Article Review: Medical Plants: Their Compounds and the Biotechniques for Identifying and Separation of Them

It was possible through this study to give a brief overview of the most important pharmaceutical compounds, which included glycosides, alkaloids, terpenes and their plant sources, which encourages the adoption of various separation techniques protocols to obtain multiple types of pharmaceutical compounds of high pharmacological value from medicinal plants compared to its standard compounds and thus the possibility of using it medically to cure many diseases and dispense with medicines and chemotherapy with multiple side effects and interactions. The study dealt with garlic, ginger and chamomile plants and their pharmaceutical compounds as examples of medicinal plants known since ancient times, research has confirmed its health benefits, as it supports normal body functions and the immune system

Polyoxometalate catalysis for oxidative desulfurization

Oxidative desulfurization removes sulfur-containing molecules from petroleum feedstocks to upgrade the quality of fuels. It is driven by environmental legislation demanding very low sulfur levels in fuels. The oxidative desulfurization technology usually involves oxidation of sulfur-containing molecules with an appropriate oxidant, e.g. hydrogen peroxide, organic peroxide, etc., followed by extraction of oxidation products from the petroleum feedstock. In this study, new efficient catalysts have been developed for the oxidation of benzothiophenes commonly found in diesel fuel, such as benzothiophene, dibenzothiophene and 4,6-dimethyldibenzothiophene, to the corresponding sulfones with hydrogen peroxide in two-phase system including immiscible aqueous and organic phases. These catalysts are based on polyoxometalates comprising Keggin type heteropolyanions [XM12O40]m- {X= Pv (m = 3) and Siv (m = 4) and phase transfer agents such as aminocyclotriphosphozenes or terminally functionalized polyisobutylene (PIB) oligomers. In this system, Keggin polyanions are transformed by excess H2O2 in aqueous phase to active peroxo polyoxometalate species which are transferred to the organic phase by the phase transfer agent where the oxidation of sulfur-containing molecules takes place

Assessment of Prevalence and Risk Factors of Obesity in Pediatric Age Group Between (5-15) years.

خلفية البحث : البدانة تعرف كمقياس كتلة الجسم بالنسبة للعمر والجنس وتساوي او اكثر من 95% وتعتبر واحده من اكثر المشاكل  الصحية العامة في الوقت الحالي .التصنيف الشائع والمقبول بالنسبة للوزن يعتمد على مقياس كتلة الجسم . حساب كتلة الجسم يساوي الوزن  بالكيلوغرام تقسيم الطول بالنتر المربع  . الاهداف : تقييم عوامل  الخطورة للبدانة لدى الاطفال الذين تتراوح اعمارهم بين  (5-15 ) سنه ولتحديد انتشار البدانة . تصميم البحث : دراسة مقطع عرضي للفترة من واحد نيسان 2017 ولغاية نهاية كانون الثاني 2018  .الدراسة شملت 1044 طفل تتراوح اعمارهم بين 5- الى 15 سنه الذي تم فيها فحص صحي في مدارس الابتدائية ورياض الاطفال لكلا الجنسين وقد تم استثناء الاطفال المصابين بأمراض الغدد الصماء والاطفال الذين يستخدمون علاجات الستيرويد ومضادات الاختلاجات ومضادات الاضطرابات العقلية وكل البيانات اعتمدت  على الوزن والطول والجنس لتحديد مؤشر الكتلة. النتائج : ان نسبة انتشار البدانة  في هذه الدراسة  هي  16.57%  وكذلك  حدد بعض  العوامل المسببه  للبدانه  وحددت بشكل هام  علاقة العامل  الوراثي  والسكن  وطريقة الغذاء  لدى الاطفال . الاستنتاجات : البدانه عند الطفال  تعتبر مشكله صحيه  في تزايد  في الدول التطوره  والناميه  . البدانه عند  الاطفال تؤدي الى  امراض  غير انتقاليه  ومشاكل نفسيه , وهذا بسبب انها  مشكله مهمه تحتاج الى  تحل  بالسرعه الممكنه  . بشكل عام  وزن الوالدين  محدد رئيسي لوزن الاطفال . تشجيع الغذاء الصحي  وتثقيف الاهل  حول تبعات البدانه على صحة الاطفال عند النضوج  واتباع  استراتيجيات  فعاله للسيطره على البدانه .Background. Obesity is defined as age- and sex-specific body mass index (BMI) at or above 95th percentile, it is one of the more pressing public health problems today. The common classification of weight is based on body mass index (BMI), calculated as the weight in kilograms divided by the square of the height in meters (kg/m2). We try to explain and limit the serious sequelae of this problem. Aim of study, determine the risk factors for obesity and its prevalence in childhood (5-15 years). Patients and Methods: Cross sectional study, from 1st of April 2017 to end of January 2018. Clusters of 1044 children aged 5 -15 years from primary and secondary schools, kindergarten and children admitted to the ward in Al-Diwaniya    were surveyed, excluding endocrine diseases and those using medications like steroid. Data on age, gender, height and weight, to determine BMI, has been measured in this study. Results: The overall prevalence of obesity in the current study is 16.57 %, also we determined some of risk factors of obesity and we found that obesity has significant association with family history, residence and dietary habit of the child. Conclusion: The study determined the overall prevalence of obesity which is 16.57%, also we determined family history, residence and dietary habit as significant risk factor for obesity in childhood

A stacked LSTM based approach for reducing semantic pose estimation error

© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories

Design of Dynamics Invariant LSTM for Touch Based Human-UAV Interaction Detection

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.Comment: 13 pages, 13 figures, submitted to IEEE access, A supplementary video for the work presented in this paper can be accessed from https://youtu.be/29N_OXBl1m

Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers

Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the computer-vision community and is serving as a key-enabler for a multitude of applications. This technology has offered significant advantages including reduced power consumption, reduced processing needs, and communication speed-ups. However, neuromorphic cameras suffer from significant amounts of measurement noise. This noise deteriorates the performance of neuromorphic event-based perception and navigation algorithms. In this paper, we propose a novel noise filtration algorithm to eliminate events which do not represent real log-intensity variations in the observed scene. We employ a Graph Neural Network (GNN)-driven transformer algorithm, called GNN-Transformer, to classify every active event pixel in the raw stream into real-log intensity variation or noise. Within the GNN, a message-passing framework, called EventConv, is carried out to reflect the spatiotemporal correlation among the events, while preserving their asynchronous nature. We also introduce the Known-object Ground-Truth Labeling (KoGTL) approach for generating approximate ground truth labels of event streams under various illumination conditions. KoGTL is used to generate labeled datasets, from experiments recorded in chalenging lighting conditions. These datasets are used to train and extensively test our proposed algorithm. When tested on unseen datasets, the proposed algorithm outperforms existing methods by 8.8% in terms of filtration accuracy. Additional tests are also conducted on publicly available datasets to demonstrate the generalization capabilities of the proposed algorithm in the presence of illumination variations and different motion dynamics. Compared to existing solutions, qualitative results verified the superior capability of the proposed algorithm to eliminate noise while preserving meaningful scene events

Factors Influencing Adoption of HR Analytics by Individuals and Organizations

In this paper, we explore the factors influencing the adoption of Human Resources (HR) Analytics by HR professionals in large Palestinian enterprises. A convenience sample of 151 HR professionals from the service and manufacturing sectors participated in a questionnaire-based survey. The study identified self-efficacy, performance expectancy, effort expectancy, resource availability, quantitative self-efficacy, data availability, and social influence as the most significant factors positively influencing individual acceptance and adoption of HR Analytics. Fear appeals, on the other hand, had no significant effect. The study proposes a conceptual framework to help policymakers in organizations understand how to adopt HR Analytics

Pose-graph neural network classifier for global optimality prediction in 2D SLAM

The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called PoseConv, to perform classification of pose-graphs as optimal or sub-optimal. The operation of PoseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model's capability to perform classification with 92 - 98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators