Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of nutritional composition of seeds of wild-type soybean and lines overexpressing GmWRI1a

ABSTRACT Context. With the widespread use of transgenic soybeans (Glycine max (L. ) Merr. ), their nutritional assessment for human food and animal feed is an important aspect of safety evaluation. WRINKLED 1 (WRI1) is a transcriptional activator of genes involved in fatty acid synthesis. Aims. We aimed to evaluate the effect of overexpression of GmWRI1a in soybean on the nutritional profile of soybean seeds. Methods. Using molecular biology techniques, we identified three transgenic soybean lines stably overexpressing GmWRI1a (GmWRI1a-OE). We quantified the nutritional and anti-nutritional components in seeds of GmWRI1a-OE and wild-type (DN50) soybean, and compared them on the basis of the principle of substantial equivalence. Key results. The GmWRI1a and Bar genes were stably inherited in the three GmWRI1a-OE lines, and GmWRI1a protein content was higher in transgenic soybean seeds than in wild-type seeds. Overexpression of GmWRI1a resulted in changes in fatty acid composition, a significant increase in oil content, and a significant decrease in stachyose content in seeds. Contents of other nutritional components (proximates, amino acids and isoflavones) and anti-nutritional factors (phytic acid, trypsin inhibitors and raffinose) in seeds were not significantly different between the wild-type and GmWRI1a-OE lines. Conclusions. Contents of nutritional and anti-nutritional components of GmWRI1a-OE seeds were all within the reference ranges reported for commercially available soybeans and, therefore, are substantially equivalent to those of wild-type seeds. Implications. GmWRI1a-OE soybean seeds are a high-quality product for health-conscious consumers.
CD-Guide: A Dispatching and Charging Approach for Electric Taxicabs

Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers’ origin buildings is effective in illustrating the suitability of historical data for learning; 2) passenger demands in different regions vary a lot due to various random factors; and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab’s likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots.
Investigation of Subsynchronous Oscillation in a DFIG-Based Wind Power Plant Connected to MTDC Grid

This paper investigates a new type of subsynchronous oscillation (SSO) event observed in a real-world doubly fed induction generator (DFIG)-based wind power plant (WPP) connected to a multiterminal dc (MTDC) grid in Northern China. The system under study is unique and, to date, is the world's largest four-terminal DC transmission network, which connects far-away wind and photovoltaic power plants to regional load centers. For the first time, this paper utilized field-measured data to analyze the characteristics and reveal the underlying mechanism of the SSO in doubly fed induction generators (DFIGs) connected to HVDC grids. The analysis is carried out using the impedance network model established from the impedances obtained from field recorded data. The investigations on the field data concluded that the SSO is essentially caused by the subsynchronous interaction between the DFIGs and the MTDC grid without the presence of a physical series/shunt compensation or weak AC grid. Instead, it is found that the SSO is triggered due to the converter-grid resonance where the MTDC grid presented itself as a virtual capacitor and the DFIGs showed negative resistance at the subsynchronous frequency range.
Non-LTE analysis of massive O stars. III. The O 3 stars HD 93128, HD 93129A, and HDE 303308.

Analyse des trois etoiles HD 93128, HD 93129 A et HDE 303308 au moyen d'un spectre coude a faible bruit et haute resolution. Les mesures de la largeur de la raie HeI (4471 μ) et des profils des raies de Balmen et HeII fournissent l'abondance d'helium normal, les temperatures effectives et les gravites de chaque etoile
Interruption Management in the Context of Take-Over-Requests in Conditional Driving Automation

