Search Results (14)

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

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms. Full article

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM. Full article

Unconventional natural gas extraction presents numerous opportunities and risks for communities across the United States. To capture a portion of the revenue generated by the resource states tax unconventional natural gas development. While most states collect revenue via severance taxes, Pennsylvania took a novel approach and established an impact fee on the industry instead. Unlike severance taxes in other states, the fee is collected annually and distributed directly to municipalities. While reports show that municipalities use the funds to pay for critical infrastructure, no best practices on how to allocate the funds exist. Citing the literature on mineral resource extraction and infrastructure-led development in American communities, this study examined impact fee payments made to counties with unconventional natural gas wells. The study evaluated whether counties that used the funds to invest in infrastructure were better off in terms of employment and income than other shale-producing counties that did not. Panel fixed- and random-effects regressions suggested that no statistically significant employment or income effects existed. The results suggest that local infrastructural investments are not a successful way to overcome the resource curse issues identified in the literature. Full article

This study is based on 2006–2019 panel data from 282 Chinese cities. Market segmentation and green development performance are empirically investigated to examine their non-linear relationship using static panel, dynamic panel, and dynamic spatial panel models. The results reveal the following: (1) Green development performance is found to have a high degree of temporal and spatial path dependence, exhibiting spatial linkage between cities. (2) Market segmentation stemming from local government protection has a clear inverted U-shaped structure in relationship with the green development performance. (3) Our analysis suggests that the upgrading of industrial structures significantly enhances green development, while factor price distortion inhibits it. The relationship between market segmentation and industrial structure upgrading is also an inverted U-shape. (4) The analysis further reveals that market segmentation has an inverted U-shaped correlation with the green development performance in western, central, and eastern cities. However, the different rates of development of industrial structures within the three regions result in varying degrees of market segmentation according to inflection point values. Moreover, aligned with the theoretical hypothesis of “resource curse,” in resource-based cities (exclusively), market segmentation still affects the green development performance with a significant inverted U-shaped structure. Full article

Achieving sustainable development has become the consensus of the development of human society, but many of the cities in underdeveloped resource-rich areas (UDRRAs) are sacrificing natural resources and the environment for local economic growth, which hinders the regional sustainable development. This paper uses the Solow residual method to calculate the total factor resource efficiency (TFRE) of 114 resource-based cities to assess the extent to which these cities trade resources and environment for development and identifies 59 cities in UDRRAs. The results of the study are as follows: a. Cities in UDRRAs are mainly distributed in the central and western regions and in ecologically fragile areas. b. The contribution rate of the TFRE to the economic growth of cities in UDRRAs is only 19.30%, while the contribution rate of the factor input is as high as 80.70%, and there is a phenomenon of the “resource curse” at the urban level. c. The carbon dioxide input contributed the most to the economic growth of cities in UDRRAs, accounting for 52.26%. d. The problems faced by the different types of cities in UDRRAs are quite different, especially the declining cities in UDRRAs urgently need to formulate sustainable development paths. Finally, we put forward some reference opinions on the sustainable development path of cities in UDRRAs. Full article

This study investigates whether natural gas exploitation is a blessing or a curse for provincial and prefecture economic growth. This study employs regression models and synthetic control methods (SCM) to investigate the research question based on hybrid panel and time-series data from 1993 to 2015 from 14 prefecture cities in Sichuan province in China. Based on the provincial data of Sichuan, the regression results show that natural gas exploitation is a curse for the provincial economic development of Sichuan because of the negative associations between gas resources and economic performance, for which the increasing rent-seeking in the province may partially account. However, the SCM results find that the local economy of Dazhou city may benefit from its sufficient natural gas resources in the short run, but it is not sustainable. Meanwhile, the gas resource has no significant contributions to the regional economic growth of Sichuan overall, which is partially attributed to the fact that resource exploitation has no significant spillover effects on some sustainable contributors, such as education, innovation, and others. The practical implication for the economic governors is that the resource-consumed economic route is not sustainable, although it may drive local economic growth in the short run. Full article

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency. Full article

Increasingly common methods for financing public infrastructure in developing economies are Resources-for-Infrastructure (R4I) and Resource-Financed Infrastructure (RFI), usually involving Chinese financial institutions and Chinese construction companies. Although there are advantages to the borrowing country from these project financing arrangements, there are also various issues and governance challenges. In Uganda, expectations around future revenue from oil extraction have led to many infrastructure projects being commissioned, mostly funded by RFI arrangements. To consider the appropriateness of these arrangements and to reflect on whether they are likely to contribute to positive development outcomes or be examples of the resource curse, we examined four public infrastructure projects: Kampala–Entebbe Expressway; Karuma Hydroelectric Dam; Isimba Hydroelectric Dam; and the Malaba to Kampala section of the East Africa Standard Gauge Railway. Although R4I/RFI arrangements are viewed positively by some commentators, others (especially local companies) consider they lack transparency, create unsustainable debt, promote China’s interests over the borrowing country, increase unemployment, unfairly compete with local business, deal in corruption, have poor working conditions, and result in substandard construction. Nevertheless, we conclude that Uganda and other developing countries have generally benefited from Chinese-funded infrastructure, and there is more myth trap than debt trap. However, to ensure positive development outcomes, governments and construction companies should ensure compliance with international standards, especially relating to: environmental and social impact assessment; human rights; benefit-sharing arrangements; livelihood restoration; and project-induced displacement and resettlement. Full article

Natural resources play a significant role in the development of the global economy. This refers, in particular, to strategic fuel and mineral resources. Due to the limited supply of natural resources and the lack of substitutes for most of the key resources in the world, the competition for the access to strategic resources is a feature of the global economy. It would seem that the countries which are rich in resources, because of this huge demand, enjoy spectacular economic prosperity. However, the results of empirical studies have demonstrated what is known as the ‘resource curse’. This article concentrates on the characteristics of the paradox of plenty, and in particular on the possibilities of preventing this phenomenon. The aim of this article is to identify the measures of economic policy with which to counteract the resource curse, based on the relationship between the state and the extraction business. Upon the critical analysis of the relevant literature, we concluded that the state’s economic policy, implemented in cooperation with the extraction business, is increasingly important for the prevention of the resource curse. In the context of the resource curse, the optimal and most consensual instrument, in comparison with other resource sharing agreements, is a production sharing agreement (PSA), which should also be adjusted to the current local economic conditions in a given country. Full article

Uganda’s oil and gas sector has transitioned from the exploration phase to the development phase in preparation for oil production (the operations phase). The extraction, processing, and distribution of oil require a great deal of infrastructure, which demands considerable acquisition of land from communities surrounding project sites. Here, we examine the social impacts of project land acquisition associated with oil production in the Albertine Graben region of Uganda. We specifically consider five major oil related projects that have or will displace people, and we discuss the consequences of this actual or future displacement on the lives and livelihoods of local people. The projects are: Tilenga; Kingfisher; the East African Crude Oil Pipeline; the Kabaale Industrial Park; and the Hoima–Kampala Petroleum Products Pipeline. Our findings reveal both positive and negative outcomes for local communities. People with qualifications have benefited or will benefit from the job opportunities arising from the projects and from the much-needed infrastructure (i.e., roads, health centres, airport) that has been or will be built. However, many people have been displaced, causing food insecurity, the disintegration of social and cultural cohesion, and reduced access to social services. The influx of immigrants has increased tensions because of increasing competition for jobs. Crime and social issues such as prostitution have also increased and are expected to increase. Full article

We consider the different types of rent-seeking practices in emerging oil economies, and discuss how they contribute to social conflict and a local resource curse in the Albertine Graben region of Uganda. The rent-seeking activities have contributed to speculative behavior, competition for limited social services, land grabbing, land scarcity, land fragmentation, food insecurity, corruption, and ethnic polarization. Local people have interpreted the experience of the consequent social impacts as a local resource curse. The impacts have led to social conflicts among the affected communities. Our research used a range of methods, including 40 in-depth interviews, focus group discussions, participant observation, and document analysis. We argue there is an urgent need by all stakeholders—including local and central governments, oil companies, local communities, and civil society organizations—to address the challenges before the construction of oil infrastructure. Stakeholders must work hard to create the conditions that are needed to avoid the resource curse; otherwise, Uganda could end up suffering from the Dutch Disease and Nigerian Disease, as has befallen other African countries. Full article

For a specific small-scale region with abundant resources, its copious resources tend to dictate the basic direction of its development, and may subsequently give rise to an industrial structure centered on the advantageous resources. This can give rise to an economic structure that lacks diversity, causing the economic development in the entire local region to fall into the dilemma of the resource curse. The present study conducts a case study from the perspective of small-scale regions, incorporating various types of resource-dependent cities in China, including Qingyang, Jinchang, and Baiyin, to interpret and analyze the resource curse effect by calculating a resource curse coefficient. Moreover, based on the regression model, the present study further discusses the empirical relations associated with the resource curse phenomenon. The results show that, regardless of whether a resource-dependent city is in the early, intermediate or late stage of its resource development, economic development is always plagued by the resource curse effect to a certain degree. Resource development cannot promote economic development, rather, it inhibits economic growth to some extent, resulting in an array of effects that are unfavorable to economic development, rendering the development unsustainable. For different types of resource-dependent cities, resource curse effect exhibits distinct characteristics. The resource curse effect is strongest for a resource-dependent city during an economic recession, is less severe during a development period, and is weakest during maturation. Resource development not only has a direct adverse impact on economic growth, but also often affects economic growth in multiple ways and on various levels through the Dutch disease effect, the crowding out effect, and the institution weakening effect. Until now, most results show that there is no obvious resource curse effect at the national and provincial level. The verification results of small-scale regions show that the resource curse effect at the city level still exists. In addition, the resource curse effect differs across different types of resource-dependent cities. Full article

An efficient reservoir operation technique plays a very important role in improving the water resources and energy efficiency of reservoirs. In order to effectively avoid or alleviate the “curse of dimensionality” of Multi-dimensional Dynamic Programming (MDP) in the application of cascade reservoirs operation optimization (CROO) and keep a global convergence at the same time, two dimension reduction methods are proposed in this paper. One is a hybrid algorithm of MDP and a Progressive Optimality Algorithm (POA), named MDP-POA, which combines the global convergence of MDP and the strong local search ability of POA. MDP-POA first takes the global optimal trajectory of MDP in a low discrete degree as the initial trajectory of the POA, and then implements further optimization to the obtained initial trajectory by the POA with a high discrete degree, so as to avoid the “curse of dimensionality” of MDP in high discrete degree and the dependency of the POA for the initial trajectory. The other is an improved MDP (IMDP), which first constructs a corridor by the optimal trajectory of MDP in a lower discrete degree, and then implements further optimization in the corridor by MDP with a relatively high discrete degree, so as to avoid a large number of unnecessary calculations, and shorten the run-time effectively. In a case study, the results of MDP-POA, IMDP, and MDP are compared and analyzed from the aspects of power generation and run-time. The analysis indicates that the proposed MDP-POA and IMDP both have a good application effect and are worthy of further promotion. Full article