bluuebunny/crossref_metadata_2025_split huggingface dataset:
Marker-assisted selection and DH-technology utilized to accelerate fusarium-resistant cabbage (Brassica oleracea var. capitata L.) line development
Authors: Fomicheva M. G. , Kostenko G. A. , Domblides A. S. 
Published: December 2024

Relevance. The use of modern biotechnological methods in breeding, namely the doubled haploid technology and marker-assisted selection, can significantly reduce the time for creating pure lines with valuable properties. Fusarium wilt is one of the economically important diseases of white cabbage. Therefore, the development of a MAS method for selecting doubled haploids resistant to fusarium wilt would allow fast selection of resistant pure lines. Moreover, the resistance testing can be done at any plant developmental stage.Materials and methods. The response of hybrids Gertsoginya F1, Poisk 2018 F1 (resistant samples, the originator – the Agrofirm “Poisk”) and Slava 1305 (susceptible sample, the originator – Federal Scientific Vegetable Center) towards Fusarium oxysporum f. sp. conglutinans infection was determined by evaluating their growth on the inoculated soil. To test the efficiency of the FocBNUf/r marker to the polymorphic region of the FocBo1 gene, the markers were tested on resistant and susceptible genotypes. Then the markers were applied to analyze 60 doubled haploids of 12 different genotypes.Results. It was demonstrated that the FocBNUf/r marker effectively differentiated resistant and susceptible samples. Doubled haploids were obtained from 12 different breeding samples for FocBNUf/r marker-based selection of fusarium wilt resistant plants. PCR testing of doubled haploids for fusarium resistance allowed us to select 6.7-100% of plants with the fusarium resistance gene in a homozygous state in 8 genotypes. 4 genotypes of doubled haploids that did not carry the resistance gene were eliminated from the breeding program.Conclusion. The FocBNUf/r marker effectively identified resistant and susceptible samples, as well as differentiates homozygous and heterozygous plants. FocBNUf/r marker was used to select resistant doubled haploids of white cabbage at the seedling stage. Thus, it was possible not only to accelerate the production of pure lines by obtaining doubled haploids, but also to accelerate the selection of valuable samples carrying the gene of resistance to fusarium, which makes it possible to avoid the labor-intensive selection of resistant lines on inoculated soil.
Adaptive capacity and ecological stability of some bean varieties in the conditions of the western forest-steppe of the Priobie region
Authors: Parkina O. V. , Yakubenko O. E. , Nguyen N. T. 
Published: December 2024

Relevance. Crop productivity is determined by genotype, environmental effects and their interactions. Comprehensive evaluation of crop varieties based on their adaptability, plasticity and stability makes it possible to select the most promising varieties, high-yielding and adapted to many environmental conditions to improve productivity.Materials and Methods. Fourteen common bean varieties were tested in a randomized complete block design with three replications during two years (2022 and 2023) in the experimental field “Michurintsev Garden” of Novosibirsk State Agrarian University. Productivity traits were taken into account: number and weight of beans per plant, weight of 1000 seeds, yield. The purpose of the work was to evaluate bean samples for adaptive capacity and ecological stability in the conditions of the forest-steppe Priobie.Results. The article provides an assessment of the adaptability and stability of bean varieties in order to identify varieties with high productivity and adapted to climatic and farming conditions in the Siberian region. The results of the tests showed that the highest average net yields of Rubin, Krasnopestraya, Zolotistaya and Muhranula varieties were in the range of 0.41-0.64 kg/m2. Analysis of genotype-environment interaction (GxE) according to the mathematical model of Eberhart and Russel shows that varieties Rubin, Krasnopestraya, Zolotistaya and Muhranula have high indices of general adaptability and breeding value of genotype, Among them, varieties Rubin and Zolotistaya have stable productivity and high adaptability to environmental conditions.
Factors increasing productivity of aubergine under seedless cultivation in irrigated conditions of Astrakhan region
Authors: Gaplaev M. Sh. , Nechaeva S. L. 
Published: December 2024

Relevance. To increase the yield of aubergine, a number of agrotechnical methods have been studied. In the conditions of the Astrakhan region, one of the methods is the cultivation of aubergine without seedlings, in which it is very important to select varieties adapted to these conditions, suitable for the duration of the growing season. Cultivation of aubergine without seedling method, in comparison with seedling method, allows to exclude material and technical costs for growing seedlings. To increase the productivity of selected varieties, an important agronomic technique is the use of plant growth regulators that increase plant resistance to unfavourable environmental factors and have a stimulating effect on plant growth and development.Material and methodology. In conditions of Astrakhan region on alluvial-meadow, medium-loamy soils with granulometric composition were carried out studies to identify varieties that are best adapted to these natural-climatic conditions and give the highest yields in seedless cultivation. In the second experiment on seedless aubergine the effect of growth regulators on plant productivity was studied using preparations - Melafen, BP, Gibbersib, P, Biodux, Zh, Epin extra, R.Results. On drip irrigation in conditions of Astrakhan region under cultivation by seedless method were studied varietal characteristics of 6 varieties of aubergine with fruits of cylindrical shape, fruit index 4,3-4,4. Among the varieties studied, Almaz and Alekseevsky stood out by the main productivity indicators, forming 6.2-6.6 pieces of fruit per plant with an average weight of 220.7-223.5 g, which provided the formation of yield 42.3-45.8 tonnes/ha. The use of plant growth regulators Melafen, BP and Epin Extra, P on the variety Alexeevsky at the seedless method of cultivation promoted the friendly appearance of shoots, activated growth processes; the number of lateral shoots increased by 1.3-1.5 pieces, the raw above-ground weight of plants was greater by 29.1-34.0 g, the total yield increased by 27.1-34.5%, compared with the control. The varieties most suitable for growing by seedless method and showing the highest yield were identified - Alekseevsky, Almaz. It was found that the use of growth regulators accelerated plant development, increased the height of the main stem and the number of lateral shoots, weight and number of fruits on the plant, which was reflected in plant productivity. Application of growth regulator Epin extra, P allowed to obtain a yield of 52.2 t/ha, which was 34.5% higher than the yield of the control.
Status of mushroom production: global and national scenario
Authors: Devochkina N. L. , Surikhina T. N. , Ivanova M. I. 
Published: December 2024

Relevance. Mushroom farming has the potential to be a very profitable agribusiness venture that addresses several contemporary issues such as resource utilization, circular economy. Increasing mushroom production will increase its availability across the country, which will further help reduce food insecurity and also increase the income of small and marginal producers.Results. Mushrooms have enormous potential for medicinal and nutraceutical purposes. Over the past 20 years, global mushroom production has grown at a CAGR of 8.26%. This average annual growth rate is the highest for the Asian continent. Mainly six species of mushrooms dominate the global production and market, namely shiitake (26%), oyster mushroom (21%), black ear mushroom (21%), button mushroom (11%), flammulina (7%), straw mushroom (1%) and other types (13%). In the Russian Federation, industrial mushroom growing is actively developing. In recent years, 85 enterprises have been opened. The average annual growth rate of fresh mushroom production was 11.7%. Champignons and oyster mushrooms are mainly grown in Russia. Many obstacles faced by mushroom producers, including lack of technical knowledge, inadequate infrastructure, unorganized market, expensive inputs, etc., still need to be addressed through sufficient research and additional legislative solutions tailored to the requirements of Russian mushroom producers. Growing investments in smart automation to improve the efficiency and productivity of cultivated mushroom production involve advanced technologies such as robotics, artificial intelligence and the Internet of Things (IoT).
DIGITÁLIS TRANSZFORMÁCIÓ, MESTERSÉGES INTELLIGENCIA ÉS OKTATÁS
Authors: TÓTH Ágota , NÁMESZTOVSZKI Zsolt , HORVÁTH FUTÓ Hargita 
Published: December 2024

A 21. században a digitális technológia térnyerése új tanulási környezetet hozott létre, és olyan lehetőségeket biztosít a tanítási-tanulási folyamatban, amelyekre a hagyományos tantermi oktatás keretében nem volt lehetőség. Pedagógiai módszertani váltás is zajlik, s módosul a tanári szerep, az információs társadalom pedagógusa mentor, aki a diák tanulását és fejlődését segíti a piacképes, gyakorlati tudás megszerzése érdekében. A fejlődést nem lehet megállítani, ezért fontos a tanárok innovációra való nyitottsága, és azoknak a módoknak a megtalálása, amelyekkel az oktatás és nevelés szolgálatába, azaz a tantermi gyakorlatba állíthatók az információs társadalom új eszközei (a mesterséges intelligencia alapú chatbotok, fordítóprogramok, képgenerálók stb.).
Antonio González Bueno, Juan Núñez Valdés y Antonio Ramos Carrillo. Rompiendo moldes. La formación universitaria y el ejercicio profesional de la mujer en Farmacia con anterioridad a la guerra civil española (1893-1936)
Authors: Coromina Verdaguer Lluís 
Published: December 2024

Este artículo reseña: Antonio González Bueno, Juan Nú-ñez Valdés y Antonio Ramos Carrillo. Rompiendo moldes. La formación universitaria y el ejercicio profesional de la mujer en Farmacia con anterioridad a la guerra civil española (1893-1936). Sevilla: Editorial Universidad de Sevilla, 2024, 424 pp.
Explainable Machine Learning dalam Analisis Risiko Akademis Mahasiswa Fakultas Vokasi Institut Teknologi Sepuluh Nopember
Authors: Ananda Lovinki Fitra , Dewi Mukti Ratna , Habibi Mochammad Reza 
Published: December 2024

<p><strong>Abstrak:</strong></p><p>Mahasiswa dengan performa akademis yang buruk dan tingkat drop out yang relatif tinggi dapat memengaruhi akreditasi dan citra institusi pendidikan tinggi. Hal tersebut dapat diantisipasi dengan cara mengevaluasi kondisi akademik mahasiswa, khususnya pada mahasiswa yang menunjukkan penurunan performa akademis. Penelitian ini bertujuan memberikan informasi mengenai faktor-faktor yang memengaruhi risiko akademis mahasiswa menggunakan explainable machine learning. Persentase mahasiswa yang berisiko akademis hanya sebesar 7,3% sehingga kasus imbalance ini perlu ditangani menggunakan SMOTE untuk mengoptimalkan kinerja model klasifikasi. Model random forest pada data yang telah seimbang memiliki kemampuan prediksi dengan tingkat akurasi 96,4%, specificity mencapai 95%, dan nilai recall atau sensitivity sebesar 98%. Selanjutnya, SHAP diimplementasikan untuk mengetahui kontribusi masing-masing faktor terhadap potensi risiko akademik. Hasil dari SHAP menunjukkan bahwa skor TPKA kuantitatif, diikuti oleh jenis kelamin dan jalur masuk memiliki kontribusi paling tinggi terhadap risiko akademis mahasiswa</p><p>===========================================</p><p><strong>Abstract:</strong></p><p><em>Students with poor academic performance and relatively high dropout rates can affect the accreditation and image of higher education institutions. This can be anticipated by evaluating the academic conditions of students, especially those who show a decline in academic performance. This study uses explainable machine learning to provide information on the factors that influence students’ academic risk. The percentage of students at academic risk is only 7.3%, so this imbalance case needs to be handled using SMOTE to optimize the performance of the classification model. The random forest model on balanced data has a predictive ability with an accuracy level of 96.4%, specificity reaching 95%, and a recall or sensitivity value of 98%. Furthermore, SHAP is implemented to determine the contribution of each factor to the potential academic risk. The results of SHAP show that the three most significant contributing factors to students’ academic risk are the quantitative TPKA score, followed by gender and type of student admission.</em></p>
Experiments on the settling of carbonate sand–mud suspensions
Authors: Reijmer John J.G. , De Kruijf Max , Slootman Arnoud , Kranenburg L. Jonathan , De Boer Rosa A. 
Published: December 2024

ABSTRACT
This experimental study addresses the settling behavior of natural carbonate sediment suspensions. Natural carbonate suspensions differ hydrodynamically from siliciclastic suspensions due to a broader range of grain sizes, shapes, and solid densities of skeletal grains. These variations also underlie hydrodynamic differences between carbonate and siliciclastic turbidity currents. The experiments used coarse, noncohesive skeletal tropical carbonate sand obtained from the back-reef of Moorea (French Polynesia) and cohesive tropical carbonate mud retrieved from the slopes of Little Bahama Bank. Twenty-one settling experiments were conducted at volumetric sediment concentrations of 9%, 20%, and 30%. The suspensions consisted of carbonate sand mixed with cohesive carbonate mud in sand/mud ratios ranging from 96%/04% to 76%/24%. Textural trends in grain size and composition of the experiment deposits were evaluated by laser diffraction analysis and microscopic observations. Three facies were identified, from base to top: 1) interval A: weakly graded to ungraded rudstone–grainstone to floatstone–packstone, occasionally with a fining-upward base at low bulk concentrations and mud proportions, 2) interval B: normally graded grainstone to packstone, and 3) interval C: normally graded wackestone to mudstone. Interval B, the least muddy (cleanest sand) with the best sorting, has a normalized thickness consistent across experiments and unaffected by sediment concentration or mud proportion. In contrast, interval C thickens at the expense of interval A as mud proportion increased at each sediment concentration, although this trend lessens for deposits of higher-concentration suspensions where interval A is the dominant facies. In deposits of low-concentration suspensions, the fining-upward base of interval A decreased in normalized thickness with increasing sediment concentration and mud proportion. The experiments demonstrate that grain-size segregation becomes less efficient with increasing sediment concentration and/or cohesive-mud proportion. Thus, adding cohesive carbonate mud lowers the critical sediment concentration at which grain-size segregation is suppressed, resulting in thicker ungraded interval A deposits. Compared to previous siliciclastic suspension-settling experiments, grain-size segregation is suppressed at lower sediment concentrations in carbonate suspensions, although the present experiments used much coarser grain sizes. This work contributes to understanding carbonate suspension-flow deposits such as calciturbidites and calcidebrites, by hinting to: i) vertical and longitudinal (proximal to distal) grain-size sorting processes, and ii) grain-shape sorting patterns within individual deposits. Both aspects tie to the hydrodynamic behavior of individual, irregular-shaped grains in sediment suspensions with varying grain composition, as well as rheological changes due to interaction with variable quantities of cohesive carbonate mud.
A Practical Stacking Learning Framework with Hybrid Resampling for New-Well Artificial Lift Mode Selection
Authors: Cao Zhimin , Liang Yang , Han Jian 
Published: December 2024

Summary
Due to the long-term exploitation of oil and gas resources, the reservoir pressure will inevitably reduce to a level where it can no longer sustain acceptable production rates. Artificial lift provides additional energy to lift resources produced downhole to the wellhead. However, as the result of artificial lift selection will directly affect the productivity, it is crucial to select a suitable lift method. Generally, the more the parameters there are available for artificial lift selection, the higher the quality of the results will be. However, two challenges arise when selecting artificial lift methods for new wells: (A) Fewer parameters are available from new wells and (B) historical data tends to be multiclass imbalanced. To address these problems, we propose a hybrid resampling stacking ensemble learning (HRSE) framework and implement an intelligent optimization method for new-well artificial lift selection in the oil-recovery process. Specifically, HRSE mitigates the problem of limited available parameters by using stacking ensemble learning to extract valuable information from the data. Additionally, HRSE improves the accuracy of minority class samples through hybrid resampling techniques. To evaluate the performance of the proposed method, two imbalanced data sets from Daqing Oilfield were used, namely, Data Set A and Data Set B. Experimental results show that the HRSE framework achieves greater than 95% and 98% accuracy in the two minority classes of Data Set A, and with an overall average accuracy of 96%. For Data Set B, the accuracy across five minority classes reaches 95%, 91%, 97%, 94%, and 92%, respectively, and with an overall accuracy of 94%. Obviously, the proposed HRSE framework provides a more objective and scientific decision-making reference for petroleum engineers selecting artificial lift methods for new wells.
Investigation of Dynamic Adsorption and Desorption of Polymer Nanogel in Porous Media through Microfluidics
Authors: Liu Junchen , Bai Fuqiao , Almakimi Abdulaziz A. , Wei Mingzhen , He Xiaoming , Hussein Ibnelwaleed A. , Bai Baojun 
Published: December 2024

Summary
Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated the adsorption kinetics at concentrations of 0.1 wt%, 0.2 wt%, and 0.3 wt% and flow rates of 0.01 mL/h, 0.02 mL/h, and 0.03 mL/h. In addition to time, concentration, and flow rate, the experimental results showed that dynamic adsorption is also a function of transport distance, which is due to the different adsorption abilities of particles. We also found that the uneven adsorption distribution can be attenuated by decreasing nanogel concentration or increasing flow rate. The work provides a new method to obtain adsorption and desorption kinetics and adsorption profile of submicron particles in porous media at flowing conditions through microfluidics.
Innovative Method to Select Optimal Plugging Materials for Preventing Loss Circulation in Deep Fractured Reservoirs Using Machine Learning
Authors: Zhou Jie , Kang Yili , Xu Chengyuan , Ma Chenglin 
Published: December 2024

Summary
Lost circulation, a critical issue in drilling operations caused by drilling fluid loss into formation fractures, is a significant barrier in the exploration and production of oil, natural gas, and geothermal reservoirs. Effective design of the plugging formula to mitigate such losses is vital for the successful extraction of these resources. To efficiently design the plugging formula, in this paper we determine the key performance parameters of plugging materials based on the formation mechanism of the plugging zone, using them as feature input variables. We then use multitask learning (MTL) to establish a high-precision prediction model for the plugging formula, followed by the development of a mathematical optimization model for selecting performance parameters of the plugging formula, with displacement pressure and cumulative loss volume as the objective functions. An improved particle swarm optimization (PSO) algorithm is used to solve this mathematical model and determine the characteristic parameters of the plugging formula. Based on these parameters, the appropriate types of plugging materials, including bridging materials, fillers, and deformable reinforcement materials, are identified for the formula. The results show that the improved PSO algorithm outperforms the basic PSO algorithm, genetic algorithms, and whale optimization algorithms in solving the mathematical optimization model, with a performance improvement of about 10%. Additionally, sensitivity analysis confirms the model’s robustness, revealing that bridging materials play a critical role in the effectiveness of the plugging formula. As the variety of bridging, filling, and deformable reinforcement materials increases, their displacement pressure improves. More specifically, the analysis explores how the friction coefficient, D90 particle-size distribution, thermostability, compressive strength, and acid solubility of bridging materials affect displacement pressure and cumulative loss volume. Experimental findings validate that the innovative method to select optimal plugging materials for deep fractured reservoirs, leveraging MTL and intelligent optimization, facilitates the swift and effective development of deep fracture plugging strategies. This method not only assures effective fracture plugging but also minimizes material consumption in the formulations, thereby reducing overall material costs. The proposed method provides new novel perspectives and a theoretical foundation for the design of the deep fractured reservoir plugging formula.
Investigation of Acid-Etching Evolution in Natural Fractures of Carbonate Reservoirs Under High Closure Stress
Authors: Zeng Mingyong , Zhou Hangyu 
Published: December 2024

Summary
The acid-etching evolution of local natural fractures in high-stress carbonate rocks is a complex, multifield coupled phenomenon, governed primarily by closure stress loading, acid flow, and mass transfer reaction. To unravel the characteristics of this evolution and the impact of closure stress and acid injection rate on acid-etching effectiveness, a set of numerical models that integrate fluid flow, chemical reaction, and mechanical deformation within local natural fractures is established, and numerical simulations of fracture deformation and fracture acid-etching are conducted. The results show that continuous closure stress loading fosters the emergence of dominant flow channels by means of acid flow and acid-rock reaction. These channels guide most of the acid flow, continuously dissolving and etching the fracture walls, ultimately forming channelized acid-etching fractures. When closure stress ranges between 10 MPa and 15 MPa, a coupling balance between high closure stress and flow reaction enables the acid-etching fractures to attain greater widths and flow channels, significantly enhancing their long-term conductivity and deformation resistance. As the acid injection rate increases, the dominant flow channels merge into a single channelized acid-etching fracture due to competitive propagation and preferential dissolution. The dominant flow channel expands both vertically and horizontally under the scouring effect of high injection rates, leading to an increase in both the width of the channelized acid-etching fracture channel and the overall acid-etching fracture width.