Miracle-dz/UniFAR-FTUs huggingface dataset:
Status of animal welfare research in zoos and aquariums: Where are we, where to next?

Research into the conditions that promote good animal welfare is essential to equip zoos and aquariums with the knowledge to create environments in which animals thrive. In order to collate the empirical information that is available regarding animal welfare in zoos and aquariums with regard to topics, methods and species, a systematic literature review was conducted of the primary peer-reviewed journals publishing zoo-based and welfare-based research. Journals included Animal Welfare, Animals, Applied Animal Behaviour Science, International Zoo Yearbook, Journal of Applied Animal Welfare Science, Journal of Zoo and Aquarium Research, and Zoo Biology. The literature review spanned 2008–2017 and revealed that 7.6% (n=310) of reviewed publications (n=4,096) in these journals were zoo- or aquarium-based and animal-welfare focused. The main topics studied included enrichment, social conditions and enclosure design, while understudied topics included the welfare of ambassador animals, and the welfare impacts of sound and light. Behaviour was by far the dominant welfare parameter used and the use of hormonal measures declined over this period. Taxonomic representation in these publications was notably skewed. Mammals were the focus of 75% of studies, and 82% of studies were vertebrate-focused (great apes being the dominant taxa). This study considers potential reasons for these patterns and highlights research areas for future emphasis that could serve to fill gaps in current knowledge regarding zoo and aquarium animal welfare, including more research into affective states that underlie an animal’s welfare status.
Efficient Controlling of Three-Phase AC-DC-AC Dual Converter Systems with Vector Control

In this work the well-known voltage and current DC interface converter systems, used to execute an AC/AC converter, are at first exhibited. Utilizing this learning and their space vector modulation techniques we demonstrate their association with the group of PWM Converters and after that at long last the association with direct PWM Converters. A short exchange of broadened PWM Converter circuits is given and another unidirectional three-level PWM Converter topology is proposed. This plainly demonstrates the topological associations of the converter circuits that specifically prompt a flexibility of the tweak techniques. KeywordAC-DC-AC converter, Pulse width Modulation (PWM), PFC Converter, Control Design.
Outcomes of mindfulness-based coaching for managers

ABSTRACT The integration of mindfulness into the field of executive coaching is unsurprising, given growing interest in mindfulness in the workplace. At present, the literature linking mindfulness with coaching tends to focus on the potential afforded by mindfulness techniques, while empirical assessments remain limited. The objectives of the current research are to establish preliminary understandings regarding perceived outcomes of coaching processes incorporating mindfulness techniques, and to assess the perceived outcomes of mindfulness-based coaching processes across different points of time. This study analyses the assessments of 14 Israeli professional managers, working in the education system and in the high-tech industry, of a 12-session coaching process incorporating mindfulness practices. The coachees recorded their perceptions of the coaching outcomes from each session, contemporaneously, in personal journals. In addition, coachees were interviewed twice – at the end of the coaching sessions, and two years later. The study’s findings show the benefits of mindfulness-based coaching for individuals across various domains of their lives, and its implications for coachees’ well-being and performance at work. They also show that these benefits emerge even after very short interventions, and that these benefits continue to be perceived as valuable two years after the intervention.
Evaluating parameters of econometric models with linear limitations and a rank deficient observation matrix

There considered the approach of the linear econometric dependences parameters estimating for the case of combining a set of special conditions arising in the simulation process. These conditions address the most important problems met in practice when implementing a series of classes of mathematical models, for the construction of which the matrix of explanatory variables can be used. In most cases the vectors that make up the matrix have a close correlation relationship; this leads to the need of performing calculations using a rank deficient matrix. There are also take place violations of the Gauss-Markov theorem condition. The list of above mentioned special conditions is augmented by the additional model parameters constraints. Cobb-Douglas's production function and the Solow model are known economic problems of this type. In this research the need to impose additional constraints on the model parameters is extended to a wider range of tasks. In general, the economic formulation of the problem with the specified features is presented. Known ways to solve these tasks are discussed. The authors’ approach proposed takes into account the whole spectrum of these features. This approach is based on the application of pseudorandom matrices and the use of singular matrix decomposition. The use of proposed mathematical tools makes it possible to improve the quality of estimating model parameters while using real economic processes data. The analytical definition is found for the parameter evaluating vector of a linear econometric model with all the above mentioned features. Analysis of the used definition provides determination of the conditions that the matrix must satisfy; this describes additional model parameters constraints. The term was also obtained to estimate the variance of a linear econometric model parameters vector. The results obtained can be used in machine learning systems in the implementation of problems of econometric dependencies or discriminant models. Ganna Gryshchenko Department of Economics and Mathematical Modeling PhD, Assistans Oleksandr Kutovyi Department of Mathematik Dr hab, Prof. Oleg Shutovskyi Department of Information Technologies for Design and Applied Mathematics PhD, Ass.Prof. Математика та статистика 4 ПІДВОДНІ ТЕХНОЛОГІЇ  2020 Вип. 10, 3-12 промислова та цивільна інженерія
Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.
A New Evolution Model for Weighted Directed Networks

The most of the recent models of directed weighted network evolution capture the growth process based on two conventional assumptions: constant average degree assumption and slowly growing diameter assumption. Such evolution models cannot fully support and reflect the dense power law and diameter shrinkage in the process of evolution of real networks. In this paper, a new evolution model, called BBVd, is proposed for directed weighted networks by extending BBV model with the idea of the Forest Fire model. In BBVd, new directed edges are established with probabilities computed based on in/our-strength of nodes, with dynamical evolution of weights for local directed edges. The experimental result shows that the generated networks using BBVd display power-law behavior for the node strength distributions, and moreover, it satisfies the densification power laws and has shrinking diameter.
Depthwise and Spatial Factorized Network: a Light-weight Network for Real-time Semantic Segmentation

Real-time semantic segmentation requires high quality output in relatively short computing time. The existing deep neural network model usually has complex hierarchical structure and a large number of parameters. For the application of real-time computing, the network has higher requirements for computing performance. To address the speed and accuracy trade-off, a novel structure, named Depthwise and Spatial Factorized Network (DSFNet) is proposed in this paper. In DSFNet, two important strategies are introduced. Firstly, Depthwise and Spatial Factorized Convolution is used to reduce the number of parameters in encoder network remarkably. After that, residual attention mechanism is utilized in decoder with a fast downsampling strategy to obtain sufficient receptive field. These two strategies make the process of upsampling more precisely and achieve better performance. Comprehensive experiments on Cityscapes dataset verify the effectiveness of the proposed DSFNet. The DSFNet achieves 63.08 mIoU at 58 FPS with residual attention mechanism.
Nematode Population Dynamics after Applications of Plant Extracts and Trichoderma Species as Soil Amendments in Tomato Field

Three different plant extracts ( Khaya ivorensis, Azadirachta indica and Daniella oleifera ) and two Trichoderma species ( T. harzianum and T. atroviride ) , singly and in combination, were assessed on nematode population changes and also in the control of plant parasitic nematodes on tomato under field condition during 2018 and 2019 planting seasons. During each trial, plant parasitic nematodes encountered in the field were Meloidogyne, Pratylenchus, Xiphinema and Helicotylenchus species. The experiment was randomized complete block design with 4 x 3 factorial scheme and five replications. Factor A being plant extracts ( Khaya ivorensis, Azadirachta indica, Daniella oleifera ) and control, and factor B being bio-control agents ( Trichoderma harzianum , T. atroviride ) and control, resulting in 12 treatments. Significant reductions were observed on the population of the plant parasitic nematodes at all treatment levels with neem plant extract, in combination with either T. harzianum or T. atroviride resulted in the greatest significant decrease. Applications of different plant extracts and Trichoderma species, singly and in combination, enhanced the growth and yield of tomato infected with parasitic nematodes.
The Cenozoic Evolution of Crustal Shortening and Left‐Lateral Shear in the Central East Kunlun Shan: Implications for the Uplift History of the Tibetan Plateau

The timing of crustal shortening and strike‐slip faulting along the East Kunlun Shan provides insight into the history of surface uplift and may constrain the time at which the Tibetan Plateau reached high elevations. We investigate a series of extensional basins and restraining bends along the Xidatan strand of the Kunlun strike‐slip fault, which provide an ideal setting to unravel the tectonic history of the northern plateau margin. We present new apatite (U‐Th)/He, apatite fission track, and zircon (U‐Th)/He ages and QTQt thermal modeling, 40Ar/39Ar fault gouge dating, and structural mapping from the central East Kunlun Shan. Our data suggest that the East Kunlun Shan experienced slow to negligible exhumation until late Cretaceous time, followed by an increase in rate by 65–50 Ma. Along with a ~47 Ma fault gouge age, we posit that the Paleocene–early Eocene was a time of crustal shortening along the northern plateau. Rapid exhumation along transpressional portions of the Xidatan fault initiated by 23–20 Ma, which we interpret as the local onset of strike‐slip faulting. An early Miocene transition from north‐south crustal shortening to left‐lateral shear along the East Kunlun Shan, the onset of normal and strike‐slip faulting in central and southern Tibet by 18 Ma, and lower crustal flow in eastern Tibet by 13 Ma suggest the establishment of orogen‐wide east‐west oriented extension and extrusion by the middle Miocene. The plateau‐wide shift in stress accommodation implies that high gravitational potential energy, and likely high elevation, was attained by the middle Miocene.
Comparison of Histogram Based Image Enhancement Methods on Iris Images

Iridology, which is an alternative diagnosis that links iris patterns, colour, tissue weakness, damage and other characteristics, which can obtain evidence about the patient’s systemic health. Iridology can be integrated with the best technology such as computer vision for accurate identification of abnormalities in various organs of the human body. By extracting information from iris image data. Image quality improvement is needed because often the images tested have poor quality, for example images experiencing lighting, noise (noise), the image is too dark or bright, the image is not sharp, and blurred. In this research, the iris image quality was improved by the method of HE, AHE and CLAHE. The results of the improvement of 40 iris images obtained an average value of MSE and RSME, the smallest of the three methods is the CLAHE method, so that the CLAHE method is best used for iris image improvement. Overall, based on the PSNR values, the three methods are good for enhancing image contrast because they have an average PSNR of more than 30dB.
Intact Ether Lipids in Trench Sediments Related to Archaeal Community and Environmental Conditions in the Deepest Ocean

Archaea play an important role in marine biogeochemical cycle; however, their phylogenetic distribution and lipid composition in the hadal zone (6–11 km water depth) are poorly known. Here, we analyzed archaeal membrane lipids and 16S rRNA gene sequences in sediments from Mariana Trench (MT), Massau Trench (MS), and New Britain Trench (NBT), varying from 1,560 to 10,840 m depth. Forty‐two intact polar lipids (IPLs) were identified, including glycerol dialkyl glycerol tetraethers (GDGTs), OH‐GDGTs, glycerol dialkyl diethers (GDDs), and archaeol (AR) with polar headgroups of monohexose (1G), dihexose (2G), trihexose (3G), and hexose‐phosphohexose (HPH). Compositional and spatial distribution patterns of archaeal lipids suggest benthic Thaumarchaeota as a major source for IPLs, consistent with the predominance of Thaumarchaeota genes (>80%). The redundancy analysis (RDA) based on lipid and 16S rRNA data separates samples into three groups: extremely deep water (MT), significant terrestrial influence (NBT 1, 4, and 6), and predominant marine influence (MS, NBT 2, 3, 7, and 10). 1G‐GDDs and 1G‐AR positively correlate with water depth, likely reflecting the adaptation of benthic archaea to elevated hydrostatic pressure or variation of archaeal community in trench sediments. Bathyarchaeota are more abundant in sediments receiving terrestrial input; this pattern was attributed to their capability of utilizing terrestrial organic matter as an energy source. Our study highlights important environmental influences (e.g., pressure and organic matter quality and quantity) on benthic archaeal community and archaeal IPL compositions, which should be considered when IPLs and core lipids are applied as chemotaxonomic markers and paleo‐proxies.
Selection of Potent Isolates from a Population of Alternaria Alternata , a Leaf Spot Pathogen of Poplar

Poplar, an important tree in the agri-silvicultural system, is propagated mainly through cuttings to maintain genetic purity. Monocultures of poplar clones are amenable to many diseases as they have a narrow genetic base. Pathogenic populations have variability in terms of pathogenicity and virulence which are governed by its genetic makeup. Mapping the variability and selection of potential pathogenic isolates for breeding disease resistance remains a challenge. During the survey conducted in poplar nurseries located at different geographical sites, altogether 72 isolates of Alternaria alternata, were collected from four commercial clones of P. deltoides. Three selection methods were attempted to select fifteen potent A. alternata isolates based on growth rate, sporulation and spore size (maximum length and maximum breadth). Initially, Rough Gauging Method which is simply based upon index of sum of the character’s values and Equal Class Interval Method which depends upon the index of class interval scores were applied. To overcome the limitations of the above two methods, Unequal Class Interval Method was proposed based on Coefficient of Variation for each character assessed. The index was constructed using the geometric rather than arithmetic mean as the former normalizes the range, so that, no range dominates the scores assigned to the characters. The proposed method is recommended for the situations when the criterion variable depends upon various growth characters having inherent significant variation among each other.
A silver/silver chloride woven electrode with convex based on electrical impedance tomography

Abstract Advanced wearable medical devices are widely used for health monitoring, i.e. recording and analyzing signals from human organs. However, the signal quality is influenced significantly by medical electrodes, which became the bottleneck for health monitoring due to unstable impedance under long-term daily usage. Here, a novel monitoring electrode was designed with uniform micro-convex shape to increase the impedance reliability of electrode-skin interface in long-term monitoring and under dynamic conditions, and we set up the electrode-skin interface model to describe the electrode-skin interface and impedance distribution clearly. Furthermore, we investigated how the electrodeposition factors affect the electrode deposition uniformity and electrode resistance. In this study, we compared the monitoring performance among two sets of electrodes: 1) Ag electrodes and Ag/AgCl electrodes, 2) plain Ag/AgCl electrodes, jacquard Ag/AgCl electrodes, and commercial wet electrodes. Specifically, the monitoring performance was quantified by the electrode-skin interface impedance during long-term monitoring, under different dynamic conditions. The optimal electrodeposition process was a ‘small step’ multi-potential method with the 1-1.5-2 V mode. The jacquard Ag/AgCl electrodes presented the best monitoring performance under all three measurement scenarios across four types of electrodes. The results indicated that the proposed electrodes could be used for long-term medical monitoring under daily life activities based on electrical impedance tomography.
A Study of Different Disease Detection and Classification Techniques using Deep Learning for Cannabis Plant

: In this paper, different models for disease detection and classification are studied for cannabis plants. Cannabis plants are used for medical and recreational purposes with its recent legalization in some places. Cannabis farmers face problems in cultivation of the crop since it’s susceptible to multiple disorders. With early detection of the disease in the crop it is possible to prevent large waste of yield in the crop. A real dataset is considered for disease detection and classification purposes which is a combination of text and image data and that has been collected over a period of one and a half years (Feb 2018-August 2019). The models used in this study are Fast Region Convolutional Neural Network(F-RCNN), MobileNet Single Shot Multibox Detector(MobileNet-SSD), You Only Look Once(YOLO) and Residual Network-50 Layers (ResNet50). It is found that the MobileNet-SSD provided the best accuracy amongst all the object detection models that are studied and has a lesser training time as well. ResNet 50 is used for identifying the number of images required for a good fit without having to label first and then studied for the object detection models.
Objective Quality Assessment of Multi-Resolution Video based on H.264/AVC and H.265/HEVC Encoding

This study aims to carry out an objective assessment by evaluating the encoding performance of the new standard compared to its predecessor H.264/AVC. In addition, to produce a comprehensive investigation, we compared several resolutions (1080p, 720p, and 480p) with a combination of CRF and encoder presets. To find out the performance of both techniques we used several test parameters such as encoding duration, compression ratio, bitrate, MSE, and PSNR. The results showed that H.264 was faster in terms of the time needed to carry out the encoding process. However the compression ratio and bitrate of H.265 was better with a difference of 38.5% and 52.7% respectively. Finally, if the user wants to prioritize better quality video output without having to sacrifice a lot of compression time and small file size, then our recommendation is to use H.265 and by HD resolution configuration (720p) with medium presets and 18 CRF value.
Maternal age ? 35 years, Nulliparity, High Blood COHb Levels, and Low Serum Nitric Oxide Levels Increased Risk of Preeclampsia

Objective: To find out the risk factors for preeclampsia. Materials and Methods: a case-control study was between March 2018 and December 2018. Pregnant women who with gestational age of at least 22 weeks live in residential areas, exposed to smoke from combustion of tile or brick were divided into two groups. Cases were those diagnosed with preeclampsia. Controls were normotensive pregnant women who had a history of preeclampsia in previous pregnancy. CO (carbon monoxide) exposure in pregnant women was determined by examining COHb levels in the blood. Serum Nitric Oxide levels were measured using the ELISA (Enzyme-Linked Immunosorbent Assay) method. Data were analyzed by Chi-square test or Fisher’s exact test, Independent T-Test and regression analysis. Adjusted odds ratio (ORs) with 95% confidence interval (CI) was calculated. Results: The results of Blood COHb levels were significantly higher in preeclampsia (4.58±1.2) than the case of control group (2.85±0.4). Serum nitric oxide levels in preeclampsia were significantly lower in preeclampsia (18.28±4.1) than the case of control group (35.15±7.2). Maternal age ? 35 years, nulliparity, high blood COHb levels > 3.5%, and low serum Nitric Oxide levels < 25 ? mol/L were significantly associated with increased risk of preeclampsia. Conclusion: Low serum Nitric Oxide levels (< 25 ? mol/L) due to exposure to CO from the smoke of combustion of tiles or brick carried an increased risk of developing preeclampsia (ORs 25.5;95% CI 4.3- 29.7). These results can help health workers in pregnancy care counseling and prevention of preeclampsia in pregnant women located in areas exposed to carbon monoxide from the smoke of combustion of tiles or brick