bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploratory Study of Sex Identification for Chicken Embryos Based on Blood Vessel Images and Deep Learning
Authors: Jia Nan , Li Bin , Zhao Yuliang , Fan Shijie , Zhu Jun , Wang Haifeng , Zhao Wenwen
Published: July 2023

The identification of a chicken’s sex is a massive task in the poultry industry. To solve the problems of traditional artificial observation in determining sex, such as time-consuming and laborious, a sex identification method of chicken embryos based on blood vessel images and deep learning was preliminarily investigated. In this study, we designed an image acquisition platform to capture clear blood vessel images with a black background. 19,748 images of 3024 Jingfen No. 6 breeding eggs were collected from days 3 to 5 of incubation in Beijing Huadu Yukou Poultry Industry. Sixteen thousand seven hundred sixty-one images were filtered via color sexing in 1-day-old chicks and constructed the dataset of this study. A sex identification model was proposed based on an improved YOLOv7 deep learning algorithm. An attention mechanism CBAM was introduced for YOLOv7 to improve the accuracy of sex identification of chicken eggs; the BiFPN feature fusion was used in the neck network of YOLOv7 to fuse the low-level and high-level features efficiently; and α-CIOU was used as the bounding box loss function to accelerate regression prediction and improve the positioning accuracy of the bounding box of the model. Results showed that the mean average precision (mAP) of 88.79% was achieved by modeling with the blood vessel data on day 4 of incubation of chicken eggs, with the male and female reaching 87.91% and 89.67%. Compared with the original YOLOv7 network, the mAP of the improved model was increased by 3.46%. The comparison of target detection model results showed that the mAP of our method was 32.49%, 17.17%, and 5.96% higher than that of SSD, Faster R-CNN, and YOLOv5, respectively. The average image processing time was 0.023 s. Our study indicates that using blood vessel images and deep learning has great potential applications in the sex identification of chicken embryos.
Insecticide Monitoring in Cattle Dip with an E-Nose System and Room Temperature Screen-Printed ZnO Gas Sensors
Authors: Rohde Archibald W. , Nel Jacqueline M. , Joubert Trudi-Heleen
Published: July 2023

Taktic, an Amitraz-based insecticide, is commonly used in sub-Saharan Africa to treat cattle for ticks. Due to misuse in rural dipping pools, some ticks are showing resistance to Taktic. This work presents a low-cost e-nose with commercial sensors to monitor Taktic levels in dipping pool water. The device shows distinctly different measurements for the odours of air, distilled water, farm water, and four levels of Taktic insecticide in farm water. A naive Bayes algorithm with a Gaussian distribution is trained on the data and a validation set achieves a 96.5% accuracy. This work also compares two sol-gel ZnO nanoparticle solutions with an off-the-shelf ZnO nanoparticle ink for use as active material in chemiresistive gas sensors to be employed in an e-nose array. The ZnO solutions are screen-printed onto gold electrodes, auto-sintered with a built in heater, and used with UV illumination to operate as low-power, room temperature gas sensors. All of the screen-printed ZnO sensors show distinct changes in resistance when exposed to Taktic vapours under room temperature and humidity conditions. The custom room temperature ZnO gas sensors fabricated via facile and low-cost processes are suitable for future integration in a point-of-need microsystem for the detection of Taktic in water.
Intrinsic and Extrinsic Factors Affecting Neutral Detergent Fiber (NDF) Digestibility of Vegetative Tissues in Corn for Silage
Authors: Ferreira Gonzalo , Thomas Sarah E. , Teets Christy L. , Corl Benjamin A.
Published: July 2023

Dairy farming requires forages with high neutral detergent fiber (NDF) to maximize milk production, sustain cows’ health, and ensure the economic and environmental sustainability of the dairy farm. The objectives of this study were to determine the effects of the brown midrib (BMR) genotype, agronomic environment, and maturity at harvest on the NDF digestibility (NDFD) and the composition of the cell wall of corn plant tissues. In this plot study, one conventional and one BMR corn hybrid were planted and subjected to an abundant (60,000 seeds/ha and 225 kg N/ha) and a limited (90,000 seeds/ha and 180 kg N/ha) environment. The ruminal NDFD was determined in vitro in leaf blades, leaf sheaths, and stem internodes. Cell walls from BMR corn had greater NDFD than cell walls from conventional corn on most tissues. Relative to the abundant environment, the limited environment had minimal effects on NDFD. As maturity advanced, NDFD decreased for various but not all tissues. In conclusion, under the conditions of this study, intrinsic characteristics of corn, such as genotype and maturity at harvest, had a greater effect on NDFD than environment or agronomic management.
Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review
Authors: Qi Fei , Zhao Xuedong , Shi Zhengxiang , Li Hao , Zhao Wanying
Published: July 2023

The environment in livestock and poultry houses plays an important role in the growth and reproduction of livestock and poultry. In order to obtain the environmental conditions of livestock and poultry houses in a timely and reliable manner, and eliminate adverse environmental factors, scholars have been exploring various methods to obtain and predict environmental factors. This paper reviewed the literature from the last 10 years, specifically focusing on technologies for detecting environmental factors in livestock and poultry houses, which can be mainly divided into three categories: research on the environmental monitoring and control of livestock and poultry houses based on detection equipment and wireless sensor technology; research on the distribution and regularity of environmental factors in livestock and poultry houses based on a mathematical model; research on the environmental simulation and detection of livestock and poultry houses based on computer technology. The current testing methods have their advantages and disadvantages. When studying environmental factors, researchers should choose the most appropriate method for data acquisition according to the actual situation. The proposed recommendations for achieving this goal are as follows: (1) The control of environmental factors should be combined with the physiological response of livestock and poultry. The needs of animals should be considered; (2) Novel approaches need to be developed to integrate energy requirements into the environmental regulation of livestock and poultry houses; (3) It is necessary to research and develop control models and strategies that can predict the environment in the houses, and the transient simulation method should be further explored; (4) Improve environmental detection and control accuracy through the coupling of different technologies.
Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model
Authors: Zhan Baishao , Li Peng , Li Ming , Luo Wei , Zhang Hailiang
Published: July 2023

The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears.
Analysis of the Effect of the Interaction of Genotype and Environment on the Yield Stability of Maize Varieties; Genetic Resources for Breeding
Authors: do Couto Diego Pereira , Oliveira Wagner Bastos dos Santos , de Oliveira Jéssika Santos , Guilhen José Henrique Soler , Bernardes Carolina de Oliveira , Posse Sheila Cristina Prucoli , Ferreira Marcia Flores da Silva , Ferreira Adésio
Published: July 2023

Maize (Zea mays L.) is widely cultivated worldwide and in Brazil under different production systems and technology levels. However, the interaction with different environments may hinder an accurate indication of maize genotypes. The effects of the genotype × environment (G × E) interaction and the stability parameters must be considered to indicate responsive genotypes for the different cultivation regions. Thirteen maize varieties, including nine traditional (farmer-led selection and adaptation) and four commercial (bred and produced for specific markets through formal breeding programs) varieties, were evaluated in nine environments in Espírito Santo, Brazil, to study G × E interaction, identify adaptable and stable materials, and explore variability through genetic resources, using a randomized block design with three replications. Adaptability and stability parameters were tested using five different methodologies. The variety Aliança approached the level considered as broad adaptability. Environments 5, 6, 7, and 9 were classified as unfavorable. The varieties Alfredo Chaves, Catete and Catetim showed good stability indexes, with an average productivity of 5870.36, 5259.27 and 4914.20 kg/ha, respectively. Our findings will allow the exploration of the variability and genetic resources of some important materials, providing potential for gains in genetic breeding.
Improving Energy Efficiency and Greenhouse Gas Emissions in Small Farm Wheat Production Scenarios Using Data Envelopment Analysis
Authors: Sayed Hassan A. A. , Ding Qishuo , Hendy Zeinab M. , Alele Joseph O. , Al-Mashhadany Osamah H. , Abdelhamid Mahmoud A.
Published: July 2023

Assessing the energy cycle and greenhouse gas (GHG) emissions of wheat production in small Egyptian farms is essential to improve wheat productivity to meet population growth and achieve sustainable development. This study aims to compare wheat production in terms of energy use and GHG emissions for different scenarios in the Delta of Egypt and to use Data Envelopment Analysis (DEA) to optimize the wheat production system. Three common scenarios of the wheat production system (S-I, S-II, and S-III) from old lands with one scenario (S-IV) from newly reclaimed land were included in the study. Data were collected from small farmers through a face-to-face questionnaire and interviews in 2022–2023. The results showed that the third scenario (S-III) in the old lands had the lowest input energy consumption (42,555 MJ ha−1) and the highest output energy (160,418 MJ ha−1), with an energy use efficiency of 3.770. In comparison, the input and output energy for the newly reclaimed scenario (S-IV) were 37,575 and 130,581 MJ ha−1, respectively, with an energy use efficiency of 3.475. S-III was an optimum scenario due to its high energy indicators, such as energy productivity of 0.173 kg MJ−1. The total GHG emissions of S-III were the lowest in old lands with a value of 1432.9 kg CO2-eq ha−1, while S-IV had 1290.2 kg CO2-eq ha−1. The highest GHG emissions input was diesel fuel for machinery and irrigation, followed by manure, chemical fertilizers, and agricultural machinery use. Using mechanization in most farming operations for S-III and S-IV led to decreased losses of agricultural inputs with increasing outputs (yield and straw). Therefore, using them in wheat farming practices is recommended to increase the wheat farming system’s energy efficiency and GHG emissions.
Improving Endogenous Nitric Oxide Enhances Cadmium Tolerance in Rice through Modulation of Cadmium Accumulation and Antioxidant Capacity
Authors: Cai Wei , Wang Wenshu , Deng Hui , Chen Bin , Zhang Guo , Wang Ping , Yuan Tingting , Zhu Yongsheng
Published: July 2023

Nitric oxide (NO) plays an important role in plant stress responses. However, the mechanisms underlying NO-induced stress resistance to cadmium (Cd) stress in rice remain elusive. In this study, rat neuron NO synthase (nNOS)-overexpressing rice plants with higher endogenous NO level showed higher cadmium stress tolerance than the wild-type plants. The results showed that nNOS-overexpressing rice plants accumulated less cadmium in the roots and shoots by downregulating the expression of Cd uptake and transport related genes including OsCAL1, OsIRT2, OsNramp5, and OsCd1. Moreover, nNOS-overexpressing rice plants accumulated less hydrogen peroxide (H2O2), accompanying with higher expression of antioxidant enzyme genes (OsCATA, OsCATB, and OsPOX1) and corresponding higher enzyme activities under cadmium stress. Furthermore, the transcription of melatonin biosynthetic genes, including OsASMT1, OsTDC1, OsTDC3, and OsSNAT2, was also upregulated in nNOS-overexpressing plants, resulting in increased content of melatonin under cadmium treatment compared with the wild-type controls. Taken together, this study indicates that nNOS overexpression improves Cd tolerance of rice seedlings through decreasing cadmium accumulation and enhancing the antioxidant capacity and melatonin biosynthesis of the plants.
Market Price and Supply of Farmland as a Means of Supporting the Wider Development of Sustainable Agricultural Production in Czechia (a Case Study)
Authors: Mach Jiří , Dauhiniova Tatsiana , Kopecká Lenka , Prášilová Marie , Štěbeták Michal , Severová Lucie , Rodonaia Elizbar , Svoboda Roman , Cvik Eva Daniela , Paták Milan
Published: July 2023

The goal of this article is to predict the further development of the market price of farmland in Czechia. As part of this goal, the environmental and socio-economic factors affecting the quantity and quality of farmland and, subsequently, the amount of its market price will be discussed. In addition to the quality of the farmland, the market price of individual plots of land is influenced by a number of other factors, such as the size and shape of the plot, location, erosion risk, the concluded usufructuary lease agreements, competition from buyers in the vicinity, and other factors. The average market price of farmland in Czechia was 29.4 CZK/m2 in 2021, which represents an increase of 16.1% against the amount of 25.3 CZK/m2 in 2020. According to the results of the statistical analysis, it is possible to assume a constant increase in the prices of farmland precisely because of the ever-decreasing area of land intended for agricultural production. Another factor for the increase in prices can be increased inflation. In 2027, market prices for farmland could reach up to 40 CZK per m2. Nevertheless, Czechia is among the countries with the lowest market price of farmland; the price is even lower than in neighboring Poland or Spain. This also contributes to the fact that the market prices of farmland in Czechia are increasing at a relatively fast pace every year.
Two-Stage Revision Arthroplasty for Resistant Gram-Positive Periprosthetic Joint Infections Using an Oral Linezolid-Based Antibiotic Regime
Authors: Gründer Lars , Bockholt Sebastian , Gosheger Georg , Schulze Martin , Schwarze Jan , Pützler Jan , Moellenbeck Burkhard , Theil Christoph
Published: July 2023

Background: Increasing antibiotic resistance has been reported as an issue in the systemic treatment of periprosthetic joint infection (PJI). Linezolid offers the advantages of high oral bioavailability and little resistance; however, efficacy in the treatment of PJI varies considerably, and studies reporting consistent surgical treatment are scarce. Methods: This is a retrospective, single-center analysis of two-stage revisions performed between 2008 and 2017. We identified 111 patients who met the inclusion criteria. Oral linezolid was given for 28 days following 14 days of intravenous tailored antibiotics in resistant gram-positive PJI. A total of 64% of the patients had methicillin-resistant coagulase-negative staphylococci. The median follow-up was 43 (interquartile range (IQR) 30–57) months. Results: 22% (24/111) of the patients underwent surgery for subsequent infection. The 5-year infection-free survival probability was 77% (95% confidence interval (CI) 69–85). A total of 5% of the patients (6/111) had the same organism at the time of reinfection. The patients with infections caused by other organisms than Coagulase-negative staphylococci tended to have a worse reinfection-free survivorship at five years (70% vs. 81%, p = 0.09). Furthermore, the patients with obesity tended to have reduced reinfection-free survivorship at five years (69% vs. 84%, p = 0.08). Overall, 5% (6/111) of the patients had blood count abnormalities with no treatment discontinuations. Conclusion: Two-stage revision arthroplasty with systemic oral linezolid treatment for resistant gram-positive PJI results in an infection control of 77% at the mid-term.
Development and Characterization of Microparticles with Actinidia arguta Leaves Extract by Spray-Drying: A New Mind-Set Regarding Healthy Compounds for Oral Mucositis
Authors: Teixeira Filipa , Silva Ana Margarida , Sut Stefania , Dall’Acqua Stefano , Delerue-Matos Cristina , Estevinho Berta , Costa Paulo C. , Rodrigues Francisca
Published: July 2023

Actinidia arguta leaves have gained notoriety over the past years due to their rich bioactive composition with human pro-healthy effects, particularly in relation to antioxidants. Nevertheless, antioxidants are well known for their chemical instability, making it necessary to develop suitable delivery systems, such as microparticles, to provide protection and ensure a controlled release. The aim of this work was to produce polymeric particles of A. arguta leaves extract by spray-drying that may improve the oral mucositis condition. Microparticles were characterized by size, shape, antioxidant/antiradical activities, swelling capacity, moisture content, and effect on oral cells (TR146 and HSC-3) viability, with the aim to assess their potential application in this oral condition. The results attested the microparticles’ spherical morphology and production yields of 41.43% and 36.40%, respectively, for empty and A. arguta leaves extract microparticles. The A. arguta leaves extract microparticles obtained the highest phenolic content (19.29 mg GAE/g) and antioxidant/antiradical activities (FRAP = 81.72 µmol FSE/g; DPPH = 4.90 mg TE/g), being perceived as an increase in moisture content and swelling capacity. No differences were observed between empty and loaded microparticles through FTIR analysis. Furthermore, the exposure to HSC-3 and TR146 did not lead to a viability decrease, attesting their safety for oral administration. Overall, these results highlight the significant potential of A. arguta leaves extract microparticles for applications in the pharmaceutical and nutraceutical industries.
Chitosan Oligosaccharides Alleviate Heat-Stress-Induced Lipid Metabolism Disorders by Suppressing the Oxidative Stress and Inflammatory Response in the Liver of Broilers
Authors: Lan Ruixia , Luo Huiwen , Wu Fan , Wang Yuchen , Zhao Zhihui
Published: July 2023

Heat stress has been reported to induce hepatic oxidative stress and alter lipid metabolism and fat deposition in broilers. Chitosan oligosaccharides (COSs), a natural oligosaccharide, has anti-oxidant, anti-inflammatory, and lipid-lowering effects. This study is conducted to evaluate dietary COS supplementation on hepatic anti-oxidant capacity, inflammatory response, and lipid metabolism in heat-stressed broilers. The results indicate that heat-stress-induced poor (p < 0.05) growth performance and higher (p < 0.05) abdominal adiposity are alleviated by COS supplementation. Heat stress increases (p < 0.05) serum AST and ATL activity, serum and liver MDA, TG, TC, and LDL-C levels, and the expression of hepatic IL-1β, IL-6, SREBP-1c, ACC, and FAS, while it decreases (p < 0.05) serum SOD and CAT activity, liver GSH-Px and SOD activity, and the expression of hepatic Nrf2, GPX1, IL-10, MTTP, PPARα, and CPT1. Nevertheless, COS supplementation decreases (p < 0.05) serum AST and ATL activity, serum and liver MDA, TG, TC, and LDL-C levels, and the expression of hepatic IL-1β, IL-6, SREBP-1c, ACC, and FAS, while it increases (p < 0.05) serum SOD and CAT activity, liver GSH-Px activity, and the expression of hepatic Nrf2, CAT, IL-10, LPL, MTTP, PPARα, and CPT1. In conclusion, COS could alleviate heat-stress-induced lipid metabolism disorders by enhancing hepatic anti-oxidant and anti-inflammatory capacity.
Abnormal Lipoproteins Trigger Oxidative Stress-Mediated Apoptosis of Renal Cells in LCAT Deficiency
Authors: Gomaraschi Monica , Turri Marta , Strazzella Arianna , Lhomme Marie , Pavanello Chiara , Le Goff Wilfried , Kontush Anatol , Calabresi Laura , Ossoli Alice
Published: July 2023

Familial lecithin:cholesterol acyltransferase (LCAT) deficiency (FLD) is a rare genetic disease caused by the loss of function mutations in the LCAT gene. LCAT deficiency is characterized by an abnormal lipoprotein profile with severe reduction in plasma levels of high-density lipoprotein (HDL) cholesterol and the accumulation of lipoprotein X (LpX). Renal failure is the major cause of morbidity and mortality in FLD patients; the pathogenesis of renal disease is only partly understood, but abnormalities in the lipoprotein profile could play a role in disease onset and progression. Serum and lipoprotein fractions from LCAT deficient carriers and controls were tested for renal toxicity on podocytes and tubular cells, and the underlying mechanisms were investigated at the cellular level. Both LpX and HDL from LCAT-deficient carriers triggered oxidative stress in renal cells, which culminated in cell apoptosis. These effects are partly explained by lipoprotein enrichment in unesterified cholesterol and ceramides, especially in the HDL fraction. Thus, alterations in lipoprotein composition could explain some of the nephrotoxic effects of LCAT deficient lipoproteins on podocytes and tubular cells.
A Toxic Style Transfer Method Based on the Delete–Retrieve–Generate Framework Exploiting Toxic Lexicon Semantic Similarity
Authors: Iglesias Martín , Araque Oscar , Iglesias Carlos Á.
Published: July 2023

Whether consciously or inadvertently, our messages can include toxic language which contributes to the polarization of social networks. Intelligent techniques can help us detect these expressions and even change them into kinder expressions by applying style transfer techniques. This work aims to advance detoxification style transfer techniques using deep learning and semantic similarity technologies. The article explores the advantages of a toxicity-deletion method that uses linguistic resources in a detoxification system. For this purpose, we propose a method that removes toxic words from the source sentence using a similarity function with a toxic vocabulary. We present two models that leverage it, namely, LexiconGST and MultiLexiconGST, which are based on the Delete –Retrieve–Generate framework. Experimental results show that our models perform well in the detoxification task compared to other state-of-the-art methods. Finally, this research confirms that linguistic resources can guide deep learning techniques and improve their performance.
Current Knowledge of the Melliferous Florae in Mexico Using Methodologies to Understand Bee–Plant–Human Interactions
Authors: Alcivar-Saldaña Jose Juan , Rodriguez-Monroy Marco Aurelio , Canales-Martinez Maria Margarita
Published: July 2023

Knowledge of melliferous flora has applications in areas of commercial and scientific interest because pollinators, such as different bee taxa, provide ecosystem services that include the pollination of both agroecosystems and wild ecosystems. In this manuscript, we reviewed research carried out in Mexico on the identification of melliferous flora, emphasizing the methods used and highlighting their implications for bee–plant–human interactions as well as the advantages and disadvantages of the different methods used in the determination of this type of vegetation. Methods such as observations of the foraging process, palynological analysis, and taxonomic determination via genetic sequencing were analyzed. We observed advantages in each method, but better integration between methods would facilitate concrete advances in understanding this type of flora. Ultimately, the determination of melliferous flora is a practical tool that provides useful knowledge in diverse economic and scientific sectors.
Potential of Supercritical Acrocomia aculeata Oil and Its Technology Trends
Authors: Aires Giselle Cristine Melo , de Carvalho Junior Raul Nunes
Published: July 2023

This study discusses the bioactive composition, supercritical extraction, and biological activity of Acrocomia aculeata in publications in the last ten years. Numerous compounds have been identified in A. aculeata, which include fatty acids, carotenoids, phenolic compounds, and tocopherols, discussed in this article. Although there are several studies with the fruit using conventional extraction to obtain oil, there are only a few involving extraction at high pressures. Therefore, this article emphasized the potential of extraction with supercritical fluid (SFC) to obtain oil due to its good selectivity, fractions distributed in terms of mass yield, and chemical composition of the obtained extracts, which provides a solvent-free product, making it safe for application in the food industry. The biological activity of A. aculeata extracts was also discussed, including antidiabetic, anti-inflammatory, diuretic, antimicrobial, antioxidant, neuroprotective, and photoprotective effects, which can produce effects on human health. This review produces important results that can act as a basis for future studies related to obtaining bioactive compounds from A. aculeata with a high degree of purity and good quality in its applications.
A Method for Correcting Endoscopic Images to Measure the Size of Defects on the Inner Surface of a Hole
Authors: Shi Pengtao , Tian Zhengwei , Sheng Qiang , Liu Peng
Published: July 2023

A method is proposed for correcting endoscopic images to enable the measurement of inner surface defects in holes. The distortion is decomposed into circumferential and axial components based on imaging principles, and geometric constraints are added to simplify the correction model parameters to a central coordinate and nonlinear parameter, improving accuracy at the edge of endoscopic images. Experimental results demonstrate good universality with an average error rate of 5.35% in defect measurements, making it applicable for automatic and intelligent detection of hole parts and pipelines.
Design and Control of a Linear Rotary Electro-Hydraulic Servo Drive Unit
Authors: Milecki Andrzej , Jakubowski Arkadiusz , Kubacki Arkadiusz
Published: July 2023

In this paper, a new solution for an electro-hydraulic servo drive is proposed, which consists of two electro-hydraulic servo drives: one with a hydraulic cylinder and one with a hydraulic rotary motor. In the proposed drive, the linear actuator is attached to a horizontal base and the hydraulic motor is mounted on the actuator piston rod. Thus, the output signal of the drive is the lifting and lowering of the element suspended on the rope. The paper describes the structure, kinematics, dynamics, and control of a novel electro-hydraulic servo drive. A servo valve and a proportional valve are used to control the flow of the hydraulic cylinder and the hydraulic motor. Special attention is paid to the construction of two actuators in one drive unit. The controller is based on the PLC controller. The measuring system uses laser displacement sensors and an encoder. The results of laboratory investigations are discussed in the paper. The proposed drive contains all of the characteristics of a mechatronic device. The main contribution of this study is the proposal of the controller architecture and the algorithm to control the speed and position when lifting or lowering loads.
An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems
Authors: Darwish Saad M. , Ali Raad A. , Elzoghabi Adel A.
Published: July 2023

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.
Determination of the Reverberation Time Using the Measurement of Sound Decay Curves
Authors: Nowoświat Artur
Published: July 2023

In the measurements of reverberation time, measurement methods of different accuracy are used depending on the room. For ordinary rooms, the measurements are made using the interrupted noise method, which consists of determining the decay curve after switching off the excitation source of the room. The measurements are made for different source arrangements and different receiver arrangements, and at least three repetitions are made at each of such points. Due to such a realization of measurements, several dozen different reverberation curves are obtained, from which the reverberation time is read out. This article demonstrates the differences between reverberation time readouts, depending on the averaging method of reverberation curves. The first analyzed method is based on reading out the reverberation times for each obtained curve and on averaging the results obtained in this way. The second analyzed method involves averaging the reverberation curves using the linear regression method and then determining a simple regression on the basis of which the reverberation time is read out. For each method, different average reverberation time values and different standard uncertainties were obtained. The difference for the 500 Hz frequency band in a teaching room for the measurement uncertainty is 0.28 s. The results obtained in the article are extremely important when designing interiors intended for the reception of verbal sound, in particular teaching rooms.
A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process
Authors: Wu Jin , Zhu Yaqiao , Wang Chunguang , Li Jinfu , Zhu Xuehong
Published: July 2023

Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.
Deformation Control Technology of Gob-Side Entry Retaining with Large Volume CFST Roadway Side Support in Top-Coal Caving Longwall and Stability Analysis: A Case Study
Authors: Liu Zi-Lu , Ma Zhan-Guo , Kazanin Oleg Ivanovich , Gong Peng , Li Ye , Ni Xiao-Yan
Published: July 2023

Considering the factors affecting the surrounding rock stability of gob-side entry retaining, the applicability of a large-diameter, concrete-filled steel tube roadside support body in a top-coal caving fully mechanized face is discussed, and a new approach to gob-side entry retaining is proposed in this study. The mechanical model of the surrounding rock structure of gob-side entry retaining in a top-coal caving fully mechanized face was established, the critical state of column–roof contact shear slip instability was clarified through Prandtl foundation failure theory, and the deformation mechanism of the surrounding rock of the retained roadway was analyzed through numerical simulation. The results indicated that the range of the tensile stress zone and extreme tensile stress of the roof between columns are closely related to the spacing of columns, which is the key factor influencing the deformation of the retained roadway. In addition, besides uncontrollable factors, the stability of the contact interface between the roof and columns is directly related to the area of the contact interface between the concrete-filled steel tubes and the roof, and the size of the critical contact area is directly related to the properties of top-coal mass. Finally, a field test was carried out in 91–101 working panels in the Wang-Zhuang Coal Mine; the maximum convergence of the roof and floor was 510 mm, and the area of the retained roadway section reached 12.9 m2, which is within a reasonable range.
High Reliable Uplink Transmission Methods in GEO–LEO Heterogeneous Satellite Network
Authors: Li Guoyan , Li Tian , Yue Xinwei , Hou Tianwei , Dai Bin
Published: July 2023

As a significant component of non-terrestrial networks, satellite communication has gradually evolved into a heterogeneous topology where the network nodes are classified as low Earth orbit (LEO) and geostationary Earth orbit (GEO) nodes. Considering the advantage of low transmission delay of LEO satellites in a heterogeneous network, ground users are arranged to access LEO satellites in the first priority. By fully understanding the equivalent channel gain difference in the beam-edge and beam-center users, we develop non-orthogonal multiple access (NOMA) in an LEO network where different users are allocated in the same resource block. As a complementary strategy, the beam-edge user has the ability to connect the GEO satellite if the LEO links are not available. That is, the two users are served cooperatively by LEO and GEO satellites in an orthogonal manner. In this paper, we provide deep insights for the uplink transmission performance of NOMA and cooperative orthogonal multiple access methods. As a general metric, the analytical expressions of outage probability are derived and the diversity orders are also provided. The simulation results show that NOMA is capable of providing remarkable performance in low signal-to-noise ratio regions and is more promising when the channels are assumed to be ordered.
Numerical Simulation of Fluid-Structure Coupling for a Multi-Blade Vertical-Axis Wind Turbine
Authors: Zhang Xiao , Zheng Maosheng
Published: July 2023

The aerodynamic characteristics of the vertical-axis wind turbine with three, four, five, and six blades are studied numerically. A coupling model of fluid flow and solid turbine blade is established to model the interactions between air and wind turbine. The pressure distribution and blade deformation affected by air are obtained and discussed. For the four wind turbines with different numbers of blades, the maximum pressure in the entire machine structure occurs at the variable angle position of the blades in the windward region under the same wind speed. Mainly due to the rapid airflow variation, complex turbulence, and significant influence of the wind field on the blades in this position, this part of the blades is prone to bending or damage. Under identical external wind field conditions, wind turbines with four and six blades exhibit significantly higher equivalent pressures on their surfaces compared to those with five and three blades. The maximum equivalent pressure of six blades can reach 3.161 × 106 Pa. The maximum deformation of the blade basically occurs at the tip and four sides of the blade. The six-blade wind turbines withstand higher and non-uniform surface pressures on their blades, resulting in the largest deformation of up to 11.658 mm. On the other hand, the four-blade wind turbine exhibits the smallest deformation. The above conclusions provide theoretical guidance for the design and optimization of vertical-axis wind turbines.
Sensitivity of Modern Deep Learning Neural Networks to Unbalanced Datasets in Multiclass Classification Problems
Authors: Barulina Marina , Okunkov Sergey , Ulitin Ivan , Sanbaev Askhat
Published: July 2023

One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where the last layers of the neural network are retrained. Therefore, large datasets with highly unbalanced classes are not good for models’ training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper, the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2–6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes’ classification also improved significantly–the True Positive value can be increased by 0.3 or more. As a result, the best approach to train considered networks on an initially unbalanced dataset was formulated.
Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
Authors: Kong Yating , Li Jide , Hu Liangpeng , Li Xiaoqiang
Published: July 2023

Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This paper first proposes a semi-supervised deep learning matting algorithm based on semantic consistency of trimaps (Tri-SSL), which uses trimaps to provide weakly supervised signals for the unlabeled data, to reduce the labeling cost. Tri-SSL is a single-stage semi-supervised algorithm that consists of a supervised branch and a weakly supervised branch that share the same network in one iteration during training. The supervised branch is consistent with standard supervised matting methods. In the weakly supervised branch, trimaps of different granularities are used as weakly supervised signals for unlabeled images, and the two trimaps are naturally perturbed samples. Orientation consistency constraints are imposed on the prediction results of trimaps of different granuliarty and the intermediate features of the network. Experimental results show that Tri-SSL improves model performance by effectively utilizing unlabeled data.
Landslide Susceptibility Using Climatic–Environmental Factors Using the Weight-of-Evidence Method—A Study Area in Central Italy
Authors: Gentilucci Matteo , Pelagagge Niccolò , Rossi Alessandro , Domenico Aringoli , Pambianchi Gilberto
Published: July 2023

The Italian territory is subject to a high level of hydrogeological instability that periodically results in the loss of lives, buildings and productive activities. Therefore, the recognition of areas susceptible to hydrogeological instability is the basis for preparing countermeasures. In this context, landslide susceptibility in the mid-Adriatic slope was analyzed using a statistical method, the weight of evidence (WoE), which uses information from several independent sources to provide sufficient evidence to predict possible system developments. Only flows, slides, debris flows and mud flows were considered, with a total of 14,927 landslides obtained from the IFFI (Inventory of Franous Phenomena in Italy) database. Seven climatic–environmental factors were used for mapping landslide susceptibility in the study area: slope, aspect, extreme precipitation, normalized difference vegetation index (NDVI), CORINE land cover (CLC), and topographic wetness index (TWI). The introduction of these factors into the model resulted in rasters that allowed calculation by GIS-type software of a susceptibility map. The result was validated by the ROC curve method, using a group of landslides, equal to 20% of the total, not used in the modeling. The performance of the model, i.e., the ability to predict the presence or absence of a landslide movement correctly, was 0.75, indicating a moderately accurate model, which nevertheless appears innovative for two reasons: the first is that it analyzes an inhomogeneous area of more than 9000 km2, which is very large compared to similar analyses, and the second reason is the causal factors used, which have high weights for some classes despite the heterogeneity of the area. This research has enabled the simultaneous introduction of unconventional factors for landslide susceptibility analysis, which, however, could be successfully used at larger scales in the future.
Nb3Sn Cavities Coated by Tin Vapor Diffusion Method at Peking University
Authors: Wang Gai , Quan Shengwen , Lin Lin , Ren Manqian , Hao Jiankui , Wang Fang , Jiao Fei , Zhu Feng , Huang Senlin , Yan Xueqing , Zhu Kun
Published: July 2023

Nb3Sn-coating experiments on samples and single-cell cavities were conducted at Peking University (PKU) to understand the Nb3Sn growth process using the vapor diffusion method. The evaporation of tin and tin chloride used in the vapor diffusion process was simulated and experimentally analyzed. The results show that the nucleation process is generally uniform, and the atomic ratios of Nb/O and Sn/O were found to be 1:2 within the 10 nm surface of the nucleated samples. Three tin sources were distributed along the cavity axis to obtain a uniform grain size on the cavity surface, and a surface tin content of 25~26% was achieved. The tin segregation effect was found in long-time coatings or coatings with insufficient tin, resulting in a low Sn% and bad cavity performance. By overcoming the tin segregation problem, a Nb3Sn cavity with a 750 nm grain size was produced by 1200 °C coating for 80 min and 1150 °C annealing for 60 min. The acceleration gradient reached 17.3 MV/m without quenching and an obvious Q-slope at 4.2 K. The relationship between coating recipes and vertical test results is discussed and conclusive advice is provided in this paper.
An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM
Authors: Mutabazi Emmanuel , Ni Jianjun , Tang Guangyi , Cao Weidong
Published: July 2023

Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions.