Exploitation Of Space Research Articles

Austerity Infrastructure, Gentrification, and Spatial Violence: A Ceaseless Battle over Urban Space in Exarcheia Neighbourhood

AbstractIn this paper, we aim to understand how long‐term state neglect of public infrastructure coexists with a renewed emphasis on profit‐driven infrastructure development in urban contexts. We focus on Exarcheia, an Athens neighbourhood known for its radical character, where a lack of public infrastructure investment aligns with plans for new transport infrastructure and urban regeneration. Using a participatory action research approach and engaging in collective discussions with grassroots organisations opposing these plans, we argue that prolonged austerity following the 2008 economic crisis has redefined state‐funded public infrastructures, aligning them more closely with the speculative interests of real estate, commercial, and infrastructure capital. The interplay of austerity infrastructure, gentrification, and spatial violence manifests as a form of slow urbicide, paving the way for revanchist urban regeneration that prioritises the commercial exploitation of urban space at the expense of the historical, political, and cultural identities of affected neighbourhoods.

The implementation of state's rights and obligations in outer space: is it equal?

The provisions of Outer Space Law govern a state's activities in outer space. The implementation of such activities needs advanced technology, which limits the ability of many countries to explore and exploit outer space. Data indicates that developed nations dominate outer space management, leaving countries without access to sophisticated technology unable to participate fully in space exploration and exploitation. This dominance triggers disparity between developed and developing countries, leading to unequal application of rights and obligations as outlined in Outer Space Law. While the law promotes international collaboration to address this issue, it does not explicitly require developed countries to involve developing nations in outer space activities. This study aims to refine the concept of international collaboration to ensure fair management and exploitation of outer space by all countries. This study is normative in nature, focusing on evolving the concept of international collaboration in outer space. Primary and secondary legal materials and non-legal sources were used as secondary data collected through a literature review and analysed qualitatively. The developed concept emphasises international cooperation and mutual assistance, proposing a framework for collaboration between developed and developing countries. This concept seeks to ensure equitable management of outer space. It reshapes the rights and obligations of states by encouraging all launching states to involve developing countries in space exploration and exploitation.

Geek saviours to the rescue? Primitive accumulation, astropropriation, and exoimperialism in NewSpace entrepreneurship

In this paper, we examine the contemporary commercial space race – colloquially known as NewSpace – from a Marxian, technofeminist and decolonial perspective. Theorising the emergent entrepreneurial activities aimed at the human colonisation and exploitation of outer space as part of an ongoing process of primitive accumulation, we contribute innovative conceptualisations of 21st century entrepreneurial space adventurism as exoimperialism, and the privatisation of outer space resources as astropropriation. We develop the notion of geek saviourism as a function of the new geek hegemony within normative white masculinity, used to justify species-level risk under the auspices of homo entrepreneurus, a subgenre of homo economicus. Our critical examination exhorts extreme caution regarding NewSpace entrepreneurial activities, given the longstanding trajectory of entrepreneurial coloniality they reproduce, the mythoi from which they stem, and the devastatingly unequal sociogeny they can generate.

A novel solution for routing a swarm of drones operated on a mobile host

The increasing use of drones across various sectors demands optimized deployment strategies under diverse constraints. This paper tackles the Multiple Capacitated Mobile Depot Vehicles Routing Problem (mCMoD-VRP), a challenging variant of the Vehicle Routing Problem (VRP) where multiple drones with limited flight range operate from a mobile depot. The goal is to maximize target coverage while considering flight endurance, depot mobility, and drone multiplicity. We introduce a novel evolutionary algorithm, Evolutionary Optimization for Synchronized Routing Problem (EOSRP), which constructs synchronized routes for the drone swarm, accounting for all constraints. EOSRP distinguishes itself with specialized genetic operators, specifically designed to efficiently handle the constraints of mCMoD-VRP, enhancing both exploration and exploitation of the search space. EOSRP also facilitates collaborative planning among drones, enabling them to share targets and optimize routes collectively, resulting in more efficient use of flight range capacity. Comprehensive simulations on benchmark problems demonstrate that EOSRP consistently outperforms a serialized version of our previous single-drone algorithm, Genetic Algorithm for Capacitated Mobile Depot (GA-CMoD), achieving an average of 8.7% higher target coverage and 7.28% more efficient use of flight range capacity. EOSRP’s ability to generate synchronized solutions through collaborative planning leads to significantly improved mission efficiency.

Identification of influencing factors and risk assessment of underground space flooding in the mountain city

As a consequence of the process of urbanization, there is an increasing reliance on and exploitation of underground spaces. Moreover, these areas are susceptible to urban waterlogging, which can be difficult to drain, potentially leading to significant risks. At present, there are few simulations in hydrodynamic models that consider underground spaces. The risk of inundation of underground spaces remains an uncertain phenomenon. This study conceptualized underground spaces as storage areas, with the aim of simulating the spatial distribution of flood risk for different return periods. We studied the flooding processes and characteristics of three underground space types. The results indicate that the flood evolution simulated by the simplified modeling method that incorporates underground space is more aligned with the actual situation. To further analyze the risk distribution of urban waterlogging, we choose the XGBoost model and SHapley Additive exPlanations (SHAP) method to assess the influence of specific indicators on the occurrence of urban waterlogging in mountainous regions. Our analysis revealed that key factors include density of road (DR), flow velocity (V), density of pipes (DP), elevation (DEM), and inundation height (H), with contribution rates of 23.13 %, 17.06 %, 14.67 %, 10.12 %, and 9.04 %, respectively. This analysis clarified the risk of underground inundation within the study area, which can serve as a reference point for the formulation of flood control and drainage strategies.

Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street

The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

Gyro fireworks algorithm: A new metaheuristic algorithm

In this paper, a novel Gyro Fireworks Algorithm (GFA) is proposed by simulating the behaviors of gyro fireworks during the display process, which adopts a framework of multi-stage and multiple search strategies. At the beginning of the iteration, the gyro fireworks are full of gunpowder; they move via Lévy flight and spiral rotation, and the sprayed sparks are widely distributed and more balanced, which is an effective global exploration method. In the later iteration stages, due to the consumption of gunpowder, the gyro fireworks gradually undergo aggregation and contraction of spiral rotation, which is conducive to the search group to exploit the local area near the global optimal position. The GFA divides the iterative process into four phases, and each phase adopts a different search strategy, in order to enhance the diversity of the search of the population and to balance the exploration capability of the gyro fireworks search group in the global space and the exploitation of the local space. In order to verify the performance of the GFA, it is compared with the latest algorithms, such as the dandelion optimizer, Harris Hawks Optimization (HHO) algorithm, gray wolf optimizer, slime mold algorithm, whale optimization algorithm, artificial rabbits optimization, in 33 test functions. The experimental results show that the GFA obtains the optimal solution for all algorithms on 76% of the functions, while the second-placed HHO algorithm obtains the optimal solution for all algorithms on only 21% of the functions. Meanwhile, the GFA has an average ranking of 1.8 on the CEC2014 benchmark set and 1.4 on the CEC2019 benchmark set. It verifies that the GFA proposed in this paper has better convergence performance and better robustness than the competing algorithms. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the GFA over alternative algorithms.

An Adaptive Multi-Meme Memetic Algorithm for the prize-collecting generalized minimum spanning tree problem

In this paper, we address the prize-collecting generalized minimum spanning tree problem (PC-GMSTP) which aims to find a minimum spanning tree to connect a network of clusters using exactly one vertex per cluster, minimizing the total cost of connecting the clusters while considering both the costs of edges and the prizes offered by the vertices. An Adaptive Multi-meme Memetic Algorithm (AMMA) is proposed to tackle PC-GMSTP, which combines an adaptive reproduction procedure and a collaborated local search procedure. The adaptive reproduction procedure uses either crossover or mutation to produce offspring to maintain a good balance between exploration and exploitation of the search space, and the probability to use crossover or mutation is adaptively adjusted based on the diversity of population. The collaborated local search procedure, which includes two efficient local search operators, can effectively enhance the intensification ability of AMMA due to their complementary features. Extensive computational experiments on 126 challenging instances demonstrate the superiority of AMMA, outperforming 23 best-known solutions from existing literature while achieving similar solutions for the remaining 103 instances. Wilcoxon’s signed rank test confirms that the performance of AMMA is significantly better than the state-of-the-art algorithms.

Intelligent mutation based evolutionary optimization algorithm for genomics and precision medicine.

In this paper, genomics and precision medicine have witnessed remarkable progress with the advent of high-throughput sequencing technologies and advances in data analytics. However, because of the data's great dimensionality and complexity, the processing and interpretation of large-scale genomic data present major challenges. In order to overcome these difficulties, this research suggests a novel Intelligent Mutation-Based Evolutionary Optimization Algorithm (IMBOA) created particularly for applications in genomics and precision medicine. In the proposed IMBOA, the mutation operator is guided by genome-based information, allowing for the introduction of variants in candidate solutions that are consistent with known biological processes. The algorithm's combination of Differential Evolution with this intelligent mutation mechanism enables effective exploration and exploitation of the solution space. Applying a domain-specific fitness function, the system evaluates potential solutions for each generation based on genomic correctness and fitness. The fitness function directs the search toward ideal solutions that achieve the problem's objectives, while the genome accuracy measure assures that the solutions have physiologically relevant genomic properties. This work demonstrates extensive tests on diverse genomics datasets, including genotype-phenotype association studies and predictive modeling tasks in precision medicine, to verify the accuracy of the proposed approach. The results demonstrate that, in terms of precision, convergence rate, mean error, standard deviation, prediction, and fitness cost of physiologically important genomic biomarkers, the IMBOA consistently outperforms other cutting-edge optimization methods.

Parsimonious airfoil Parameterisation: A deep learning framework with Bidirectional LSTM and Gaussian Mixture models

The choice of airfoil parameterisation method significantly influences the overall wing optimisation performance by affecting the flexibility and computational efficiency of the process. Ideally, one should be able to intuitively constrain airfoil shape and structural characteristics as input to the optimisation process. Current parameterisation techniques lack the flexibility to generate airfoils efficiently by specifying parsimonious shape and structural features. To address this limitation, a deep learning framework is proposed, enabling conditional airfoil generation from an airfoil’s shape and structural feature definition.Specifically, we demonstrate the application of Bidirectional Long Short Term Memory models and Bayesian Gaussian Mixture models to derive airfoil coordinates from a compact set of shape and structural characteristics that we define. The proposed framework is shown to achieve favorable airfoil performance optimisation due to improved exploration and exploitation of the design space, compared to traditional approaches. Overall, the proposed optimisation framework is able to realise a 9.04% performance improvement over an airfoil design optimised with traditional parameterisation techniques.

A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

The utilization of flexible job shops is on the rise, driven by the decreasing life cycle of consumer products. In dynamic flexible job shops, the occurrence of disruptive events like machine breakdown, tool wear, and new job arrivals is common, emphasizing the critical need for efficient scheduling to achieve productivity and cost-effectiveness. Additionally with the growing concerns about energy consumption and global warming, the imperative to contemplate workshops with lower energy usage is becoming increasingly significant. Therefore, in this study a multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with insertion of new jobs for optimization objectives: makespan, total energy consumption and schedule instability is addressed. A novel multi-objective black widow spider algorithm (MOBWSA), inspired by the mating behavior of black widow spiders, is proposed. The MOBWSA ensures an optimal balance between minimizing makespan and energy consumption through its superior evolutionary processes of procreation, mutation and cannibalism. Furthermore, a strategic on/off strategy is implemented to enhance energy efficiency. The disruptions in schedule at rescheduling stage are addressed through the proposed sigmoidal weighted instability function. To handle the multi-objective nature of the problem this work incorporates Pareto-optimality approach in combination with novel hybrid crowding distance metric. The optimization is further aided by exploitation of search space through an ensemble of local search operators. Testing and evaluation of the MOBWSA with 30 benchmark problems is conducted, and its performance for multiple metrics is compared with recently published state-of-the-art algorithms. The results establish the effectiveness and efficiency of the proposed algorithm.

A surrogate-assisted evolutionary algorithm with dual restricted Boltzmann machines and reinforcement learning-based adaptive strategy selection

To improve the effectiveness of surrogate-assisted evolutionary algorithms (SAEAs) in solving high-dimensional expensive optimization problems with multi-polar and multi-variable coupling properties, a new approach called DRBM-ASRL is proposed. This approach leverages restricted Boltzmann machines (RBMs) for feature learning and reinforcement learning for adaptive strategy selection. DRBM-ASRL integrates four search strategies based on three heterogeneous surrogate modeling approaches, each catering to different preferences. Two of these strategies focus on generative sampling in the subspaces with varying dimensions, while the other two aim to explore the local and global landscapes in the high-dimensional source space. This allows for more effective tradeoffs between exploration and exploitation in the solution space. Reinforcement learning is employed to adaptively prioritize the search strategies during optimization , based on the online feedback information from the optimal solution. In addition, to enhance the representation of potentially optimal samples in the solution space, two task-driven RBMs are separately trained to construct a feature subspace and reconstruct the features of the source space. DRBM-ASRL has been evaluated on various high-dimensional benchmarks ranging from 50 to 200 dimensions, as well as 14 CEC 2013 complex benchmark problems with 100 dimensions and a power system problem with 118 dimensions. Experimental results demonstrate its superior convergence performance and optimization efficiency compared to eight state-of-the-art SAEAs.

Parametric estimation in three-phase induction motors using torque data via the generalized normal distribution optimizer

This research addresses the parametric estimation problem in three-phase induction motors by applying a recently developed metaheuristic method known as the generalized normal distribution optimizer (GNDO). A nonlinear programming model based on the steady-state circuit of the induction motor, which uses its Thevenin equivalent, is employed to model the estimation problem. Estimation is carried out by minimizing the mean square error between torque data (obtained from measurements or provided by the manufacturer) and the values calculated with the model. The main advantage of using the GNDO is its effective balance between the exploration and exploitation of the solution space via Gaussian distributions. Numerical tests in two three-phase induction machines confirm the effectiveness of this approach in comparison with the classical genetic algorithm, the particle swarm optimizer, and the sine cosine algorithm. The GNDO approach reports objective function values of about 9.7834×10−14 and 2.6500×10−14, while the sine cosine algorithm reaches solutions of about 4.6327×10−10 and 1.2400×10−6 in both tested motors. All numerical simulations were performed in the MATLAB software, version 2022b.

Heterogeneous pbest-guided comprehensive learning particle swarm optimization

The balance between exploration and exploitation in the particle swarm optimization (PSO) algorithm is often discussed but has not been well solved. To further improve the capabilities of exploration and exploitation in the search space, the aim of this study is to focus on a novel comprehensive learning strategy for the PSO algorithm, which is named the heterogeneous pbest-guided comprehensive learning particle swarm optimizer (HPBPSO). In this algorithm, the population is divided into two subpopulations. In the exploration subpopulation, a pbest-guided mechanism is designed for obtaining better particles’ personal best information. Meanwhile, the non-elite personal best particles learn from elite personal best particles by using dynamic crossover strategy. In the exploitation subpopulation, in order to fully leverage the benefits of learning from high-quality individuals, the learning process is divided into two distinct categories: elite individual learning and intragroup learning. Furthermore, intergroup perturbation is designed to accelerate convergence performance. The experiments on classical functions are firstly used to verify the effectiveness and mutuality of the proposed strategies. Moreover, the performance of the proposed algorithm is evaluated on two well-known benchmarks and medical image segmentation problem. According to the statistical results, the HPBPSO algorithm is not only better than the other existing state-of-the-art PSO variants and metaheuristic evolutionary algorithms, but also applicable to real-world optimization problems.

AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application

We present an updated overview of the AiZynthFinder package for retrosynthesis planning. Since the first version was released in 2020, we have added a substantial number of new features based on user feedback. Feature enhancements include policies for filter reactions, support for any one-step retrosynthesis model, a scoring framework and several additional search algorithms. To exemplify the typical use-cases of the software and highlight some learnings, we perform a large-scale analysis on several hundred thousand target molecules from diverse sources. This analysis looks at for instance route shape, stock usage and exploitation of reaction space, and points out strengths and weaknesses of our retrosynthesis approach. The software is released as open-source for educational purposes as well as to provide a reference implementation of the core algorithms for synthesis prediction. We hope that releasing the software as open-source will further facilitate innovation in developing novel methods for synthetic route prediction. AiZynthFinder is a fast, robust and extensible open-source software and can be downloaded from https://github.com/MolecularAI/aizynthfinder.

A hybrid optimization algorithm to identify unknown parameters of photovoltaic models under varying operating conditions

Enhancing the performance of optimization algorithms to accurately extract parameters for photovoltaic modules is a significant challenge in the field. While several metaheuristic algorithms have been explored, achieving a balance between exploration and exploitation capabilities remains elusive. In this context, hybrid metaheuristics have emerged as a promising approach to address this issue and improve optimization efficiency. In our study, we propose a novel hybrid algorithm that integrates the backtracking search optimization algorithm (BSA) with the differential evolution (DE) algorithm. This hybridization aims to enhance the precision of parameter extraction for various photovoltaic models, including single-diode, dual-diode, and commercially available modules such as the ST40 Thin Film, SM55 Monocrystalline, and S75 Polycrystalline. The BSA is initially employed to leverage historical experiences and guide the evolutionary process, thereby enhancing the algorithm’s exploration capabilities. Subsequently, the DE algorithm is introduced to accelerate convergence during iterations and improve exploitation of the search space. By combining these two algorithms, we aim to achieve superior accuracy and efficiency in parameter estimation. To validate the effectiveness of our approach, extensive experiments are conducted, and the results demonstrate its superiority over other sophisticated algorithms. The proposed algorithm achieves remarkable performance, as evidenced by the lowest mean square error values obtained: 9.86021877891501E-04 for the single-diode model, 9.82484851785148E-04 for the dual-diode model, 2.42507486809511E-03 for Photowatt-PWP201, 1.72981370994069E-03 for STM6-40/36, and 1.66006031250855E-02 for STP6-120/36. This highlights the algorithm’s exceptional accuracy and efficiency in extracting parameters for photovoltaic modules.

Application of Time Series Analysis and Regression Algorithm in Assessing the Effectiveness of Monetary Policy in the Era of Digital Economy

Monetary policy is the series of actions to manage country's money supply and to attain economic growth. An objection to using interest rates in monetary policy is that the use of interest rates has the problem of time uncertainty. Therefore, this research introduces Conditional Autoregressive Value-at-Risk and Henry Gas Solubility Optimization (Caviar-HGSO) with Deep Long Short Term Memory (DeepLSTM) to monetary policy forecasting to demonstrate its effectiveness. Here, Caviar-HGSO is a hybrid of Caviar and HGSO used to tune DeepLSTM weights. The HGSO algorithm follows the wind state to balance exploration and exploitation in search space while avoiding local optimization. A Caviar model estimates the parameter over time by autoregressive process and determine a parameter by regression analysis. The advantages of these two optimization algorithms lead to better monetary policy forecasts. The experimental results show that Caviar-HGSO_DeepLSTM performs better regarding MAE, MSE, and RMSE, namely 0.438, 0.103, and 0.351.

Events and urban space: a challenging relationship?

PurposeThis paper aims to consider the relationship between urban events and urban public space, asking whether cities have enough space for events and whether events have enough space in cities.Design/methodology/approachPolicy analysis surrounding events and festivals in the Netherlands is used to understand the dynamics of urban events, supported by content analysis of policy documents. A vignette of event space struggles in Amsterdam illustrates the contradictions of the event/space relationship.FindingsThe research identifies a policy shift in the Netherlands towards urban events from expansive, festivalisation strategies to defensive, NIMBYist policies. It exposes contradictions between protecting space as a living resource and the exploitation of space for regenerative purposes. Three future scenarios for urban events are outlined: conflict and competition, growth and harmony and digitalisation and virtualisation.Practical implicationsDevelops scenarios for the future relationship between events and urban space.Originality/valueProvides an analysis of the recursive spatial implications of the growth of the events sector for cities and the growth of cities for events.

Geo-environmental Suitability Assessment for Shallow Foundition Pit in Urban Zhengzhou City Based on GIS and AHP Methods

With the objective of the security and sustainability of underground space, the geo-environment suitability assessment for shallow foundation pit was conducted by GIS and AHP methods combining with the characteristics of the study area on the basis of the investigation of the current exploitation of underground space and the geological environment conditions in Zhengzhou City and the advanced experiences at home and abroad. Computer technology provides powerful tools and methods to the geo-environmental suitability assessment of underground space, and supports decision-making and planning. In the research, the suitability of geo-environment was assessed using the multi-objective linear weighted function model. The results indicate that the elements influencing the geo-environment suitability of shallow foundation pit in the study area mainly include stability of foundation pit, active earth pressure of slope, and shallow groundwater depth and water enrichment of aquifer according to the impact of evaluation factors on the foundation pit engineering. The results showed that the suitable area with an area of about 96.2 km2 is located to the west of Beijing–Guangzhou Railway, the medium-suitable area with an area of about 174.4 km2 is located to the east of Beijing–Guangzhou Railway and to the west of the Beijing-Guangzhou high-speed railway, the poor-suitable area with an area of about 111.3 km2 is located to the east of Dongfeng canal-Jingguang high-speed Railway and we should particularly focus on the impact of surface water, groundwater, soft soil and land subsidence during construction. The application of GIS and AHP method provided key technical support for the assessment. The geo-environment suitability evaluation of the foundation pit engineering by GIS and AHP methods has a strong pertinence and can offer technical assistance for the exploitation of underground space in Zhengzhou City.

How to Coordinate the Relationship between Urban Space Exploitation, Economic Development, and Ecological Environment: Evidence from Henan Province, China

How to Coordinate the Relationship between Urban Space Exploitation, Economic Development, and Ecological Environment: Evidence from Henan Province, China