Lasso-KM2++, which incorporate Lasso regularization to enhance feature selection and clustering accuracy. Both Lasso-KM++ algorithms can shrink the irrelevant features towards zero, and select relevant features effectively by exploring better clustering structures for datasets. We use numerous synthetic and real datasets to compare the proposed Lasso-KM++ with k-means, k-means++ and sparse k-means algorithms based on the six performance measures of accuracy rate, Rand index, normalized mutual information, Jaccard index, Fowlkes–Mallows index, and running time. The results and comparisons show that the proposed Lasso-KM++ clustering algorithms actually improve both the speed and the accuracy. They demonstrate that our proposed Lasso-KM++ algorithms, especially for Lasso-KM2++, outperform existing methods in terms of efficiency and clustering accuracy. Full article

This study focuses on the effect of financial development, natural resource rent, human development, and technological innovation on the ecological and carbon footprints of the G-10 countries between 1990 and 2022. This study also considers the impact of globalization, trade openness, urbanization, and renewable energy on environmental degradation. The study uses Kao and Westerlund DH cointegration tests, FMOLS and DOLS estimators, and panel Fisher and Hatemi-J asymmetric causality tests to provide reliable results. Long-run estimates confirm an inverted U-shaped linkage between financial development and ecological and carbon footprints. Natural resource rent and technological innovation increase ecological and carbon footprints, while human development decreases them. Furthermore, globalization, trade openness, and renewable energy contribute to environmental quality, while urbanization increases environmental degradation. The Fisher test findings reveal that financial development, natural resource rent, human development, and technological innovation have a causal link with the ecological and carbon footprint. The results of the Hatemi-J test show that the negative shocks observed in the ecological and carbon footprint are affected by both negative and positive shocks in financial development, natural resource rent, and technological innovation. Moreover, positive and negative shocks in human development are the main drivers of negative shocks in the carbon footprint, while positive shocks in human development lead to negative shocks in the ecological footprint. Full article

To address the resource allocation problem in dynamic environments where multiple unmanned aerial vehicle base stations (UAV-BSs) provide efficient downlink services to ground users, this paper proposes a novel hierarchical decision-making mechanism based on the Proximal Policy Optimization (PPO) algorithm. The proposed method optimizes time-frequency resource allocation in the downlink, aiming to maximize the total user throughput over multiple time slots. By constructing channel and interference models, the complex multi-channel resource allocation problem is decomposed into a series of single-channel decision subproblems, significantly reducing the action space complexity. Specifically, the original exponential complexity $\mathcal{O}\left(N^M\right)$ (where N is the number of users and M is the number of channels) is reduced to a linear complexity $\mathcal{O}\left(N\right)$, effectively alleviating the curse of dimensionality. Simulation results demonstrate that the proposed hierarchical architecture, integrated with the PPO algorithm, achieves superior performance in terms of total throughput, convergence speed, and stability compared to existing methods. This study provides new insights and technical support for efficient resource management in UAV-BS systems operating in complex and dynamic environments. Full article

Low-carbon pilot policies are essential for the green transformation of resource-based cities, helping them mitigate the “carbon curse” and the “resource curse” while promoting sustainable socio-ecological development. Focusing on a panel of 114 resource-based cities in China, spanning from 2003 to 2022, this study employs a range of methodologies, including kernel density estimation, the Difference-in-Differences Model, Spatial Difference-in-Differences, Mediation Analysis, K-means Clustering, and Dual Machine Learning to assess the consequences of low-carbon pilot policies on socio-ecological resilience. The findings indicate that the socio-ecological resilience of the study area has generally improved, though there is noticeable polarization. Low-carbon pilot policies significantly enhance the resilience of resource-based cities by 0.4%, and they exhibit a positive spatial spillover effect of 1.1%. However, the long-term effects of the policies on economic resilience were not significant, and the policies did not have a direct impact on the social resilience of the pilot cities; however, they did promote social resilience in neighboring regions. Finally, the effectiveness of low-carbon pilots varies, with more pronounced benefits in declining and mature resource cities, particularly in those with medium ecological and economic resilience, and low social resilience. Green finance, industrial transformation, and carbon emission efficiency are identified as key strategies for improving socio-ecological resilience. The above findings provide insights for policymakers seeking to foster inclusive, resilient, and sustainable urban development in China. Full article