Promising Regions Research Articles

On the question of geological prerequisites of Dnieper-Donets depression hydrodynamic traps formation

Formulation of the problem. Hydrocarbon pools in hydrodynamic traps are known within many oil and gas provinces. The Dnieper-Donets basin is one of the regions where non-vault traps of almost all types and associated with them hydrocarbon pools are widespread, while HT in DDD are not yet typical, although the geological structure of the region indicates the possibility of their formation. Thus, substantiation of the role of geological preconditions in the processes of hydrodynamic type traps formation is the main task of the work. Analysis of recent research and publications. In the monograph of O. Plotnikov (2001) among the promising regions for the search for HT defined DDD. The reason for this was the data on the presence in some productive horizons, in particular in the Sorochinsky and Rudenkivsky fields, of formation waters that lie above gas-bearing deposits. It is believed by some researchers, that the leading role in the formation of gas accumulations at these fields belongs to formation waters. According to the results of geological and geophysical study of the southern slope of the Kalmius-Torets depression and the Krasnoarmijska monocline in the south-eastern part of the DDD (A. Terdovidov (2000), S. Pavlov (2004), favorable geological prerequisites for the formation of HT in Paleozoic sediments established. Selection of previously unsolved parts of the overall problem. The general problem of searching for HT in the DDD as a whole task has not been solved so far. Formulation of the purpose of the article. Estimation of possibilities of a new direction of search and reconnaissance works in DDD, namely on non-traditional non-vault traps of hydrodynamic type substantiation. Results. The Visean dominant productive complex, regionally extended on the monocline slopes of the northern and southern sides of the depression and lithologically represented mainly by sandy horizons, was selected for priority research. The significant content of alluvial sandstones in Visean sediments section allowed the authors to consider them as special reservoirs of fluids, and paleoalluvial systems to study in the rank of independent search objects. Their territorial location is logically related to the existing deflections of the surface of the crystalline basement, most of which are associated with modern river systems, which indicates the current functioning of their relationship. The authors made an attempt to substantiate within the northern side of the DDD the following forecast water exchange systems: 1) expultional overflowing; 2) docking regime (both in the terminology of O. Plotnikov) in Paleozoic sediments; 3) infiltration water exchange in Mesozoic sediments, as necessary conditions for catching migrating upward hydrocarbons. The decrease in the infiltration waters velocity to insignificant values is interpreted by the authors as a typical phenomenon for "closed" monoclines, within which there was an equilibrium between the pressure of infiltrative waters and the resistance of expultional fluids. This equilibrium is seen as a term of creating conditions for hydrodynamic trapping. The conclusions emphasize the need for comprehensive research to confirm the predictive models of water exchange and obtain data for mapping piezometric minimums, where there is an presumable accumulation of hydrocarbons.

ГЕОЭКОЛОГИЧЕСКАЯ ОЦЕНКА ВОЗДЕЙСТВИЯ ИНЖЕНЕРНО-ГЕОЛОГИЧЕСКИХ ИЗЫСКАНИЙ АКВАТОРИИ КАСПИЙСКОГО МОРЯ НА ОКРУЖАЮЩУЮ СРЕДУ

The coastal part of the Caspian Sea is a very promising region of hydrocarbon production, where the richest deposits have been explored and exploited. However, oil production at sea is much more difficult, more expensive than on land and involves significant environmental risk. The most important tool of environmental protection activities used almost all over the world is a geoecological assessment based on a comprehensive analysis of the most likely impact of the proposed activity on the environment. This procedure is carried out in order to establish compliance of the planned economic activity with environmental requirements and to determine the feasibility of the object and to prevent possible negative impacts of this activity on the environment and the associated social, economic, environmental and other consequences. The constant increase in demand for hydrocarbon resources, their use as a priority energy source in the main branches of economic activity, as well as the consequences of the environmental impact of the development and operation of deposits have determined the relevance of this article. The purpose of the work was to disclose the procedure for assessing the planned activities during the development and operation of the hydrocarbon deposit of the Zhenis block of the Caspian Sea shelf zone on the natural components of the adjacent water area.

