bluuebunny/crossref_metadata_2025 huggingface dataset:
Forecasting Pesticide Use on Golf Courses by Integration of Deep Learning and Decision Tree Techniques
Authors: Grégoire Guillaume , Fortin Josée , Ebtehaj Isa , Bonakdari Hossein
Published: May 2023

In the current study, a new hybrid machine learning (ML)-based model was developed by integrating a convolution neural network (CNN) with a random forest (RF) to forecast pesticide use on golf courses in Québec, Canada. Three main groups of independent variables were used to estimate pesticide use on golf courses, expressed as actual active ingredient rate (AAIR): (i) coordinates (i.e., longitude and latitude of the golf course), (ii) characteristics of the golf courses (i.e., pesticide type and the number of holes), and (iii) meteorological variables (i.e., total precipitation, P, and average temperature, T). The meteorological variables were collected from the Google Earth Engine by developing a JavaScript-based Code. On the basis of the different periods of total precipitation and average temperature, four different scenarios were defined. A data bank with more than 40,000 samples was used to calibrate and validate the developed model such that 70% of all samples were randomly selected to calibrate the model, while the remainder of the samples (i.e., 30%) that did not have any role in calibration were employed to validate the model’s generalizability. A comparison of different scenarios indicated that the model that considered the longitude and latitude of the golf course, pesticide type, and the number of holes in golf courses as well as total precipitation and average temperature from May to November as inputs (R = 0.997; NSE = 0.997; RMSE = 0.046; MAE = 0.026; NRMSE = 0.454; and PBIAS (%) = −0.443) outperformed the other models. Moreover, the sensitivity analysis result indicated that the total precipitation was the most critical variable in AAIR forecasting, while the average temperature, pesticide types, and the number of holes were ranked second to fourth, respectively.
Effects of Organic Farming on the Physicochemical, Functional, and Quality Properties of Pomegranate Fruit: A Review
Authors: Andreu-Coll Lucía , Cano-Lamadrid Marina , Lipan Leontina , López-Lluch David , Sendra Esther , Hernández Francisca
Published: May 2023

In this review, a selection of studies was carried out that evaluated the influence of organic agronomic practices on the cultivation of pomegranate; specifically, the influence of these practices on improving soil health and pomegranate fruit quality. Studies were selected (n = 39) in which organic treatments were applied to different cultivars of pomegranate and which evaluated fruit and soil quality parameters. These studies showed that exclusively organic manure and organic manure in combination with mineral fertilizers are suitable to fulfill the requirements of pomegranate crop and reduce the amount of mineral fertilizers. Moreover, the soil health improved with organic manures, as well as growth, and yield in the pomegranate crop. Pomegranate fruits grown under organic conditions showed high levels of fruit quality parameters, such as total soluble solids and fruit juice, and additionally presented high concentrations of bioactive compounds such as anthocyanins and total phenols. Data from these studies endorsed the fact that pomegranates cultured under organic conditions may have a better fruit quality and nutraceutical content than those grown under conventional conditions, although more scientific evidence is required to confirm this.
Real-Time Detection of Crops with Dense Planting Using Deep Learning at Seedling Stage
Authors: Kong Shuolin , Li Jian , Zhai Yuting , Gao Zhiyuan , Zhou Yang , Xu Yanlei
Published: May 2023

Crop seedlings are similar in appearance to weeds, making crop detection extremely difficult. To solve the problem of detecting crop seedlings in complex field environments, a seedling dataset with four crops was constructed in this study. The single leaf labeling method was proposed as an alternative to conventional labeling approaches to improve the detection accuracy for dense planting crops. Second, a seedling detection network based on YOLOv5 and a transformer mechanism was proposed, and the effects of three features (query, key and value) in the transformer mechanism on the detection accuracy were explored in detail. Finally, the seedling detection network was optimized into a lightweight network. The experimental results show that application of the single leaf labeling method could improve the mAP0.5 of the model by 1.2% and effectively solve the problem of missed detection. By adding the transformer mechanism module, the mAP0.5 was improved by 1.5%, enhancing the detection capability of the model for dense and obscured targets. In the end, this study found that query features had the least impact on the transformer mechanism, and the optimized model improved the computation speed by 23 ms·frame−1 on the intelligent computing platform Jetson TX2, providing a theoretical basis and technical support for real-time seedling management.
Projection of Rainfed Rice Yield Using CMIP6 in the Lower Lancang–Mekong River Basin
Authors: Xie Shimeng , Liu Hui , Liu Dengfeng , Hu Hongchang , Dong Zhiqiang , Wang Tianci , Ming Guanghui
Published: May 2023