Drivers of partially automated vehicles are relieved from parts of the driving tasks allocated to the automated driver. This reduction in driving demands encourages them to engage with nondriving related tasks, which may impair awareness of the road environment once a takeover request (TOR) is initiated. This article examined the four suggested strategies drivers that take to regain control following a TOR, from the perspective of interruption management principles. Thirty students participated in a simulated study of two drives, where we manipulated TOR alerts, time to regain control, and potential road hazards. We hypothesized that all four interruption management strategies will be observed. Our hypothesis was confirmed. Four strategies were identified. Most drivers chose strategy 2 to accept and initiate the takeover immediately after the TOR started. The second frequent strategy was to reject the TOR but look at the road. Drivers’ strategy choices changed following alert type and the chronological drive order. With simulated driving experience (i.e., second drive), drivers postponed taking control, adapting to the time budget. Yet, inaccurate understanding of the situation or over-trust affected the chosen strategy. We conclude that interruption management principles are beneficial for studying how drivers respond to TORs and evaluating options to improve TOR performance.
Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework’s adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
Molecular Docking Study for Prediction of Chiral HPLC Separation of Hydroxychloroquine as an Alternative Antiviral of SARS-CoV-2

The HPLC chiral separation of hydroxychloroquine (HCQ) using chiral α-1-acid glycoprotein (AGP) column has been predicted based on a molecular docking approach. The research begins with the geometrical optimization of the HCQ compound using the quantum calculation method of semiempirical (SE) of PM6, AM1, and PM3, and Hartree-Fock (HF) and density functional theory (DFT/B3LYP) with the basis set of 3-21G, 6-31G, and 6-311G. Molecular docking was performed with AutoDock Vina and PyRx applications on exhaustiveness of 264. Redocking with AutoDock Vina was done using coordinates of X = 13.584; Y = 1.47; Z = 18.451 with a grid box size of 40 x 40 x 40 and a grid Spacing of 0.375 Å, followed by specific docking process using the same conditions as redocking. The DFT method with the basis set of 6-311G was the best calculation method because it gives the lowest PRESS and closest r2 value to one for the comparison between calculated and experimental data of 1H-NMR. The docking result shows that R-HCQ enantiomer has more negative value of binding energy and more diverse interactions in the inclusion complex, indicating that R-HCQ forms more stable complex with AGP, and therefore it will be retained longer in the AGP column and eluted from the column later after R-HCQ.
Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.
Spectroscopic parallaxes of MAP region stars from UBVRI, DDO, and uvbyH-beta photometry. [Multichannel Astrometric Photometer for astronomical observation]

This paper presents the results of spectral type and luminosity classification of reference stars in the Allegheny Observatory MAP parallax program, using broadband and intermediate-band photometry. In addition to the use of UBVRI and DDO photometric systems, the uvbyH-beta photometric system was included for classification of blue (B - V less than 0.6) reference stars. The stellar classifications made from the photometry are used to determine spectroscopic parallaxes. The spectroscopic parallaxes are used in turn to adjust the relative parallaxes measured with the MAP to absolute parallaxes. A new method for dereddening stars using more than one photometric system is presented. In the process of dereddening, visual extinctions, spectral types, and luminosity classes are determined, as well as a measure of the goodness of fit. The measure of goodness of fit quantifies confidence in the stellar classifications. It is found that the spectral types are reliable to within 2.5 spectral subclasses.
etrm: Energy Trading and Risk Management in R

This paper introduces etrm , an R package with tools for trading and financial risk management in energy markets. Contracts for electric power and natural gas differ from most other commodities due to the fact that physical delivery takes place over a time interval, and not at a specific point in time. There is typically strong seasonality, limited storage and transmission capacity and strong correlation between price and required volume. Such characteristics need to be taken into account when pricing contracts and managing financial risk related to energy procurement. Tools for these task are usually bundled into proprietary Energy Trading Risk Management (ETRM) systems delivered by specialized IT vendors. The etrm package offers a transparent solution for building a forward price curve for energy commodities which is consistent with methods widely used in the industry. The user’s fundamental market view may be combined with contract price quotes to form a forward curve that replicate current market prices, as described in Ollmar (2003) and Benth et al. (2007b). etrm also provides implementations of five portfolio insurance trading strategies for energy price risk management. The forward market curve and the energy price hedging strategies are core elements in an ETRM system, which to the best of the author’s knowledge has not been previously available in the R ecosystem. rate, target price and portfolio price. methods plot() , summary() and show() are implemented in "GenericStrat" and inherited by the strategy classes. The plot() method returns a chart based on "Results" , with daily values for portfolio, market and target prices and portfolio hedge rate. The summary() method returns a list with five elements; a description string, portfolio volume, target price, calculated churn rate and a data frame with summary statistics for the trading period. Finally, the show() method returns the "Results" data
Muscidae (Diptera)- A Historical Perspective

