Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of anise seed (Pimpinella anisum l.) on some quality parameters of alfalfa silages

In current study, the effects of different doses of anise seed (Pimpinella anisum L.) on the chemical composition, fermentation quality and physical characteristics of alfalfa silages were investigated. Chemical composition, fermentation quality and physical characteristics of experiment silages were performed on the samples opened on the 75th day of ensiling. As a result, the differences in alfalfa silage in terms of dry matter, crude ash and ADF contents were not found statistically significant (P>0.05). Silages treated with anise seed had higher NDF contents than control group and 0.5 % anise seed (P 0.05), but its structures increased significantly in the 0.5%, 1.0 and 1.5 anise seeds compared to the control group (P<0.05). At the end of the study, it was concluded that the quality of silage improved with the treatment of alfalfa silage with 0.5% anise seed.
An Extensive Review of Mobile Agricultural Robotics for Field Operations: Focus on Cotton Harvesting

In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.
Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks

In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects–discontinuous dynamics and data-efficiency–both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.
Comparison of surgical treatments of tumor-induced osteomalacia in different locations in the lower limbs

Abstract Tumor-induced osteomalacia (TIO) is a rare disease that behaves benignly. Very few reports about the features of the responsible tumors according to anatomical locations have been presented. In this retrospective study of 53 patients with TIO-associated tumors in the foot/ankle, tibia and femur, we compared preoperative, postoperative, and follow-up courses, including alkaline phosphatase, phosphorus, and fibroblast growth factor 23, to compare the characteristics of TIO-associated tumors in these 3 locations (level of evidence: therapeutic level III). Patients in the foot/ankle group had longer disease courses and therefore a significantly higher complication rate (P < .001). All TIO-associated tumors in the foot/ankle group involved soft tissue (P = .021), whereas most lesions in the tibia group involved bone, and therefore had much higher concentrations of alkaline phosphatase (P = .020). Additionally, serum phosphorus took much longer to normalize after surgery in the foot/ankle group than that in the other 2 groups (P = .004). Consequently, symptom remission was much better in the tibia and femur groups (P = .008). Moreover, the Ki 67 index in TIO-associated tumors was significantly higher in the foot/ankle group (P < .001) and the recurrence rate in this group was markedly higher (P = .002). The TIO-associated tumors in the foot/ankle are characteristically of occult onset, more soft-tissue involvement, and more readily recurrence. More knowledge and examinations are necessary to enable early diagnosis, radical treatments, and minimize recurrence. New therapies are welcomed and needed.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation

This work develops a novel end-to-end deep unsupervised learning method based on convolutional neural network (CNN) with pseudo-classes for remote sensing scene representation. First, we introduce center points as the centers of the pseudo classes and the training samples can be allocated with pseudo labels based on the center points. Therefore, the CNN model, which is used to extract features from the scenes, can be trained supervised with the pseudo labels. Moreover, a pseudo-center loss is developed to decrease the variance between the samples and the corresponding pseudo center point. The pseudo-center loss is important since it can update both the center points with the training samples and the CNN model with the center points in the training process simultaneously. Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes. Experiments are conducted over two commonly used remote sensing scene datasets to validate the effectiveness of the proposed method and the experimental results show the superiority of the proposed method when compared with other state-of-the-art methods.
Hearing through Your Eyes: Neural Basis of Audiovisual Cross-activation, Revealed by Transcranial Alternating Current Stimulation

Some people experience auditory sensations when seeing visual flashes or movements. This prevalent synaesthesia-like visually evoked auditory response (vEAR) could result either from overexuberant cross-activation between brain areas and/or reduced inhibition of normally occurring cross-activation. We have used transcranial alternating current stimulation (tACS) to test these theories. We applied tACS at 10 Hz (alpha band frequency) or 40 Hz (gamma band), bilaterally either to temporal or occipital sites, while measuring same/different discrimination of paired auditory (A) versus visual (V) Morse code sequences. At debriefing, participants were classified as vEAR or non-vEAR, depending on whether they reported “hearing” the silent flashes. In non-vEAR participants, temporal 10-Hz tACS caused impairment of A performance, which correlated with improved V; conversely under occipital tACS, poorer V performance correlated with improved A. This reciprocal pattern suggests that sensory cortices are normally mutually inhibitory and that alpha-frequency tACS may bias the balance of competition between them. vEAR participants showed no tACS effects, consistent with reduced inhibition, or enhanced cooperation between modalities. In addition, temporal 40-Hz tACS impaired V performance, specifically in individuals who showed a performance advantage for V (relative to A). Gamma-frequency tACS may therefore modulate the ability of these individuals to benefit from recoding flashes into the auditory modality, possibly by disrupting cross-activation of auditory areas by visual stimulation. Our results support both theories, suggesting that vEAR may depend on disinhibition of normally occurring sensory cross-activation, which may be expressed more strongly in some individuals. Furthermore, endogenous alpha- and gamma-frequency oscillations may function respectively to inhibit or promote this cross-activation.
A Prediction and Prevention Model of Requirements Change driven by the Improvement of Project Teams with Case Studies

Requirements change management is a vital part of software project management. The existing literature on requirements change focuses on its technical aspects and is less concerned with the combination of personnel and technology. Few studies have focused on predicting and preventing changes in requirements and finding change predictors from the sociotechnical viewpoint. In this study, we examined prediction and prevention mechanisms of requirements volatility based on the analysis of change causes by stressing both the human and technical roles. Two case studies (a questionnaire and an interview) were done to validate the study. An online questionnaire was used to quantitatively analyze and explain the significance of human factors, including the emotional characteristics of developers. The result of the interview was applied to qualitatively illustrate the necessity and importance of effectively constructing and managing project teams to decrease the requirements change rate and enhance the software success rate. The prediction and prevention model established in this study was validated by a model called "3P + 2C", described in the interview. The paper concludes by suggesting to researchers four practical issues around the proposed model for further study.
Multiphase characterization of wild Vigna associated root nodule bacteria from Japanese subtropical islands unveiled novel high temperature resistant Bradyrhizobium strains having high symbiotic compatibility with soybean and mungbean

ABSTRACT Vigna riukiuensis plant – a rare type of vigna, found only in Taiwan and the islands of Okinawa prefecture, Japan – possesses intrinsic property of high level of salt and heat tolerance. To understand the diversity and identify suitable rhizobia, multiphase characterization of root nodule bacteria associated with V. riukiuensis grown in Ishigaki and Iriomote Islands of Okinawa prefecture was performed. Multigene phylogenetic analysis of housekeeping genes based on 16S rRNA gene sequences, 16S-23S rRNA gene internal transcribed spacer (ITS) and 23S rRNA gene sequences identified three main groups closely similar to Bradyrhizobium japonicum, B. elkanii and B. jicamae family. However, analysis of symbiotic nifH and nodD1 genes and their phylogenetic trees showed similar topology, having only few discrepancies in comparison to the housekeeping gene phylogeny. Interestingly, for some of the isolates having similarity with B. elkanii, growth was observed at 40°C, which exceed the highest record for B. elkanii to the best of our knowledge. All the isolates were observed to have the capability of forming root nodules and fix nitrogen in their original host plant V. riukiuensis and two other crops: soybean and mungbean. Most of the isolates showed similar or higher nitrogen-fixing capability in comparison with B. diazoefficiens USDA110 in V. riukiuensis and V. radiata (mungbean), and Iri 5/6 in V. riukiuensis, Iri 5/12 in soybean and Ishi 7/2 in mungbean showed highest acetylene reduction assay (in µmol/h/gm nodule dry weight) activity, which was significantly higher than B. diazoefficiens USDA110. In addition, six isolates attained higher soybean biomass production compared with B. diazoefficiens USDA110, suggesting high symbiotic compatibility with soybean. Among them, Iri 5/7 of B. elkanii group contributed 29% higher soybean biomass production than B. diazoefficiens USDA110 and could grow at 40°C, hence it could be a promising soybean inoculant in the tropics. Abbreviations ARA: acetylene reduction assay, ISR: intergenic spacer regions, ITS: internal transcribed spacer
Taking the bait: interrogation questions about hypothetical evidence may inflate perceptions of guilt

ABSTRACT During suspect interviews, police will sometimes ask about hypothetical incriminating evidence to evoke a cue to deception – a technique known as a bait question. Previous research has demonstrated such questions can distort peoples’ memory for what evidence exists in a case. Here, we investigate whether such memory distortion can also cause people to see the suspect as more likely to be guilty. Across three experiments, we find exposure to bait questions led to participants hold inflated views of a suspect’s guilt. Further, we demonstrate bait questions cause reliable, robust memory distortion, leading participants to believe non-existent, incriminating evidence exists. However, we found no evidence to support the speculated mechanisms for this inflation – namely, (1) that source monitoring errors could lead people to misremember false evidence as real evidence and (2) that bait questions provide ‘key evidence’ to fill in the gaps of an incomplete theory of a case. In sum, bait questions have the problematic potential to shift jurors towards guilty verdicts. We suggest future research directions on bait questions, including the need for different designs to clarify why bait questions inflate guilt, and recommend practitioners avoid the use of bait questions.
A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

Machine-learning techniques have received popularity in the intrusion-detection systems in recent years. Moreover, the quality of datasets plays a crucial role in the development of a proper machine-learning approach. Therefore, an appropriate feature-selection method could be considered to be an influential factor in improving the quality of datasets, which leads to high-performance intrusion-detection systems. In this paper, a hybrid multi-objective approach is proposed to detect attacks in a network efficiently. Initially, a multi-objective genetic method (NSGAII), as well as an artificial neural network (ANN), are run simultaneously to extract feature subsets. We modified the NSGAII approach maintaining the diversity control in this evolutionary algorithm. Next, a Random Forest approach, as an ensemble method, is used to evaluate the efficiency of the feature subsets. Results of the experiments show that using the proposed framework leads to better outcomes, which could be considered to be promising results compared to the solutions found in the literature.
Automatically applying a credibility appraisal tool to track vaccination-related communications shared on social media

Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Our aim was to estimate the proportion of vaccination-related posts on Twitter are likely to be misinformation, and how unevenly exposure to misinformation was distributed among Twitter users. Methods: Sampling from 144,878 vaccination-related web pages shared on Twitter between January 2017 and March 2018, we used a seven-point checklist adapted from two validated tools to appraise the credibility of a small subset of 474. These were used to train several classifiers (random forest, support vector machines, and a recurrent neural network with transfer learning), using the text from a web page to predict whether the information satisfies each of the seven criteria. Results: Applying the best performing classifier to the 144,878 web pages, we found that 14.4\% of relevant posts to text-based communications were linked to webpages of low credibility and made up 9.2\% of all potential vaccination-related exposures. However, the 100 most popular links to misinformation were potentially seen by between 2 million and 80 million Twitter users, and for a substantial sub-population of Twitter users engaging with vaccination-related information, links to misinformation appear to dominate the vaccination-related information to which they were exposed. Conclusions: We proposed a new method for automatically appraising the credibility of webpages based on a combination of validated checklist tools. The results suggest that an automatic credibility appraisal tool can be used to find populations at higher risk of exposure to misinformation or applied proactively to add friction to the sharing of low credibility vaccination information.
Role of orexin peptide system in emotional overeating induced by brain reward stimulation in fed rats

Introduction: The purpose of this work was to prove that the reaction of food self-deprivation in “fed up” rats is a suitable model for studying the emotional overeating in the experiment. Methods: The self-deprivation reaction, i.e. self-isolation of an animal from food during electrical self-stimulation of the brain, was studied in animals with food deprivation. To reproduce the self-stimulation of the lateral hypothalamus, the male Wistar rats were trained to press a pedal in a Skinner box. After training, the rats received food deprivation, then a feeder was placed in the Skinner box, and a conditioned food reflex was developed in rats within 5 days. Results and discussion: The food self-deprivation reaction was observed in the ”satiated” rats with a current intensity of 10% and above the threshold for self-stimulation. Hungry animals pressed the pedal for hypothalamic self-stimulation and took no notice of the feeding trough. Sulpiride, a dopamine D2 antagonist (5 and 20 mg/kg i.p.), administered to the “satiated” rats decreased both the eating behavior and self-stimulation in food self-deprivation testing. SB408124, an orexin A receptor antagonist (0.5 mg/ml, 20 μl intranasally) reduced only the number of pellets eaten, but not the number of pedal presses. Conclusion: The orexin A receptors are preferably involved in emotional eating compared with orexin B (OX2R TCSOX2-29) and D2 dopamine receptors. Because emotional eating is significantly related to clinical eating disorders, like bulimia and binge eating disorder, it seems promising to use drugs of the orexin system to treat and prevent the issue.
Automatic Classification of Pathology Reports using TF-IDF Features

A Pathology report is arguably one of the most important documents in medicine containing interpretive information about the visual findings from the patient's biopsy sample. Each pathology report has a retention period of up to 20 years after the treatment of a patient. Cancer registries process and encode high volumes of free-text pathology reports for surveillance of cancer and tumor diseases all across the world. In spite of their extremely valuable information they hold, pathology reports are not used in any systematic way to facilitate computational pathology. Therefore, in this study, we investigate automated machine-learning techniques to identify/predict the primary diagnosis (based on ICD-O code) from pathology reports. We performed experiments by extracting the TF-IDF features from the reports and classifying them using three different methods---SVM, XGBoost, and Logistic Regression. We constructed a new dataset with 1,949 pathology reports arranged into 37 ICD-O categories, collected from four different primary sites, namely lung, kidney, thymus, and testis. The reports were manually transcribed into text format after collecting them as PDF files from NCI Genomic Data Commons public dataset. We subsequently pre-processed the reports by removing irrelevant textual artifacts produced by OCR software. The highest classification accuracy we achieved was 92\% using XGBoost classifier on TF-IDF feature vectors, the linear SVM scored 87\% accuracy. Furthermore, the study shows that TF-IDF vectors are suitable for highlighting the important keywords within a report which can be helpful for the cancer research and diagnostic workflow. The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.
Earth observation in service of the 2030 Agenda for Sustainable Development

Abstract This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and reporting on progress towards the associated targets. This paper also highlights how the Group on Earth Observations (GEO) would contribute to ensure the actual use of EO in support of the 2030 Agenda; and how the Global Earth Observations System of Systems meets requirements for efficient investments in science and technology and a good return on investment, which is elaborated in the Addis Ababa Action Agenda on development financing. Through a number of examples, we first discuss how extensive EO use would: provide a substantial contribution to the achievements of the SDGs by enabling informed decision-making and by allowing monitoring of the expected results; improve national statistics for greater accuracy, by ensuring that the data are “spatially-explicit” and directly contribute to calculate the agreed SDG Targets and Indicators support the fostering of synergy between the SDGs and multilateral environmental agreements by addressing cross-cutting themes such as climate and energy; and facilitate countries’ approaches for working across different development sectors, which is, according to the special adviser on the 2030 Agenda, a key challenge to achieve the SDGs. We then focus on the role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships.
Robust Trajectory and Resource Allocation Design for Secure UAV-Aided Communications

This paper aims to enhance the physical layer security against potential internal eavesdroppings by exploiting the maneuverability of an unmanned aerial vehicle (UAV). We consider a scenario where two receivers with different security clearance levels require to be served by a legitimate transmitter with the aid of the UAV. We jointly design the trajectory and resource allocation to maximize the accumulated system confidential data rate. The design is formulated as a mixed-integer non-convex optimization problem which takes into account the partial position information of a potential eavesdropper. To circumvent the problem non-convexity, a series of transformations and approximations are proposed which facilitates the design of a computationally efficient suboptimal solution. Simulation results are presented to provide important system design insights and demonstrate the advantages brought by the robust joint design for enhancing the physical layer security.