Miracle-dz/UniFAR-FTUs huggingface dataset:
Vascular epiphyte diversity in two forest types of the “El Cielo” Biosphere Reserve, Mexico

We assessed the diversity, vertical distribution, and the effect of structural and geographical factors on the diversity of vascular epiphyte assemblages (VEAs) in two forest types in the “El Cielo” Biosphere Reserve. We found 2839 epiphyte individuals from 15 species on 240 host trees (27 species) in the semideciduous forest and 5085 individuals from 8 species on 215 host trees (12 species) in the pine–oak forest. Epiphytes showed a preference for the middle zone of the host trees. The diversity of VEAs was uneven in both forest types, although the VEA in the semideciduous forest was the most diverse. VEAs across forests were slightly similar in their composition (30% mean overlap), but very different in their structure (i.e., they did not share very abundant species). The variation in species richness among VEAs was influenced by forest structure, independently of forest type. Compositional differences of VEAs among forest types were explained by geographic distance and host-tree identity (31% and 28% v.
Genetic parameters and efficiency of early selection for half rotation-aged growth and form traits in Pinus taeda in China

Abstract Genetic parameters for height (H), diameter at breast height (DBH), stem straightness (STR), and under crown clear bole height (CH) of loblolly pine (Pinus taeda L.) were estimated for 255 families (209 open pollinated (OP) and 46 controlled pollinated (CP) families) using a family model and an individual tree model at age 1, 2, 3, 5, 11, and 15 years. Heritability estimates for growth traits of individual trees at age 11 years were the highest (0.17-0.78), and those at age 15 years were the lowest (0.05-0.74). Heritability estimates for DBH, STR, and CH were lower than those for H. Genetic correlations between H and DBH were generally strongly positive, attained a maximum values at age 2 to 3, and declined slightly thereafter. The genetic correlations between CH at age 11 and both H and DBH at different ages were moderate. Age-age genetic correlations for growth traits were moderate to high (0.56-0.91) at age 5 for half-rotation age (15 years), indicating the opportunity exists for early selection. Indirect selection from the age 5 to 11 years for H and DBH could be expected to produce gains of over 50% and 35% respectively, for these two ages, relative to direct selection at age 15. Efficiencies of early selection for H and DBH indicated that growth at maturity could be improved by early selection.
COMBINED QUADRIC ERROR METRIC AND LIFTING SCHEME MULTIVARIATE MODEL SIMPLIFICATION

The quadric error metric incremental simpliﬁcation process involves two stages: cost computation and vertex estimation. In this work, we replace the latter through the use of a lifting scheme prediction-update stage. Since the initial simpliﬁcation algorithm implies the fusion of neighboring vertices and their associated matrices, we propose the inclusion of a similar mechanism in the update stage of the lifting scheme. For this purpose, we explore two possible choices for the redistribution weights of the quadric error matrices. The quality of the data downsampled in this fashion is assessed by tracking the evolution of the normalized root mean squared error.
Pharmacognostical and phytochemical studies on some immunity booster herbs used in Unani system of medicine

Unani Medicine offers a number of drugs of plant origin to strengthen and enhance the body’s immune system. Having a strong immunity has always been considered important to stay healthy, but it has become even more important in the present scenario, when the whole world is dealing with highly contagious new strains of Corona virus. Strong immunity is the body’s first line of defence which protects it from disease causing virus, bacteria and germs. Unani Medicines play a vital role and hold great importance to improve body’s immune health. Present study deals with the scientific evaluation of some immunity booster Unani Medicinal plants (Berg-e-Rehan, Zanjabeel, Fifil Siyah, Gilo and Haldi) for their pharmacognostical characters, physico-chemical parameters such as ash values, extractives values, thin layer chromatography (TLC), HPTLC, heavy metal estimation, aflatoxin and microbial contamination.
Long-term Opioids Linked to Hypogonadism and the Role of Testosterone Supplementation Therapy

Opioids play a pivotal role in managing chronic pain with increasing prescription rates over the last few years. Hence, it is crucial to focus on the adverse effects of narcotics, and one of the lesser-known side effects is hypogonadism. Opioids act on the hypothalamus, pituitary, and directly on the gonads affecting serum testosterone levels. Narcotic-induced androgen insufficiency contributes to sexual dysfunction, infertility, hyperalgesia, and involving various body functions overall, affecting the quality of life. Opioid-induced hypogonadism is very challenging to diagnose for the clinicians, as the patients often under-report the symptoms. There are no established guidelines to analyze androgen insufficiency and dealing with their manifestations successfully. We did a substantial search in PubMed and Google Scholar, using various combinations of keywords to collect data to evaluate the impacts of opioids on serum testosterone levels. This study aims to highlight the clinical significance of opioid-induced androgen deficiency and the diagnostic techniques to recognize and credible treatment alternatives, including testosterone replacement therapy. Health care providers should screen the patients routinely for the signs and symptoms and monitor them often for the hormonal changes to select the patients cautiously for testosterone replacement therapy.
IMPROVED SAUVOLA THRESHOLD FOR BACKGROUND SUBTRACTION ON MOVING OBJECT DETECTION

Image Segmentation is one essential processing on moving object detection. The one of common segmentation methods is thresholding. In this paper, Thresholding method based on adaptive local technique using local mean and standard deviation is known as ‘WAN’ method. WAN has been inspired by the Sauvola’s binarization method and exhibits its robustness and effectiveness when evaluated on low quality document images. The objective of the WAN to enhance the sauvola method and to get a better binarization result and enhance the accuracy. This research aims to produce output value of WAN algorithm. WAN would be compared to other existing adaptive local method like sauvola and niblack. This research is implemented by using matlab and four videos original from camera. The best result calculation error (MSE,PSNR) of WAN method are (0.0011, 53,6655). Overall, the result of WAN method in this paper is more effective and efficient than the other existing method based on MSE and PSNR.
Problems of identification in information systems

The subject of this article is exploration of methods for identifying the status of a computer system. The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer system anomalous state by analyzing operating system events. The methods used are artificial intelligence, machine learning and ensemble classification methods. The following results were obtained: the methods of identifying the computer systems anomalous state based on ensemble methods were investigated, namely, bagging, boosting, and classifiers: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump to identify a computer system anomalous state. The different classifiers set and classifiers ensembles were developed. Training and cross-validation on each algorithm was performed. The developed classifiers performance has been evaluated. The research suggests an ensemble method of a computer system state classifying based on the J48 decision tree algorithm. Conclusions. The scientific novelty of the obtained results consists in creating an ensemble method for classifying the state of a computer system based on a decision tree, which makes it possible to increase the reliability and speed of classification.
A New Evaluation Technique Through Serious Games for Children with ASD

Assessment at preschool age plays a crucial role in the process of learning especially for children with autism spectrum disorder (ASD), whom encounter communication difficulties as exchanging thoughts or information by speech, writing or nonverbal expression. Therefore, a welldesigned assessment method is necessary in order to guide learning and serve as a powerful tool to reinforce children's motivation to learn. Information about what a child with ASD knows, understands and is able to do is used by tutors to adjust their teaching strategies. All assessment methods have different purposes before, during or after instruction. Besides, formative assessment is the most recommended type that helps improving children's learning and it takes place during the learning process. Also, serious games provide a context for assessing a broader range of skills and constructs compared to traditional assessment approaches. In this article, we propose a new technique for evaluating the degree of concept's assimilation of children with ASD through serious games. It allows identifying concepts that children are struggling to understand and skills they are having difficulty acquiring.
Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles

Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (N = 72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (N = 105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management.
Increase Employee Morale Through Transformational Leadership, Employee Relation, and Work Environment

This aims of this study is to determine the effect of transformational leadership, employee relations, and work environment on employee morale on employees of the Department of Trade, Cooperatives, and SMEs Magelang Regency. The type and design of research used in this study is quantitative research. Sampling in this study uses a saturated sampling technique and obtained a sample size that must be used is 130 employees. The analytical method used is the instrument test (validity and reliability), the classic assumption test, and the hypothesis test with the application of SPSS 22. Data collection methods using interviews, questionnaires, and literature studies. The results of this study indicate that three hypotheses were accepted. So it can be seen that transformational leadership has a positive and significant effect on employee morale. Employee relations have a positive and significant effect on employee morale. The work environment has a positive and significant effect on employee morale. Transformational leadership, employee relation, and work environment together has a positive and significant effect on employee morale. Article Information Article History: Received January 2020 Approved February 2020 Published March 2020
Soil salinity inhibits plant shade avoidance

Global food production is set to keep increasing despite a predicted decrease in total arable land [1]. To achieve higher production, denser planting will be required on increasingly degraded soils. When grown in dense stands, crops elongate and raise their leaves in an effort to reach sunlight, a process termed shade-avoidance [2]. Shade is perceived by a reduction in the ratio of red (R) to (FR) light and results in the stabilisation of a class of transcription factors known as PHYTOCHROME INTERACTING FACTORs (PIFs) [3,4]. PIFs activate the expression of auxin biosynthesis genes [4,5] and enhance auxin sensitivity [6], which promotes cell wall loosening and drives elongation growth. Despite our molecular understanding of shade-induced growth, little is known about how this developmental programme is integrated with other environmental factors. Here we demonstrate that low levels of NaCl in soil strongly impair the ability of plants to respond to shade. This block is dependent upon abscisic acid (ABA) signalling and the canonical ABA signalling pathway. Low R:FR light enhances the expression of a positive regulator of the brassinosteroid (BR) signalling pathway, BRASSINOSTEROID SIGNALLING KINASE 5 (BSK5). We found that ABA inhibits BSK5 up-regulation and interferes with GSK3-like kinase inactivation by the BR pathway, thus leading to a suppression of PIF function. By demonstrating a link between the ABA and BR-signalling pathways this study provides an important step forward in our understanding of how environmental cues are integrated into plant development.
Mass reservoirs surrounding massive infrared dark clouds

Context. Infrared dark clouds (IRDCs) harbor progenitors of high-mass stars. Little is known of the parental molecular clouds of IRDCs. Aims. We demonstrate the feasibility of the near-infrared (NIR) dust extinction mapping in tracing the parental molecular clouds of IRDCs at the distances of D ≈ 2.5−8 kpc. Methods. We derive NIR extinction maps for 10 prominent IRDC complexes using a color-excess mapping technique and NIR data from the UKIDSS/Galactic Plane Survey. We compare the resulting maps to the 13CO emission line data, to the 8 μm dust opacity data, and to the millimeter dust emission data. We derive distances for the clouds by comparing the observed NIR source densities to the Besançon stellar distribution model and compare them to the kinematic distance estimates. Results. The NIR extinction maps provide a view of the IRDC complexes over the dynamical range of AV ≈ 2−40 mag, in spatial resolution of ∼30′′ . The NIR extinction data correlate well with the 13CO data and probe a similar gas component, but also extend to higher column densities. The NIR data reveal a wealth of extended structures surrounding the dense gas traced by the 8 μm shadowing features and sub-mm dust emission, showing that the clouds contain typically 10 times more mass than traced by those tracers. The IRDC complexes of our sample contain a relatively high amount of high-column density material, and their cumulative column density distributions resemble active nearby star-forming clouds like Orion rather than less active clouds like California. Conclusions. The NIR dust extinction data provide a new powerful tool to probe the mass distribution of the parental molecular clouds of IRDCs up to the distances of D ∼ 8 kpc. This encourages deeper NIR observations of IRDCs, because the sensitivity and resolution of the data can be directly enhanced with dedicated observations. In addition to mass distributions, the NIR data provide relatively reliable distance estimates.
BENCHMARKING OF CONVOLUTIONAL NEURAL NETWORKS FOR FACIAL EXPRESSIONS RECOGNITION

Facial Expression Recognition (FER) is one of the most interesting problems in computer science due to its potential applications in AI, many studies were proposed for the FER, but it based on traditional machine learning techniques and these techniques do not have the generalizability to classify expressions from unseen images or those that are captured from the wild, so it is still a diﬃcult and a complex problem. Recently, trends of research in various ﬁelds have begun to transfer to deep learning techniques, since it can learn and capture features automatically, robustness to natural variations in the data and generalizability. This work presents a comparative analysis of the popular convolutional neural network (CNN) models based on modular CNN architectures such as ResNet, DenseNet, MobileNet, NASNetMobile, Inception and Xception, applied on FER problem. The purpose of the paper is benchmarking the best architecture models, in order to help researchers to explore and investigate the best architectures for future research in FER based CNN models. For the comparative analysis multiple metrics were used such as Accuracy, Loss, precision, recall, number of parameters and model size, to conduct experiments facial expressions dataset was used from the AffectNet dataset with 287,651 images for training and 4000 images for validation represent
Cross-disciplinarity in the advance of Antarctic ecosystem research

The biodiversity, ecosystem services and climate variability of the Antarctic continent and the Southern Ocean are major components of the whole Earth system. Antarctic ecosystems are driven more strongly by the physical environment than many other marine and terrestrial ecosystems. As a consequence, to understand ecological functioning, cross-disciplinary studies are especially important in Antarctic research. The conceptual study presented here is based on a workshop initiated by the Research Programme Antarctic Thresholds – Ecosystem Resilience and Adaptation of the Scientific Committee on Antarctic Research, which focussed on challenges in identifying and applying cross-disciplinary approaches in the Antarctic. Novel ideas and first steps in their implementation were clustered into eight themes. These ranged from scale problems, through risk maps, and organism/ecosystem responses to multiple environmental changes and evolutionary processes. Scaling models and data across different spatial and temporal scales were identified as an overarching challenge. Approaches to bridge gaps in Antarctic research programmes included multi-disciplinary monitoring, linking biomolecular findings and simulated physical environments, as well as integrative ecological modelling. The results of advanced cross-disciplinary approaches can contribute significantly to our knowledge of Antarctic and global ecosystem functioning, the consequences of climate change, and to global assessments that ultimately benefit
Governmental Control of Digital Media Distribution in North Korea: Surveillance and Censorship on Modern Consumer Devices

Modern devices like PCs and tablet PCs enable users to consume a wide range of media like videos, audio and documents. Introducing such devices in repressive regimes like North Korea [10] (ofﬁcially Democratic People’s Republic of Korea, DPRK) contradicts the ob-jective of controlling and suppressing information within the country and particularly information imported from the outside world. This can be generalized as any information that has not been reviewed and approved by the government. This paper is an effort to evaluate the technical challenges that arise while enabling users to consume or create potentially unwanted media and analyzes two media-controlling mechanisms developed by North Korean government organizations. The analysis covers implementations found in Red Star OS, a Linux-based operating system developed by the Korean Computer Center, and Woolim, an North Korean tablet PC that is based on the Android operating system. We will conclude about the effectiveness and implication on the distribution of digital media within North Korea and how these mechanisms have evolved in the analyzed products over the past several years.
Acceptance Towards Smile Makeover Based on Spa Factor- A Myth or Reality

Maxillary anterior teeth are of utmost importance as their size and form play an important role not only in facial aesthetics but also dental aesthetics. Aesthetics is one of the primary concerns of patients seeking restorative treatment. Due to this reason, dentists restore maxillary anterior teeth in harmony with the facial appearance of the patients. The aim of the study is to evaluate the incorporation of SPA factor in Fixed dental prosthesis and prevalence of different personalities and its correlation with gender and age. A Cross-sectional, descriptive study was conducted in an institution, on randomly selected individuals. The study group consisted of adult patients who attended theoutpatientDepartment of Prosthodontics from June2019-March 2020. Data collection was done and the Chi-square test was used to explore the relationship between two variables.41% of males accepted treatment. 46% of females accepted treatment. 72% Treatment acceptance was found in the young age group S factor was not followed by 17.8% case, P factor was not followed by 17.3%case, A factor was not followed by 20.4%case SPA plays an important role in the esthetic outcome of the prosthesis, so it should be incorporated in the treatment. More awareness and knowledge is needed for implementation clinically in each and every case. Young adults aremore likely to accept esthetic smile makeover treatment, whereas other age groups are not. Accepting maybe because of socioeconomic status or lack of awareness. So it’s our responsibility to conduct health education and awareness programs at the society level for patients and also clinical awareness among practitioners to improve the smile quality.
Finite Element Investigation of Thermal Distribution in Fretting Fatigue

1Mechanical Engineering department, Kashan University, Kashan, Isfahan, Iran. ------------------------------------------------------------------------***----------------------------------------------------------------------Abstract: A finite element model is developed for investigation of temperature development of fretting between a cylinder and flat surface. The model is based on the partial sliding between the surfaces and the friction in the sliding region is considered the source of heat generation as a form of surface heat flux acting on the boundary between the contacting parts. The results show that temperature rise around the contact can be used as a tool to predict the friction force.