Influence of Low-Cost Carrier Attributes towards Airline Choice Decision in Bandung-Surabaya Route

Abstract. The number of Indonesian domestic passengers which increased rapidly has encouraged for the airlines to expand their market share by opening routes in the local region and one of the promising region is Bandung. In early 2019, the Low-Cost Carrier has implemented paid baggage policy. The changes of airline conditions and regulations could affect consumer purchasing decisions. The airline have to redesign its strategies and conduct a strategy adjusment to identify the consumer's needs and wants these days. Based on the problem description, this research aims to identify what significant attributes influence passenger's purchasing decision-making and determine what elastic and inelastic attributes. Data collection using an online questionnaire then spread to 235 respondents and were analyzed with Mixed Logit Model using BIOGEME software. The results shows that airline's departure time is a significant attribute towards passenger's airline choice. Ticket price also plays a key role in selecting an airline for passengers when the ticket price increases, Passenger will consider choosing other alternative airlines that are more affordable. The results could be used by an airline company to attract the passenger by improving its marketing strategy. Keywords: Low-cost carrier, airline choice, airline attribute, mixed logit, demand elasticities

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system loss is taken as single-objective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm.

Экосистемный подход к управлению инновационными процессами в российских регионах

Russia’s transition to innovative development is required to ensure the sustainable competitive growth. At the same time, the share of the high-tech sector in innovation costs today is about 15 %. The study suggests ways to improve the system of innovation management in Russian regions. Analysis of modern theories on the organisation of regional innovation processes confirmed a hypothesis that innovation management should consider the regional innovation ecosystem as one of the key sustainable growth institutions. A proposed ecosystem approach states that regional innovation ecosystems in the context of globalisation depend on the coordinated goals of socio-economic and innovative development, differentiated approaches to their construction, sustainable flows of knowledge and technology, diversity and competition of participants. The research determined such priority directions of the state policy as the recognition of the lack of alternative to innovative development; creation of conditions for high-tech industries outside the established business structures; increase in budget research and development (R&D) expenditures; strengthening of self-organisation of science; stimulation of horizontal interactions between science and business. The presented differentiated approach to innovative strategies of Russian regions considers their production and technological specialisation, as well as the state of science and higher education. A described methodology for selecting the most promising regions for innovative transformations is based on a comparison of the values of the author’s indices for the development of scientific and educational space and high-tech industries. The calculations show that, in addition to Moscow, Moscow oblast and Saint Petersburg that are already at the centre of the country’s innovation system, regions occupying the first 10–15 positions in the constructed rating (Nizhny Novgorod, Sverdlovsk, Novosibirsk oblasts, Republic of Tatarstan, etc.) can also become local innovation centres. The obtained results can be used in the state innovation management of the constituent entities of the Russian Federation.

A Semiautomatic Method for History Matching Using Sequential Monte Carlo

The aim of the history matching method is to locate nonimplausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via a series of waves where at each wave an emulator is fitted to a small number of training samples. An implausibility measure is defined which takes into account the closeness of simulated and observed outputs as well as emulator uncertainty. As the waves progress, the emulator becomes more accurate so that training samples are more concentrated on promising regions of the space and poorer parts of the space are rejected with more confidence. While history matching has proved to be useful, existing implementations are not fully automated, and some ad hoc choices are made during the process, which involves user intervention and is time consuming. This occurs especially when the nonimplausible region becomes small and it is difficult to sample this space uniformly to generate new training points. In this article we develop a sequential Monte Carlo (SMC) algorithm for implementing history matching that is semiautomated. Our novel SMC approach reveals that the history matching method yields a nonimplausible region that can be multimodal, highly irregular, and very difficult to sample uniformly. Our SMC approach offers a much more reliable sampling of the nonimplausible space, which requires additional computation compared to other approaches used in the literature.

СТРАТИФІКАЦІЯ КРЕЙДОВИХ ВІДКЛАДІВ РОЗРІЗУ СВЕРДЛОВИНИ № 29 – ЗАХІДНО-ОКТЯБРСЬКОЇ (КАРКІНІТСЬКО-ПІВНІЧНОКРИМСЬКИЙ ПРОГИН) ЗА ПАЛІНОЛОГІЧНИМИ ДАНИМИ

The Karkinitsko-North Crimean trough is a promising region of Ukraine for the search for hydrocarbons, and one of the main oil and gas complexes is the Cretaceous.Therefore, a detailed biostratigraphic subdivision of the Cretaceous sediments is important and necessary, which are the basis for further prospecting work and for the correlation of North Crimean trough with the similar age deposits of the adjacent regions. The Neocomian strata are especially problematic for subdivision and correlation. According to the results of the study of microphytofossils, the stratification of Cretaceous deposits of the Karkinitsko-North Crimean trough was carried out on the example of the supporting well ¹ 29 – Zakhidnoîktyabrska. There are four spore-pollen complexes: Barremian, Aptian, Albian and Cenomanian. For the first time, the Cretaceous deposits were dissected according to the systematic composition of dinocysts, as well as the paleontological characteristics of sediments by other groups of microfossils – spores and pollen of higher plants, remnants of green algae, acritarchsand miñroforaminifers.

Short term wind energy resource prediction using WRF model for a location in western part of Turkey

Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.

ГЕОЭКОЛОГИЧЕСКОЕ СОСТОЯНИЕ ОКРУЖАЮЩЕЙ ПРИРОДНОЙ СРЕДЫ В РАЙОНЕ НЕФТЕГАЗОВОГО УЧАСТКА "ЖЕНИС" ШЕЛЬФОВОЙ ЗОНЫ КАСПИЯ

Energy is considered one of the key factors of human development, providing the necessary conditions for its existence and vital activity, the degree of material and economic well-being. The leading position among fuel and energy resources at the present time belongs to oil, as the most productive energy carrier. Overcoming the current economic crisis is associated with an increase in global demand for black gold and an increase in the pace of its production. The involvement of ever-increasing volumes of hydrocarbon resources in production circulation reduces their reserves and leads to the need for exploration and development of ever-new deposits. It is a well-known fact that since the peak of the formation of geological exploration in the 60s of the XX century to the present period, the number of discovered deposits on the onshore part is steadily decreasing, and the number of developments on the shelf is increasing. The offshore zone of the Caspian Sea is a very promising region for the production of hydrocarbons, where the richest deposits have been explored and exploited. However, oil production at sea is much more difficult, more expensive than on land and involves significant environmental risk. The most important tool of environmental protection activities used almost all over the world is an environmental assessment based on a comprehensive analysis of the most likely impact of the proposed activity on the environment. This procedure is carried out in order to establish the compliance of the planned economic activity with environmental requirements and to determine the admissibility of the implementation of the object and to prevent possible negative impacts of this activity on the environment and the associated social, economic, environmental and other consequences. The constant increase in demand for hydrocarbon resources and their involvement in economic turnover, as well as the reduction of reserves and environmental risks associated with their production and exploitation of deposits have determined the relevance of this article. The purpose of the work was to identify the features of the current ecological state of the marine environment in the water area of the planned development of hydrocarbon deposits.

Reorganization of industrial zones as a factor of sustainable development of urban areas

The reuse of industrial zones is becoming one of the determinants of sustainable urban development, which ensures the effective use of urban areas, taking into account the principle of their integrated development. However, the modern urbanized environment has serious environmental problems, which limits the active use of the redevelopment tool. The conducted research of the implemented redevelopment projects showed that their main share is accounted for the construction of apartments and housing, multinational complexes, creative spaces and offices. The results of the cluster analysis of the territories of Russia allowed the authors to identify the most promising regions for redevelopment.