Climate change has had a strong impact on grain production in the Lower Lancang–Mekong River Basin (LMB). Studies have explored the response of LMB rice yield to climate change, but most of them were based on climate projection data before CMIP6 (Coupled Model Intercomparison Project Phase 6). Based on the latest CMIP6 climate projection data and considering three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), this study used the crop growth model (AquaCrop) to simulate and project the LMB rice yield and analyzed the correlation between the yield and the temperature and precipitation during the growth period. The results show that the output of rice yield will increase in the future, with greater yield increases in the SSP5-8.5 scenario (about 35%) than in the SSP2-4.5 (about 15.8%) and SSP1-2.6 (about 9.3%) scenarios. The average temperature of the rice growth period will increase by 1.6 °C, 2.4 °C, and 3.7 °C under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The rice yield was predicted to have a significant positive response to the increase in temperature in the near future (2021–2060). In the far future (2061–2100), the rice yield will continue this positive response under the high-emission scenario (SSP5-8.5) with increasing temperature, while the rice yield under the low-emission scenario (SSP1-2.6) would be negatively correlated with the temperature. There will be a small increase in precipitation during the rice growth period of LMB in the future, but the impact of the precipitation on the rice yield is not obvious. The correlation between the two is not high, and the impact of the precipitation on the yield is more uncertain. This result is valuable for the management of the rice cultivation and irrigation system in the LMB, and it will help the government to adapt the impact of climate change on the rice production, which may contribute to the food security of the LMB under climate change.
Counting Crowded Soybean Pods Based on Deformable Attention Recursive Feature Pyramid
Authors: Xu Can , Lu Yinhao , Jiang Haiyan , Liu Sheng , Ma Yushi , Zhao Tuanjie
Published: May 2023

Counting the soybean pods automatically has been one of the key ways to realize intelligent soybean breeding in modern smart agriculture. However, the pod counting accuracy for whole soybean plants is still limited due to the crowding and uneven distribution of pods. In this paper, based on the VFNet detector, we propose a deformable attention recursive feature pyramid network for soybean pod counting (DARFP-SD), which aims to identify the number of soybean pods accurately. Specifically, to improve the feature quality, DARFP-SD first introduces the deformable convolutional networks (DCN) and attention recursive feature pyramid (ARFP) to reduce noise interference during feature learning. DARFP-SD further combines the Repulsion Loss to correct the error of predicted bboxse coming from the mutual interference between dense pods. DARFP-SD also designs a density prediction branch in the post-processing stage, which learns an adaptive soft distance IoU to assign suitable NMS threshold for different counting scenes with uneven soybean pod distributions. The model is trained on a dense soybean dataset with more than 5300 pods from three different shapes and two classes, which consists of a training set of 138 images, a validation set of 46 images and a test set of 46 images. Extensive experiments have verified the performance of proposed DARFP-SD. The final training loss is 1.281, and an average accuracy of 90.35%, an average recall of 85.59% and a F1 score of 87.90% can be achieved, outperforming the baseline method VFNet by 8.36%, 4.55% and 7.81%, respectively. We also validate the application effect for different numbers of soybean pods and differnt shapes of soybean. All the results show the effectiveness of the DARFP-SD, which can provide a new insight into the soybean pod counting task.
DNA-Based Molecular Markers and Antioxidant Properties to Study Genetic Diversity and Relationship Assessment in Blueberries
Authors: Debnath Samir C. , Bhatt Dhrumit , Goyali Juran C.
Published: May 2023

Blueberries (Vaccinium L. spp.) are an economically and medicinally important plant. Their antioxidant properties are well-known for their medicinal value in negating the harmful effects of free radicals. It is very important to develop genotypes that are high in health-promoting factors and economic value to meet present world needs. Estimation of genetic diversity using molecular markers, antioxidant properties, and their association can reveal genotypes with important characteristics and help in berry improvement programs. Wild blueberries are a better source of antioxidant metabolites compared to cultivated ones. Extensive variations are present in molecular and biochemical contents among wild clones and cultivars. The current review provides detailed and updated information on the economic and medicinal importance of blueberries, the application of molecular markers, and biochemical estimation in berry improvement and conservation, filling the gap in the literature.
Development of Specific Molecular and Phenotypic Marker-Based Haploid Inducers in Rice
Authors: Wang Jian , Yan Huijing , Jiao Xiaozhen , Ren Jun , Hu Fengyue , Liang Huan , Liang Weihong , Liu Chaolei
Published: May 2023

Doubled haploid (DH) technology is an efficient strategy for producing completely homozygous lines for breeding programs. Mutations in the MATRILINEAL (MTL) phospholipase trigger intraspecific haploid induction in cereals. Although an in vivo haploid induction system based on OsMTL-edited plants has been established in rice (Oryza sativa), DH technology is still limited by other factors, such as haploid identification, which is one of the essential steps required for DH technology. In the study, we addressed this technical challenge by integrating specific molecular and phenotypic markers into rice haploid inducers. We first generated large fragment insertion or deletion mutations within the OsMTL gene and designed a pair of primers flanking the mutational sites to be used as the specific and universal molecular markers between wild-type and Osmtl plants. Next, we screened for hairy leaf as a single dominant trait and integrated it into specific molecular marker-based haploid inducers using the cross and self-cross method. When crossing cytoplasmic male sterile lines with these haploid inducers, we utilized the specific InDel marker and hairy leaf phenotypic marker to identify putative haploids (or double haploids). These putative haploids were further confirmed through ploidy and phenotypic analysis, demonstrating the high efficiency of haploid identification using these markers. The haploid induction rate (HIR) of the developed specific molecular and phenotypic marker-based haploid inducers ranged from 3.7% to 12.5%. We have achieved successful integration of distinct molecular and phenotypic markers into rice haploid inducers. Our advanced marker-based system has significantly enhanced the accuracy of haploid identification, thereby expediting the adoption of DH technology in rice breeding.
Progesterone and Androstenedione Are Important Follicular Fluid Factors Regulating Porcine Oocyte Maturation Quality
Authors: Zhao Huaxing , He Xiaohua , Zhang Xianjun , Shi Junsong , Zhou Rong , Mai Ranbiao , Su Qiaoyun , Cai Gengyuan , Huang Sixiu , Xu Zheng , Wu Zhenfang , Li Zicong
Published: May 2023

Oocytes matured in vitro are useful for assisted human and farm animal reproduction. However, the quality of in vitro matured oocytes is usually lower than that of in vivo matured oocytes, possibly due to the absence of some important signal regulators in vitro. In this study, untargeted metabolomics was used to detect the changes in the metabolites in the follicular fluid (FF) during in vivo pig oocyte maturation and in the culture medium during in vitro maturation. Our results showed that the total metabolite changing profile of the in vivo FF was different from that of the in vitro maturation medium, but the levels of 23 differentially expressed metabolites (DEMs) changed by following the same trend during both in vivo and in vitro pig oocyte maturation. These 23 metabolites may be important regulators of porcine oocyte maturation. We found that progesterone and androstenedione, two factors in the ovarian steroidogenesis pathway enriched from the DEMs, were upregulated in the FF during in vivo pig oocyte maturation. The levels of these two factors were 31 and 20 fold, respectively, and they were higher in the FF than in the culture medium at the oocyte mature stage. The supplementation of progesterone and androstenedione during in vitro maturation significantly improved the pig oocyte maturation rate and subsequent embryo developmental competence. Our finding suggests that a metabolic abnormality during in vitro pig oocyte maturation affects the quality of the matured oocytes. This study identified some important metabolites that regulate oocyte maturation and their developmental potential, which will be helpful to improve assisted animal and human reproduction.
Eriocheir sinensis feminization-1c (Fem-1c) and Its Predicted miRNAs Involved in Sexual Development and Regulation
Authors: Zhu Dandan , Feng Tianyi , Mo Nan , Han Rui , Lu Wentao , Cui Zhaoxia
Published: May 2023

Feminization-1c (Fem-1c) is important for sex differentiation in the model organism Caenorhabditis elegans. In our previous study, the basic molecular characteristics of the Fem-1c gene (EsFem-1c) in Eriocheir sinensis (Henri Milne Edwards, 1854) were cloned to determine the relationship with sex differentiation. In this study, the genomic sequence of EsFem-1c contained five exons and four introns, with an exceptionally long 3′UTR sequence. The qRT-PCR results of EsFem-1c demonstrated lower tissue expression in the androgenic gland of the intersex crab than the normal male crab, implying that EsFem-1c plays a role in crab AG development. RNA interference experiments and morphological observations of juvenile and mature crabs indicated that EsFem-1c influences sexual development in E. sinensis. A dual-luciferase reporter assay disclosed that tcf-miR-315-5p effectively inhibits the translation of the EsFem-1c gene, influencing male development. An intriguing finding was that miRNA tcf-miR-307 could increase EsFem-1c expression by binding to the alternative splicing region with a length of 248 bp (ASR-248) in the 3′UTR sequence. The present research contributes to a better understanding of the molecular regulation mechanism of EsFem-1c and provides a resource for future studies of the miRNA-mediated regulation of sexual development and regulation in E. sinensis.
Bacterial Periodontitis in Horses: An Epidemiological Study in Southern Italy
Authors: Occhiogrosso Leonardo , Capozza Paolo , Buonavoglia Alessio , Decaro Nicola , Trotta Adriana , Marin Claudia , Corrente Marialaura
Published: May 2023

Equine periodontal disease (EPD) is a painful oral inflammatory syndrome characterized by multifactorial pathogenesis. Although it is well known that bacterial proliferation and consequent gingivitis are caused by the decomposition process of food residues, in hypsodont species, the pathogenetic role and the different bacterial species involved in the progression of EPD must be fully clarified. This study aimed to investigate the association of bacteria, including the complex red bacteria (RCB), with EPD, and to evaluate possible EPD risk factors. Bacterial species, including Treponema denticola, Tannerella forsythia, Porphyromonas gingivalis (belonging to the RCB), Fusobacterium nucleatum, Veilonella parvula, and Prevotella intermedia, were investigated in 125 oral swabs from healthy and EPD-affected horses using real-time multiplex PCR. Subsequently, possible risk factors (i.e., age, gender, and breed of the animals and type of feed used) were evaluated using univariate and multivariate analyses. Tannerella spp. and Treponema spp. were detected in a significantly higher proportion of horses affected by EPD than in healthy animals, although pathogens belonging to RCB were detected in low number of horses. At the same time, none of the investigated pathogens was significantly associated with a particular stage of disease severity. Horses aged older than 20 years were at higher risk of EPD. The high rate of coinfection, statistically associated with EPD, supports the hypothesis that EPD is a complex syndrome characterized by the possible simultaneous involvement of several pathogens and an increased risk depending on the animal’s age. Constant oral hygiene is the best prevention to prevent and treat the disease, especially in old animals.
Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil–Pile Interface Model
Authors: Yoo Byeong-Soo , Tran Nghiem Xuan , Kim Sung-Ryul
Published: May 2023

The liquefaction of soil surrounding a pile significantly affects the dynamic interaction between the soil and the pile. In particular, liquefaction of the sloping ground can induce permanent deformation and a bending moment on the pile due to the lateral displacement of the liquefied soil in the downslope direction. However, numerical analysis studies on piles installed in a liquefiable slope have been very limited and have not properly simulated the behavior of the pile. Therefore, a modified soil–pile interface model was proposed, which linearly decreases the interface friction angle with the increase in the excess pore pressure ratio. The proposed model was validated by comparing it with the centrifuge test results of Yoo et al. (2023). Simulation results on the slope crest settlement and the pile-bending moment showed good agreement with the centrifuge test results. A parametric study was conducted by applying the validated model to analyze the effect of slope inclinations and the amplitude of input motions on the slope displacement and the pile moment. The simulation results showed that the slope inclinations affected the area of the sliding mass, causing a larger pile-bending moment with a larger inclination. When the amplitude of the input motion was sufficiently large to trigger the failure of the liquefied slope, the slope displacement and the pile-bending moment did not increase any further.
Deep Drawing Behaviour of Steel–Glass Fibre-Reinforced and Non-Reinforced Polyamide–Steel Sandwich Materials
Authors: Hua Wei , Harhash Mohamed , Ziegmann Gerhard , Carradò Adele , Palkowski Heinz
Published: May 2023

Thermoplastic-based fibre metal laminates (FMLs) have gained increasing interest in the automotive industry due to their forming potential—especially at higher temperatures—into complex components compared to thermoset-based ones. However, several challenges arise while processing thermoplastic-based FMLs. One the one hand, forming at room temperature (RT) leads to early failure modes, e.g., fracture and delamination. On the other hand, warm forming can extend their forming limits, although further defects arise, such as severe thickness irregularities and wrinkling problems. Therefore, this study focuses on developing different approaches for deep drawing conditions to deliver a promising, feasible, and cost-effective method for deep-drawn FML parts. We also describe the defects experimentally and numerically via the finite element method (FEM). The FMLs based on steel/glass fibre-reinforced polyamide 6 (GF-PA6/steel) are studied under different deep drawing conditions (temperatures, punch, and die dimensions). In addition, mono-materials and sandwich materials without fibre reinforcement are investigated as benchmarks. The results showed that the best deep drawing condition was at a temperature of 200 °C and a die/punch radius ratio of 0.67, with a gap/thickness ratio of ≤2.0. The FEM simulation via Abaqus 6.14 was able to successfully replicate the anisotropic properties and wrinkling of the GF-PA6 core in an FML, resembling the experimental results.
Betatron Radiation and Bremsstrahlung in the Interaction of Intense Laser Pulse with Solid Target
Authors: Li Ling , Li Ran , Ju Libao , Jiang Ke , Yu Mingyang , Huang Taiwu , Zhang Hua , Wu Sizhong , Qiao Bin , Zhou Cangtao , He Xiantu
Published: May 2023

X-ray generation via synchrotron radiation and bremsstrahlung in the interaction of short laser pulses with a solid target is of much current interest owing to its numerous applications. The efficiency of laser to X-ray energy conversion is thus a crucial factor. We found that the energy conversion efficiency of synchrotron radiation and bremsstrahlung is mainly governed by the ratio of the laser pulse width to the preplasma width, which is in turn governed by the laser profile, intensity, and spot size. Synchrotron radiation dominates when the ratio is less than unity, otherwise bremsstrahlung dominates. The type of radiation can thus be controlled by tailoring the laser parameters.
Experimental Study of Pore-Scale Water Flooding with Phase Change Based on a Microfluidic Model in Volatile Carbonate Reservoirs
Authors: Jia Pin , Li Yang , Guo Hongxin , Feng Haoran , Cheng Linsong
Published: May 2023

Carbonate reservoirs usually have strong heterogeneity, with complex pore structure and well-developed natural fractures. During reservoir development, when the formation pressure is lower than the bubble point pressure of crude oil, the fluid undergoes phase change and degassing. This leads to the subsequent waterflooding displacement under the oil–gas two-phase condition, also followed by a secondary phase change of oil and gas caused by the increase in formation pressure. In this paper, the glass etching model is used to carry out microfluidic experiments. The porous carbonate model and the fractured porous carbonate model are designed to simulate the process of depletion development and waterflooding development. In the process of depletion development, it can be observed that the crude oil degassing and gas phase occurrence areas of the porous model are in the order of the large pore throat area first, followed by the small pore throat area. And the crude oil degassing and gas phase occurrence order in the fractured porous model is as follows: fractures, large pore throat area and, finally, small pore throat area. In the process of converting to the waterflooding development, the early stage of the replacement reflects the obvious characteristic of “displace oil but not gas”; with the replenishment of formation energy, the gas redissolution area expands from the mainstream to other areas, and the waterflooding mobilization increases. The characteristics of oil, gas and water flow in different stages of carbonate reservoirs with different pore-fracture characteristics that are clarified, and the characteristics of fluid migration and the distribution under the condition of oil and gas coexisting and water flooding after crude oil degassing are explored, and the water displacement mechanism of volatile carbonate reservoirs with different pressure levels is revealed.
Intelligent TCP Congestion Control Policy Optimization
Authors: Shi Hanbing , Wang Juan
Published: May 2023

Network congestion control is an important means to improve network throughput and reduce data transmission delay. To further optimize the network data transmission capability, this research suggests a proximal policy optimization-based intelligent TCP congestion management method, creates a proxy that can communicate with the real-time network environment, and abstracts the TCP congestion control mechanism into a partially observable Markov decision process. Changes in the real-time state of the network are fed back to the agent, and the agent makes action commands to control the size of the congestion window, which will produce a new network state, and the agent will immediately receive a feedback reward value. To guarantee that the actions taken are optimum, the agent’s goal is to obtain the highest feedback reward value. The state space of network characteristics should be designed so that agents can observe enough information to make appropriate decisions. The reward function is designed through a weighted algorithm that enables the agent to balance and optimize throughput and latency. The model parameters of the agent are updated by the proximal policy optimization algorithm, and the truncation function keeps the parameters within a certain range, reducing the possibility of oscillation during gradient descent and ensuring that the training process can converge quickly. Compared to the traditional CUBIC control method, the results show that the TCP-PPO2 policy reduces latency by 11.7–87.5%.
Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
Authors: Gruden Timotej , Jakus Grega
Published: May 2023

In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.
Proposals for Flexural Capacity Prediction of Precast Segmental Concrete Beam Prestressed with Internal Un-Bonded CFRP Tendons
Authors: Yan Wutong , Jiang Fangxin , Chen Liangjiang , Sun Yue
Published: May 2023

The application of CFRP tendons in precast segmental concrete beams (PSCB) as internal un-bonded prestressing reinforcement is a newly developed scheme to improve structural flexural performance. The stress increment of the un-bonded tendon, depending on the whole structural deformation, is a crucial value to be predicted for flexural capacity design. Due to the discontinuity of the opening joints, the deformation modes of segmental beams differ from the monolithic ones. The existing prediction methods built for monolithic beams can not be directly used for segmental beams. In this paper, the new prediction equations of the tendon stress increment and flexural capacity were put forward for PSCB with internal un-bonded CFRP tendons (PSCB-IUCFRP). Firstly, the differences between the deformation modes of monolithic and segmental beams were compared and clarified based on the numerical model analysis. Then, a parametric analysis was conducted on 162 numerical models, and the results were employed to evaluate the applicability of existing methods for PSCB-IUCFRP. The predictions of the ACI 318-14 model and the AASHTO LRFD model were both conservative and scattering compared with numerical results. The ACI 440.4R model underestimated the tendon stress increments of beams under one-point loading but overestimated it for those under two-point loading. According to the failure mode of PSCB-IUCFRP, a simplified curvature distribution mode was assumed, and the relation between tendon elongation and structural deflection was derived. The prediction equations for PSCB-IUCFRP were proposed using the back-calculated plastic hinge length. Compared with existing methods, the proposed equations considered the deformation characteristic of segmental beams and had clear physical significance. The predictions of the proposed method were in good agreement with the numerical and experimental results. Furthermore, a balanced prestressing reinforcement ratio equation is proposed for PSCB-IUCFRP to avoid tendon rupture-controlled failure. The proposed equations provide suggestions for the flexural design of PSCB-IUCFRP and will help to popularize this new structure.
A Decision Tree for Rockburst Conditions Prediction
Authors: Owusu-Ansah Dominic , Tinoco Joaquim , Lohrasb Faramarzi , Martins Francisco , Matos José
Published: May 2023

This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.
Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
Authors: Chen Wenyu , Li Weimin , Shao Lei , Zhang Tao
Published: May 2023

During the interception of a high-speed gliding vehicle (HGV), the predicted intercept point (PIP) is updated many times, so the interceptor must continuously adjust its trajectory. In this paper, we propose an online guidance correction algorithm for the interceptor, which can effectively meet the position constraints and energy management of the guidance process. Firstly, we simplify the interceptor’s maneuvering problem to a two-body orbital transfer problem. The Lambert orbit transfer method is used to enable the interceptor to maneuver towards the target’s PIP. Secondly, by comparing the target’s high probability existence area (THPEA) with the interceptor-interceptable area (IIA), the engine start threshold is set in stages to avoid frequent redundancy maneuvers caused by frequent engine starting during missile correction. This approach achieves energy management. Simulation results show that our midcourse guidance strategy is effective.
Optimal Preparation Protocol of Cell-Encapsulating Alginate Capsules for Long-Term Cell-Based Therapy
Authors: Suzuki Ryota , Kusamori Kosuke , Takamura Kodai , Miyakawa Yuma , Obana Shu , Itakura Shoko , Nishikawa Makiya
Published: May 2023

Cell-based therapy is an excellent therapeutic modality that involves cell transplantation into patients; however, given that most transplanted cells die immediately post-transplantation, the application of this strategy remains limited. Cell encapsulation is a promising technique for prolonging the survival of transplanted cells, although a definitive encapsulation protocol is yet to be established. Herein, we selected sodium alginate as a polymer for cell encapsulation and optimized the structure and function of cell-encapsulating alginate capsules. First, alginate capsules were prepared using various concentrations of sodium alginate and calcium chloride solution. The NanoLuc luciferase (Nluc)-expressing murine mesenchymal stem cell line C3H10T1/2 was used to prepare the alginate capsules, and cell survival was evaluated after transplantation into mice. The structural properties of the alginate capsules were dependent on the preparation conditions. Capsules with adequate hardness were obtained using 1% sodium alginate and 10% calcium chloride solutions. Alginate capsules encapsulating 5 × 103 C3H10T1/2/Nluc cells/10 μL maintained a constant cell number over time under in vitro culture conditions. After transplantation into mice, C3H10T1/2/Nluc cells encapsulated in alginate capsules exhibited a significantly longer survival (≥40 days) than suspended cells. Based on these findings, cell-encapsulating alginate capsules with optimal properties can be used for long-term cell-based therapies.
Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes
Authors: de Ávila Álvaro Vasconcellos Araujo , de Gonçalves Luis Gustavo Gonçalves , Souza Vanessa de Arruda , Alves Laurizio Emanuel Ribeiro , Galetti Giovanna Deponte , Maske Bianca Muss , Getirana Augusto , Ruhoff Anderson , Biudes Marcelo Sacardi , Machado Nadja Gomes , Roberti Débora Regina
Published: May 2023

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.
Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents
Authors: Song Jianyang , Tian Hua , Yuan Xiaoyu , Gao Jingjing , Yin Xihui , Wang Zhi , Qian Meichao , Zhang Hengtong
Published: May 2023

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results.
Uncovering the Impact of Local and Global Interests in Artists on Stock Prices of K-Pop Entertainment Companies: A SHAP-XGBoost Analysis
Authors: Yu Daeun , Choi Sun-Yong
Published: May 2023

Stock price prediction is a significant area of research in finance that has been ongoing for a long time. Several mathematical models have been utilized in this field to predict stock prices. However, recently, machine learning techniques have demonstrated remarkable performance in stock price prediction. Moreover, XAI (explainable artificial intelligence) methodologies have been developed, which are models capable of interpreting the results of machine learning algorithms. This study utilizes machine learning to predict stock prices and uses XAI methodologies to investigate the factors that influence this prediction. Specifically, we investigated the relationship between the public’s interest in artists affiliated with four K-Pop entertainment companies (HYBE, SM, JYP, and YG). We used the Naver Keyword Trend and Google Trend index data for the companies and their representative artists to measure local and global interest. Furthermore, we employed the SHAP-XGBoost model to show how the local and global interest in each artist affects the companies’ stock prices. SHAP (SHapley Additive exPlanations) and XGBoost are models that show excellent results as XAI and machine learning methodologies, respectively. We found that SM, JYP, and YG are highly correlated, whereas HYBE is a major player in the industry. YG is influenced by variables from other companies, likely owing to HYBE being a major shareholder in YG’s subsidiary music distribution company. The influence of popular artists from each company was significant in predicting the companies’ stock prices. Additionally, the foreign ownership ratio of a company’s stocks affected the importance of Google Trend and Naver Trend indexes. For example, JYP and SM had relatively high foreign ownership ratios and were influenced more by Google Trend indexes, whereas HYBE and YG were influenced more by Naver Trend indexes. Finally, the trend indexes of artists in SM and HYBE had a positive correlation with stock prices, whereas those of YG and JYP had a negative correlation. This may be due to steady promotions and album releases from SM and HYBE artists, while YG and JYP suffered from negative publicity related to their artists and executives. Overall, this study suggests that public interest in K-Pop artists can have a significant impact on the financial performance of entertainment companies. Moreover, our approach offers valuable insights into the dynamics of the stock market, which makes it a promising technique for understanding and predicting the behavior of entertainment stocks.
Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services
Authors: Nguyen Luong Vuong , Vo Quoc-Trinh , Nguyen Tri-Hai
Published: May 2023

In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite its popularity, collaborative filtering has limitations that researchers aim to overcome. In this paper, we enhance the K-nearest neighbor (KNN)-based collaborative filtering algorithm for a recommendation system, by considering the similarity of user cognition. This enhancement aimed to improve the accuracy in grouping users and generating more relevant recommendations for the active user. The experimental results showed that the proposed model outperformed benchmark models, in terms of MAE, RMSE, MAP, and NDCG metrics.
Phytochemical Extracts of Dittrichia viscosa (L.) Greuter from Agroecological Systems: Seed Antigerminative Properties and Effectiveness in Counteracting Alternaria Leaf Spot Disease on Baby-Leaf Spinach
Authors: Pane Catello , Manganiello Gelsomina , Vitti Antonella , Celano Rita , Piccinelli Anna Lisa , De Falco Enrica
Published: May 2023

Dittrichia viscosa (L.) Greuter subsp. viscosa (Asteraceae) is a perennial species naturally distributed in arid and marginal areas whose agroecological cultivation could be a useful innovation to produce quality biomass to extract phenolic-rich phytochemical blends. Here, biomass-yield trends were profiled at different growth stages under direct cropping, and inflorescences, leaves, and stems were submitted to water extraction and hydrodistillation. Then, four extracts were investigated for their biological activities in invitro and in planta assays. Extracts inhibited cress (Lepidium sativum)- and radish (Raphanus sativus)-seed germination and root elongation. All samples showed dose-dependent antifungal activity in the plate experiments, inhibiting up to 65% of the growth of the fungal pathogen Alternaria alternata, a leaf-spot disease agent of baby spinach (Spinacea oleracea). However, only the extracts from dried green parts and fresh inflorescences at the highest concentration significantly reduced (54%) the extent of Alternaria necrosis on baby spinach. UHPLC-HRMS/MS analysis revealed that the main specialized metabolites of the extracts are caffeoyl quinic acids, methoxylated flavonoids, sesquiterpene compounds such as tomentosin, and dicarboxylic acids, which may explain the observed bioactivity. Plant extracts obtained through sustainable methodology can be effective in biological agricultural applications.
Notes on the Spreading of Penaeus aztecus Ives 1891 (Decapoda, Penaeidae) in the Mediterranean Sea and on Its Repeated Misidentifications in the Region
Authors: Froglia Carlo , Scanu Martina
Published: May 2023

The shrimp Penaeus aztecus, native to the western Atlantic, was first reported in the eastern Mediterranean Sea in 2010. New records, from different Mediterranean localities, multiplied in the following years. The accurate search of the literature on non-indigenous species discovered it was misidentified more than once as another alien shrimp, P. semisulcatus, native to the Indo-Pacific region, with the result that its earlier presence in the Black Sea went unnoticed. Morphological characteristics allowing the identification of these two species, the autochthonous P. kerathurus and two other alien Penaeus species present in the Mediterranean, are reprised. The present distribution of P. aztecus based on literature records and surveys carried out in the northern and central Adriatic between 2016 and 2021 is mapped. The unintentional transport of larvae carried in ballast water by transoceanic vessels departing from the U.S. East Coast is suggested as the most probable introduction pathway. The significance of the correct identification of non-indigenous species, a “Descriptor” adopted in the Marine Strategy Framework Directive for determining the good environmental status of marine waters in the European States, is emphasized.
Evaluating Current Molecular Techniques and Evidence in Assessing Microbiome in Placenta-Related Health and Disorders in Pregnancy
Authors: Stupak Aleksandra , Kwaśniewski Wojciech
Published: May 2023

The microbiome is of great interest due to its potential influence on the occurrence and treatment of some human illnesses. It may be regarded as disruptions to the delicate equilibrium that humans ordinarily maintain with their microorganisms or the microbiota in their environment. The focus of this review is on the methodologies and current understanding of the functional microbiome in pregnancy outcomes. We present how novel techniques bring new insights to the contemporary field of maternal–fetal medicine with a critical analysis. The maternal microbiome in late pregnancy has been extensively studied, although data on maternal microbial changes during the first trimester are rare. Research has demonstrated that, in healthy pregnancies, the origin of the placental microbiota is oral (gut) rather than vaginal. Implantation, placental development, and maternal adaptation to pregnancy are complex processes in which fetal and maternal cells interact. Microbiome dysbiosis or microbial metabolites are rising as potential moderators of antenatal illnesses related to the placenta, such as fetal growth restriction, preeclampsia, and others, including gestational diabetes and preterm deliveries. However, because of the presence of antimicrobial components, it is likely that the bacteria identified in placental tissue are (fragments of) bacteria that have been destroyed by the placenta’s immune cells. Using genomic techniques (metagenomics, metatranscriptomics, and metaproteomics), it may be possible to predict some properties of a microorganism’s genome and the biochemical (epigenetic DNA modification) and physical components of the placenta as its environment. Despite the results described in this review, this subject needs further research on some major and crucial aspects. The phases of an in utero translocation of the maternal gut microbiota to the fetus should be explored. With a predictive knowledge of the impacts of the disturbance on microbial communities that influence human health and the environment, genomics may hold the answer to the development of novel therapies for the health of pregnant women.