In India, the history of biological research on muscid flies has never been thoroughly reviewed. There is no useful documentation of their recent and past taxonomic, medical, veterinary, and forensic research trends, as well as natural history and ecology studies. In the 75 years since independence, efforts have mostly focused on faunistic surveys. However, new study avenues have emerged in agricultural, medical, and forensic fields, as well as other areas such as molecular, ecological, and microbial research. In order to develop a state-wise perspective, we reviewed all the available old and recent studies on family Muscidae (Diptera) throughout the country and suggested areas for future research.
Morphological Characterization of Terminalia chebula Retz. in Sri Lanka

Terminalia chebula Retz. (Aralu in Sinhala) is a perennial plant, which consists of vital medicinal properties. The T. chebula trees are naturally distributed only in the Intermediate Zone of Sri Lanka, although planted trees can be seen in all agro-climatic zones. However, no agronomic or ecological research has been conducted on this species. Consequently, this research was conducted to identify the ecological distribution of T. chebula in different agro-ecological regions of Sri Lanka and to characterize the different populations based on its morphological variation. Forty one morphological characters were considered using samples from 125 trees representing different populations. The studied populations in Sri Lanka could be clustered into 4 groups based on their morphological characters. Each group consisted of plants from different locations, which suggest that the grouping is not based on environmental parameters. However, 80% of the Nilgala population grouped into one cluster and all the natural T. chebula plants are located only in Intermediate Zone indicating a relationship between the plant and the environmental parameters under natural conditions. Tropical Agricultural Research Vol. 25 (1): 127-132 (2013)
A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning

Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the broiler house by the breeders every day. However, as the total amount of broilers is huge, the inspection work is not only time-consuming but also laborious. Therefore, a dead broiler inspection system is constructed in this study to replace the manual inspection work. It mainly consists of an autonomous inspection platform and a dead broiler detection model. The automatic inspection platform performs inspections at the speed of 0.2 m/s in the broiler house aisle, and simultaneously collects images of the four-layer broilers. The images are sent to a server and processed by a dead broiler detection model, which was developed based on the YOLOv3 network. A mosaic augment, the Swish function, an spatial pyramid pooling (SPP) module, and complete intersection over union (CIoU) loss are used to improve the YOLOv3 performance. It achieves a 98.6% mean average precision (intersection of union (IoU) = 0.5) and can process images at 0.007 s per frame. The dead broiler detection model is robust to broilers of different ages and can adapt to different lighting conditions. It is deployed on the server with a human–machine interface. By observing the processing results using the human–machine interface, the breeders could directly find the cage position of dead broilers and remove them, which could reduce the workload of breeders and promote the intelligent development of poultry breeding.
Spatial effect of digital financial inclusion on the urban–rural income gap in China—analysis based on path dependence

Digital financial inclusion (DFI) helps to narrow the income gap between urban and rural areas, but path dependence may lead to spatial agglomeration in the development of DFI, causing the spatial effect on the urban – rural income gap. This study mainly examines the mechanism and effect of DFI on the urban – rural income gap in China, including its spatial effect issues. Results show that China ’ s DFI has different impacts on the urban – rural income gap in the east, central, and western regions, showing evident spatial heterogeneity. In addition, the development of DFI has spatial agglomeration, and its impact on China ’ s urban – rural income gap also has a spatial spillover effect. Moreover, the spatial effect of DFI is mainly because of the path dependence on the development of traditional finance and digital technology. The spatial correlation between them is transmitted to DFI, and the impact of digital technology development is greater. Finally, corresponding policy recommendations are proposed based on the conclusions.
Chronic Cerebral Hypoperfusion Aggravates Parkinson's Disease Dementia-Like Symptoms and Pathology in 6-OHDA-Lesioned Rat through Interfering with Sphingolipid Metabolism

Chronic cerebral hypoperfusion (CCH) is a cardinal risk factor for Parkinson's disease dementia (PDD), but this potential causality lacks mechanistic evidence. We selected bilateral common carotid artery occlusion (BCCAO) to simulate chronic cerebral hypoperfusion in the rat model of PD induced by typical neurotoxin 6-hydroxy dopamine (6-OHDA). Four weeks after unilateral injection of 6-OHDA into the medial forebrain bundle, rats underwent BCCAO. Male Sprague-Dawley rats were divided into five groups of ten, including sham, PD+BCCAO 2 weeks, PD+BCCAO 1 week, PD, and BCCAO 2 weeks. Then, open field test (OFT) and Morris water maze test (MWM) were used to assess the PDD-like symptoms in rats. Also, the pathological manifestations and mechanisms of BCCAO impairing cognitive functions have been explored via hematoxylin-eosin staining, Nissl staining, immunohistochemistry, immunofluorescence, RNA sequencing analysis, lipidomics, and quantitative real-time polymerase chain reaction. In this study, we found that CCH could aggravate PDD-like cognitive symptoms (i.e., learning memory and spatial cognition) and PDD-like pathology (higher expression of α-Syn and Aβ in prefrontal cortex and striatum). Moreover, a potential relationship between differentially expressed mRNAs and lipid metabolism was revealed by RNA sequencing analysis. Lipidomics showed that CCH could affect the intensity of 5 lipids, including sphingomyelin (SM 9:0;2O/26:2; SM 8:1;2O/25:0; and SM 8:0;2O/28:4), cardiolipin, lysophosphatidylcholine, cholesteryl ester, and triacylglycerol. Interestingly, the KEGG pathway analysis of both RNA sequencing analysis and lipidomics suggested that CCH leaded to learning impairment by affecting sphingolipid metabolism. Finally, we found that CCH disrupts the sphingolipid metabolism by affecting the mRNA expression of SMPD1 and SMS2, leading to the accumulation of sphingomyelin in the prefrontal cortex. In summary, CCH, an independent exacerbating reason for impairment in learning and memory within the pathopoiesis of PD, aggravates Parkinson's disease dementia-like symptoms and pathology in 6-OHDA-lesioned rat through interfering with sphingolipid metabolism.
From Pattern to Interpretation. Using Colibri Core to Detect Translation Patterns in the Peshitta.

This article presents the first results of the CLARIAH-funded project ‘Patterns in Translation: Using Colibri Core for the Syriac Bible’ (PaTraCoSy). This project seeks to use Colibri Core to detect translation patterns in the Peshitta, the Syriac translation of the Hebrew Bible. We first describe how we constructed word and phrase alignment between these two texts. This step is necessary to succesfully implement the functionalities of Colibri Core. After this, we further describe our first investigations with the software. We describe how we use the built-in pattern modeller to detect n-gram and skipgram patterns in both Hebrew and Syriac texts. Colibri Core does not allow the creation of a bilingual model, which is why we compare the separate models. After a presentation of a few general insights on the overall translation behaviour of the Peshitta, we delve deeper into the concrete patterns we can detect by the n-gram/skipgram analysis. We provide multiple examples from the book of Genesis, a book which has been treated broadly in scholarly research into the Syriac translation, but which also appears to have interesting features based on our Colibri Core research.
Kinetics of Lignin Removal from the Lignocellulosic Matrix after Ozone Transportation

This study presents a new method to remove lignin from wheat straw (lignocellulosic) using the ozonation technique. Lignocellulosic material is a complex biopolymer composed of cellulose, hemicellulose and lignin. Apart from lignin, which acts as a chemical resistant, lignocellulosic is the main resource of cellulose and hemicellulose sugars. The ozonation reaction takes place in a two-phase solid–gas fluidization stainless steel reactor. The focus of this paper is to investigate the kinetics that govern lignin removal from lignocellulosic material after ozonation treatment. The kinetics of lignin removal did not agree with the experimental data until the suggested model is modified to a pseudo-second-order. The results showed that at a higher ozone supply of 150 mg min−1, the surface reaction and intra-particular diffusion were the most significant factors to remove the lignin. Moreover, at a lower ozone supply of 30 mg min−1, the intra-particular diffusion was the only contributor towards lignin removal.