Geography and Current State of Apitourism in Ukraine and Slovenia

The article provides a comparative analysis of the geography and the current state of apitourism in Ukraine and Slovenia. The essence of apitourism and its importance for achieving the goals of sustainable development are characterized. Apitourism is a form of tourism related to beekeeping as a traditional profession and beekeeping products in ecological, nutritional and medical aspects. At its core, apitourism is permanent, so it does not deplete resources, but contributes to their enrichment. The main prerequisites for the development, markets and geography of apitourism in Ukraine and Slovenia are analyzed. Ukraine is one of the main producers of honey in the world. The most promising regions of Ukraine for the development of apitourism are the Carpathian and Transcarpathian regions since beekeeping there is a widespread industry, a priority and quite profitable. Slovenia does not have a significant number of hives and beekeepers or a significant production of honey, but bees play a huge role in Slovenian culture. Slovenia is the only member state of the European Union to protect its native Karniolska bee and one of the first in Europe to ban the use of pesticides harmful to bees. Slovenian honey has the status of a protected geographical indication. Famous apitouristic centers in Slovenia are Ljubljana, Bled, Radovlica, Laško, Brda, Zhirovnice, Maribor, Aquarius, Bohinj and many others, that is, villages, cities, entire regions. The main problems of development of apitourism in Ukraine are revealed. The experience of Slovenia in the development of apitourism is studied and the priority directions of the development of apitourism in Ukraine are determined. Ukraine should develop the existing potential for apitourism, in particular, provide a favorable regulatory framework, create regional programs for the development of apitourism and beekeeping, protect bees from pesticides, grant the status of a geographical indication to Ukrainian honey, in particular, Transcarpathian. Public organizations should study international (in particular, Slovenian) and domestic experience in the field of apitourism, prepare teaching materials, organize training of sandstones in the provision of apitourism services and their promotion. The apitouristic route “The Honey Route of Two Countries: Ukraine and Slovenia” is developed. To increase interest in apitourism among promising tourists, it is necessary to develop new apitourism programs and routes, in particular, not only domestic, but also international (for example, Ukraine-Slovenian).

General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification

ABSTRACT Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms.

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

Adaptive sampling with an autonomous underwater vehicle in static marine environments

AbstractThis paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point‐source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real‐time, for which standard (e.g., grid‐based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions. Specifically, this study employs the GP upper confidence bound as the optimization criteria to adaptively plan sampling paths that balance a trade‐off between exploration and exploitation. Two informative path planning algorithms based on (i) branch‐and‐bound techniques and (ii) cross‐entropy optimization are presented for choosing future sampling locations while considering the motion constraints of the sampling platform. The effectiveness of the proposed methods are explored in simulated scalar fields for identifying multiple regions of interest within a three‐dimensional environment. Field experiments with an AUV using both virtual measurements on a known scalar field and in situ dissolved oxygen measurements for studying hypoxic zones validate the approach's capability to quickly explore the given area, and then subsequently increase the sampling density around regions of interest without sacrificing model fidelity of the full sampling area.

QTL analysis across multiple environments reveals promising chromosome regions associated with yield-related traits in maize under drought conditions

Drought is one of the most critical abiotic stresses influencing maize yield. Improving maize cultivars with drought tolerance using marker-assisted selection requires a better understanding of its genetic basis. In this study, a doubled haploid (DH) population consisting of 217 lines was created by crossing the inbred lines Han 21 (drought-tolerant) and Ye 478 (drought-sensitive). The population was genotyped with a 6 K SNP assay and 756 SNP (single nucleotide polymorphism) markers were used to construct a linkage map with a length of 1344 cM. Grain yield (GY), ear setting percentage (ESP), and anthesis–silking interval (ASI) were recorded in seven environments under well-watered (WW) and water-stressed (WS) regimes. High phenotypic variation was observed for all traits under both water regimes. Using the LSMEAN (least-squares mean) values from all environments for each trait, 18 QTL were detected, with 9 associated with the WW and 9 with the WS regime. Four chromosome regions, Chr. 3: 219.8–223.7 Mb, Chr. 5: 191.5–194.7 Mb, Chr. 7: 132.2–135.6 Mb, and Chr. 10: 88.2–89.4 Mb, harbored at least 2 QTL in each region, and QTL co-located in a region inherited favorable alleles from the same parent. A set of 64 drought-tolerant BC3F6 lines showed preferential accumulation of the favorable alleles in these four regions, supporting an association between the four regions and maize drought tolerance. QTL-by-environment interaction analysis revealed 28 edQTL (environment-dependent QTL) associated with the WS regime and 22 associated with the WW regime for GY, ESP, and ASI. All WS QTL and 55.6% of WW QTL were located in the edQTL regions. The hotspot genomic regions identified in this work will support further fine mapping and marker-assisted breeding of drought-tolerant maize.

An Enhanced Differential Evolution Algorithm with Fast Evaluating Strategies for TWT-NFSP with SSTs and RTs

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times (i.e., the NFSP with SSTs and RTs) is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective (TWT) for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES.

Change in prefrontal activity and executive functions after action-based cognitive remediation in bipolar disorder: a randomized controlled trial.

Cognitive impairment is prevalent in bipolar disorder (BD) but treatments with pro-cognitive effects are lacking. Insight concerning the neurocircuitry of cognitive improvement could provide a biomarker for pro-cognitive effects to advance treatment development. The dorsal prefrontal cortex (dPFC) is a promising region for such treatment target engagement. The aim of this functional magnetic resonance imaging (fMRI) study was to examine the effects of action-based cognitive remediation (ABCR) on early change in the dPFC blood-oxygen-level-dependent response in patients with BD in remission, and whether the observed neural change predicted improved executive functions following 10 weeks of treatment. Forty-five participants with remitted BD (ABCR: n = 26, control treatment: n = 19) completed a spatial n-back working memory task during fMRI and executive function tasks outside the scanner before and after two weeks of ABCR/control treatment, and an additional assessment of executive function at treatment completion. Thirty-four healthy controls underwent a single fMRI and executive function assessment for baseline comparisons. We found an early reversal of pretreatment hypo-activity in the dorsolateral prefrontal cortex (dlPFC) following ABCR vs. control during both high-load (2-back > 1-back) working memory (WM) (F(1,43) = 5.69, p = 0.02, η2 = 0.12) and general WM (2-back > 0-back) (F(1,43) = 5.61, p = 0.02, η2 = 0.12). This dlPFC activity increase predicted improved executive functions at treatment completion (high-load WM: B = -0.45, p = 0.01, general WM: B = -0.41, p < 0.01), independent of changes in subsyndromal symptoms. In conclusion, early dPFC increase may provide a neurocircuitry-based biomarker for pro-cognitive effects. Future cognition trials should include fMRI assessments to confirm the validity of this putative biomarker model across disorders with cognitive impairment.

A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.

Dual Probability Learning Based Local Search for the Task Assignment Problem

The task assignment problem (TAP) is concerned with assigning a set of tasks to a set of agents subject to the limited processing and memory capacities of each agent. The objective to be minimized is the total assignment cost and total communication cost. TAP is a relevant model for many practical applications, yet solving the problem is computationally challenging. Most of the current metaheuristic algorithms for TAP adopt population-based search frameworks, whose search behaviors are usually difficult to analyze and understand due to their complex features. In this work, unlike previous population-based solution methods, we concentrate on a single trajectory stochastic local search model to solve TAP. Especially, we consider TAP from the perspective of a grouping problem and introduce the first probability learning-based local search algorithm for the problem. The proposed algorithm relies on a dual probability learning procedure to discover promising search regions and a gain-based neighborhood search procedure to intensively exploit a given search region. We perform extensive computational experiments on a set of 180 benchmark instances with the proposed algorithm and the general mixed integer programming solver CPLEX. We assess the composing ingredients of the proposed algorithm to shed light on their impacts on the performance of the algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the problem of program modules designing and task allocation in parallel and distribution systems. It can also be applied to deal with job (task) grouping problems in practical industrial applications. This article presents a novel and effective learning-based local search algorithm to obtain high-quality solutions for the considered problem. The results of numerical experiments and comparisons show that our algorithm can achieve good search performances on problem instances of different scales and difficulties. Afterward, we use the proposed solution method to solve a real-life open-order slab assignment problem, which is derived from the production planning of silicon steel for an iron and steel company. The learning techniques of the proposed algorithm are of general interest and can be used in search algorithms for solving other real-life optimization problems with grouping features. For future research, we will design solution methods based on these learning techniques to address other practical optimization problems.

Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning

Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes.&#x0D; Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy.