Exploitation Process Research Articles

Mining-Induced Stress Control by Advanced Hydraulic Fracking under a Thick Hard Roof for Top Coal Caving Method: A Case Study in the Shendong Mining Area, China

Fully mechanized top-coal caving mining with high mining height, hard roofs and strong mining pressure are popular in the Shendong mining area, China. The occurrence of dynamic disasters, such as rock burst, coal and gas outburst, mine earthquakes and goaf hurricanes during the coal exploitation process under hard roof conditions, pose a threat to the safe production of mines. In this study, the characteristics of overburden fracture in fully mechanized top-coal caving with a hard roof and high mining height are studied, and the technology of advanced weakening by hard roof staged fracturing was proposed. The results show that the hard roof strata collapse in the form of large “cantilever beams”, and it is easy to release huge impact kinetic energy, forming impact disasters. After the implementation of advanced hydraulic fracturing, the periodic weighting length decreases by 32.16%, and the length of overhang is reasonably and effectively controlled. Ellipsoidal fracture networks in the mining direction of the vertical working face, horizontal fracture networks perpendicular to the direction of the working face, and near-linear fracture planes dominated by vertical fractures were observed, with the accumulated energy greatly reduced. The effectiveness of innovation technology is validated, and stress transfer, dissipation and dynamic roof disasters were effectively controlled.

Linear interpolated multi-objective economic emission scheduling using grey wolf optimizer: a strategic balance and solution with diverse load pattern

PurposeThe purpose of this paper is to focus on the cost-effective and environmentally sustainable operation of thermal power systems to allocate optimum active power generation resultant for a feasible solution in diverse load patterns using the grey wolf optimization (GWO) algorithm.Design/methodology/approachThe economic dispatch problem is formulated as a bi-objective optimization subjected to several operational and practical constraints. A normalized price penalty factor approach is used to convert these objectives into a single one. The GWO algorithm is adopted as an optimization tool in which the exploration and exploitation process in search space is carried through encircling, hunting and attacking.FindingsA linear interpolated price penalty model is developed based on simple analytical geometry equations that perfectly blend two non-commensurable objectives. The desired GWO algorithm reports a new optimum thermal generation schedule for a feasible solution for different operational strategies. These are better than the earlier reports regarding solution quality.Practical implicationsThe proposed method seems to be a promising optimization tool for the utilities, thereby modifying their operating strategies to generate electricity at minimum energy cost and pollution levels. Thus, a strategic balance is derived among economic development, energy cost and environmental sustainability.Originality/valueA single optimization tool is used in both quadratic and non-convex cost characteristics thermal modal. The GWO algorithm has discovered the best, cost-effective and environmentally sustainable generation dispatch.

Trophic niche overlap between coyotes and gray foxes in a temperate forest in Durango, Mexico.

Resource partitioning, and especially dietary partitioning, is a mechanism that has been studied for several canid species as a means to understand competitive relationships and the ability of these species to coexist. Coyotes (Canis latrans) and gray foxes (Urocyon cinereoargenteus) are two canid species that are widely distributed, in Mexico, and they are sympatric throughout most of their distribution range. However, trophic dynamic and overlap between them have not been thoroughly studied. In order to better understand their ecological relationship and potential competitive interactions, we studied the trophic niche overlap between both canids in a temperate forest of Durango, Mexico. The results are based on the analysis of 540 coyote and 307 gray fox feces collected in 2018. Both species consumed a similar range of food items, but the coyote consumed large species while the gray fox did not. For both species, the most frequently consumed food categories throughout the year and seasonally were fruit and wild mammals (mainly rodents and lagomorphs). Coyotes had higher trophic diversity in their annual diet (H' = 2.33) than gray foxes (H' = 1.80). When analyzing diets by season, trophic diversity of both species was higher in winter and spring and tended to decrease in summer and autumn. When comparing between species, this parameter differed significantly during all seasons except for summer. Trophic overlap throughout the year was high (R0 = 0.934), with seasonal variation between R0 = 0.821 (autumn) and R0 = 0.945 (spring). Both species based their diet on the most available food items throughout each season of the year, having high dietary overlap which likely can lead to intense exploitative competition processes. However, differences in trophic diversity caused by differential prey use can mitigate competitive interactions, allowing these different sized canid species to coexist in the study area.

Petroleum Physical Properties Prediction Application in Enhanced Oil Recovery Process

The Enhanced Oil Recovery (EOR) process is one of the ways in the petroleum exploitation process so that thick oil can be lifted to the surface and produced. The EOR process referred to in this study is the EOR process carried out in previous studies at the EOR laboratory of UPN Veteran Yogyakarta Indonesia by adding biosurfactants and adjusting the temperature. In laboratory experiments, each time an amount of biosurfactant concentration is added and the temperature is adjusted, the calculation must be done repeatedly to determine the amount of viscosity, interfacial tension (IFT), and density. This experiments takes a long time, requires high cost and variety limitation of the condition. The previous research has succeeded in building a model with multivariate polynomial regression equations to predict the value of the physical properties of crude oil from existing data then classify it into three categories using Naive Bayes, i.e., light oil, medium oil, and heavy oil. The physical properties of petroleum measured in the research are viscosity, interfacial tension, and density. The model uses laboratory data which are taken from the test results of Pertamina's KW-55 well as validation. The validation result shows that Multivariate Polynomial Regression has succeeded in predicting the value of viscosity, interfacial tension, and density with error values ranging from 0% to 1% from the sample data. With a low error value, the application can make forecasting with more variable conditions. The model still cannot be used independently without the Python environment, so to be used easily by more users, the model must be built into an independent application that can be installed on the user's device. In this research, the prediction application of petroleum physical properties has been built. The application is made using the Multivariate Polynomial Regression method according to the model in the previous study to predict the physical properties of petroleum, then uses Naïve Bayes to classify the oil. The application completed the several adjustment to shift from model to application, including user interface, system, and database adjustments.

A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

In-situ investigation on methane hydrate decomposition characteristics under variational seawater flow process

Natural gas hydrates (NGHs) are a form of natural gas widespread in the seafloor, and the realization of commercial exploitation is of great significance to solve the energy problem. There must be large amount of seawater and gas migration during the hydrate exploitation process. Studies have shown that seawater flow at a constant flow rate will enhance hydrate exploitation, especially at high flow rates. However, the seawater flow rate varies as a result of different seawater saturation in different areas of hydrate reservoir. There is little research on the effects of variational flow rate on hydrate decomposition characteristics. In this study, the effects of step-up flow rate and step-down flow rate modes on hydrate decomposition were investigated using the magnetic resonance imaging (MRI) system. The experimental results showed that the flowing seawater filled the pores that were occupied by free gas or generated by hydrate decomposition, which is beneficial to maintain the hydrate reservoir stability during the exploitation process. Through comprehensive comparison, the energy consumption of the variational flow rate mode was more than 60% less than that of the constant flow rate mode. For low hydrate saturation (22.5%), large seawater flow rate maintained a large driving force (fugacity difference) for hydrate decomposition, the efficiency of step-down flow rate mode was 131.1% greater than the efficiency of step-up mode; for high hydrate saturation (36.5%), low seawater flow rate made the mass transfer between seawater and hydrate more sufficient, the efficiency of step-up mode was 40.6% greater than that of step-down mode.

Toxic effects of shale gas fracturing flowback fluid on microbial communities in polluted soil.

A large amount of shale gas fracturing flowback fluid (FFBF) from the process of shale gas exploitation causes obvious ecological harm to health of soil and water. However, biological hazard of soil microbial populations by fracturing flowback fluid remains rarely reported. In this study, the microbiological compositions were assessed via analyzing diversity of microbial populations. The results showed significant differences between polluted soil by fracturing flowback fluid and unpolluted soil in different pH and temperature conditions. And then, the microbe-index of biological integrity (M-IBI) was used to evaluate the toxicity of the fracturing flowback fluid based on analysis of microbial integrity. The results showed that polluted soil lacks key microbial species known to be beneficial to soil health, including denitrifying bacteria and cellulose-decomposing bacteria, and 35 °C is a critical value for estimating poor and sub-healthy level of damage to microbial integrity by fracturing flowback fluid. Our results provide a valuable reference for the evaluation of soil damage by fracturing flowback fluid.

Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications

A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.

'The trees do not belong to Chief Maranke but to the Native Reserves Trust': The politics of timber resource exploitation in African reserves, colonial Zimbabwe, 1924-1948

Contextualised within a settler state characterised by racial discrimination and unequal access to natural resources, this article examines the ideological, environmental and economic considerations surrounding the formation of the Native Reserves Trust (NRT) and the role it played in the exploitation of timber resources in the African reserves of Southern Rhodesia (now Zimbabwe). Cognisant of the fact that the colonial state set aside marginal and less productive reserves for the Africans, the paper uses the NRT as a lens to view the process by which the settler society penetrated African reserves and exploited timber resources that were needed for the white-owned enterprises, while at the same time, Africans were barred from exploiting the same resources in European domains. The study further discusses the significance of timber in the African reserves, analyses the role of the NRT in regulating timber exploitation processes and the relations between the state, timber concessionaire companies and the African communities. Lastly, it assesses the extent to which timber exploitation contributed to environmental destruction, and how this prompted a policy shift, leading to the implementation of state-initiated afforestation programmes in these reserves and how these re-shaped state-African relations. On the whole, we note that the exploitation of timber resources in African areas replicated the larger colonial policy that favoured whites at the expense of Africans.

A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN.

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.

INDIGENOUS PEOPLES’ RIGHTS OVER NATURAL RESOURCES: AN ANALYSIS OF HOST COMMUNITIES RIGHTS IN NIGERIA

Many States are engulfed in crises over natural resources in the form of claims and counterclaims over who should exercise legal authority over the resources located within the state territory. In Nigeria, the agitation over control of natural resources has led to militancy and rebellion against the federal government and multinational oil companies. The debate on who should control and manage natural oil resources in Nigeria exists at the local community level, the federating states level, and the federal government level. This paper x-rayed the varying contentions of these agitations from an international law perspective. It adopted the doctrinal method to explore international human rights instruments and other legal and non-legal sources to realize the result and arrive at persuasive conclusions. The paper concluded that although international law guarantees states’ exercise of sovereign rights over their natural resources, it safeguards the right of indigenous peoples and communities to manage the natural resources found within their ancestral lands to deepen their economic and social development. It also concluded that the Niger Delta indigenous peoples and oil-producing communities are entitled to exercise some measure of control and management of the processes of exploitation of the natural resources found within their lands. The paper calls on the Nigerian government to fast-track legal and policy reforms to resource rights to indigenous host communities of natural resources in Nigeria.

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

The Failure Mechanism and Stability of the End Slope of Inclined Composite Coal Seam

In view of the inclined occurrence of coal seam in Heishan open-pit coal mine, in the longitudinal exploitation process of the first mining area, the height of the slope at the west end is increasing, and the occurrence of weak interlayer in the slope is in production. The failure mechanism and stability of the end slope of the inclined composite coal seam, which is typical of the West end slope of Heishan open-pit mine, are studied by means of field investigation, theoretical analysis, limit equilibrium analysis, and numerical simulation. The factors affecting the stability of the western side slope and the potential landslide mode are analyzed. The residual thrust method and simplified Bishop method were used to study the stability of two potential landslide modes on the western slope, and the landslide mode, final slope angle, and slope morphology were determined. FLAC3D was used to simulate the western end of the slope, reveal its landslide mechanism, and clarify the evolution law of the slope rock mass displacement. The results show that the landslide mode at the west end of the first mining area is the combined sliding mode of “cutting and bedding.” With the decrease of longitudinal mining depth, the final slope angle functions from 40° to 37°. The stress and strain are concentrated at the weak layer of the coal floor of the western side slope 13-2 and at the foot of the slope. Meanwhile, the failure also occurs inside the slope. The internal cracks connect through the weak layer of the coal floor and slide along the weak layer of the coal floor 13-2. The displacement evolution law of the slope monitoring point shows that the deformation of the slope is mainly horizontal displacement, the maximum displacement deformation is at the foot of the slope, and the degree of displacement deformation of the slope surface is obviously greater than that of the inside of the slope.

Social Entrepreneurship Education: A Combination of Knowledge Exploitation and Exploration Processes

It is widely accepted that entrepreneurship education has become a key development priority for coping with uncertainty and for addressing various social, economic, and technological concerns related to health, living conditions, education systems, employment, and economic growth. In recent times we have witnessed a proliferation of Entrepreneurship Education (EE) programs and initiatives with the aim to promote the creation of new skills, competencies, and capabilities to deal with a wide range of social issues and for creating new societal. The COVID-19 pandemic situation has introduced radical challenges to the society and has impacted significantly, especially the process of entrepreneurial competencies, skills and attitudes development. This paper aims to provide insights into knowledge mechanisms and learning strategies that have been activated for the effective creation of entrepreneurship competencies. We have based our evaluation on an exploratory analysis of 10 program studies that deliver social entrepreneurship education. The paper presents elements of originality under two perspectives: it proposes a framework with the main patterns characterizing social entrepreneurship education (SEE), and it argues that the creation of an entrepreneurship mindset and competencies is an evolutionary process that combines dynamic knowledge exploitation and exploration mechanisms.

Chemical Leaching of Sulfidic Mining Waste, Plombières Tailings Pond, Eastern Belgium: Insights from a Mineralogical Approach

Historic sulfidic mine waste material poses both an economic opportunity and an environmental threat (e.g., acid mine drainage). Due to low metal content in the mine waste and complex mineralogy, metal extraction of metal of interest is challenging. The Plombières tailings pond in eastern Belgium was selected as a case study to determine the potential for metal extraction of targeted metals from three different types of mine waste (metallurgical waste, brown, and yellow tailings). Material from this historic tailing originates from the exploitation and pyrometallurgical processing of the Mississippi Valley-type ores. The examined mine waste contains amounts of lead and zinc which can be potentially recovered by developing a hydrometallurgical method, which is selective for zinc and lead with respect to iron. From the three selected chemical extraction techniques, i.e., sulfuric acid, methanesulfonic acid, and sodium hydroxide solutions, the best results were given by sodium hydroxide solution leaching, due to negligible concentration of Fe in pregnant leaching solution, and leached metals were not precipitated into new mineral phases. Pb and Zn showed, however, a leaching efficiency of only 22% (0.0–63.6%) and 14% (0.0–29.1%) in average respectively. Furthermore, this study investigated how the mineralogy of mine waste material influences the leaching efficiency of the samples. Regardless of the lixiviant and the material, the highest leaching efficiency of Pb and Zn was achieved for samples where abundant Pb and Zn minerals were present. In samples where no Pb- or Zn-bearing minerals were detected, leaching efficiency was lower or negligible.

Experimental study on the temperature field evolution of granite under rapid cooling

Rock cracking induced by the temperature gradient is widely observed in the various processes of geothermal exploitation, including wellbore drilling, reservoir stimulation, and long-term geothermal extraction. In this work, the evolution of the temperature field and temperature gradient within granite samples during rapid cooling was experimentally investigated to study the influence of thermal cracking induced by the temperature gradient on granite. Results showed that the heat transfer process of high-temperature granite under rapid cooling is characterized by unsteady heat conduction. The evolution of the internal temperature and temperature gradient of the specimens over time could be divided into three stages: the rapid cooling stage within the first 200 s, the slow cooling stage from 200 s to 400 s, and the constant temperature stage from 400 s onward. The temperature gradient of the specimens shows a trend of rapidly increasing first and then slowly decreasing. The maximum values of the temperature gradient were always observed on the outer surface of the specimens.

Analysis of defects arising during exploitating of elements of rolling bearings

Bearings are damaged most often as a result of operation under improper lubrication conditions (no correlation between lubricant parameters and operating conditions). this often leads to the operating temperature of the grease being exceeded. Such operating conditions are either the result of the bearing operating under overload conditions (too high load causing the dropping point to be exceeded, leading to lubricant leakage and deterioration of the bearing operating conditions), or the result of improper lubrication of the bearings (application of too little grease and lack of re-lubrication of the bearing or the application of improper grease). The article presents an exemplar analysis of the influence of the exploitation process on their slow degradation.

Aircraft operators maintenance decisions supporting method

A key element of exploitation processes constitutes maintenance operations and tasks. While being conducted in the proper way, they have a crucial effect on achieving the assumed by aircraft designer and operator goals. Properly conducted maintenance operations allow to meet all the technical objects readiness requirements as well as to achieve desired acceptable risk level. Maintenance system effectiveness might be generally a crucial task for company or entity responsible for the maintenance. In this context, particularly relevant become technical object maintenance procedures and tasks developed by their manufacturers. Experience of the article authors quite early shows the need of the mainte-nance programmes modification. Aircraft manufacturers usually are not so eager to develop and implement mainte-nance programme modifications. Presented situation is very much the case in aviation transport. This was the reason why authors of this article decided to prepare and develop this elaboration which might constitute the assistance and supports complex technical objects users in maintenance decision. The main purpose of this article is to present maintenance decisions’ supporting method for the aircraft operators. This article provides guidelines which include a description of risk in the context of aviation maintenance and introduction of some methodologies, tools and criteria that support identification, analysis and evaluation of risk. Authors included idea, how the aircraft preventive maintenance could be used to mitigate aircraft failure risk during flight operations. It also shows how to adopt and develop effective maintenance program using tools for adequate risk analysis, optimal interval assignments, and selection of the most effective maintenance task. Authors presented methodology and de-scribed steps of the logic diagram analysis for the aircraft systems and their components, in order to manage and adopt aircraft maintenance program to fulfil aircraft airworthiness requirements and operational availability. The whole methodology was described on the basis of the F 16 aircraft maintenance system and with reference to the maintenance data. This article might also constitute an introduction to the aircraft maintenance programme development method.

Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability.

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization.

BackgroundData exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time.MethodsWe proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results.ResultThe experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.