Miracle-dz/UniFAR-FTUs huggingface dataset:
DHPS_A_241413 41..48

Department of Pharmacy, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia Background: Poisoning causes significant patient morbidity and mortality worldwide. It is a common reason for emergency department visits and hospitalizations. Acute poisoning needs immediate effective management to prevent patient mortality or sequela. This study was carried out to determine the pattern of acute poisoning cases and their management at the emergency department of Dessie referral hospital, northeast Ethiopia. Methods: A retrospective cross-sectional study of all registered poisoning cases managed at the emergency department of Dessie referral hospital was conducted from March 10 to May 2, 2018. Results: There were a total of 147 registered poisoning cases listed in the registry during the study period, of which 120 cases (81.6%) had complete data and were included in the study. Among the total of 120 studied poisoning cases, 66 (55%) were females, and 53 (44.2%) were in the age group of 21–30 years. The causative poison was documented for 118 cases. Organophosphates were the most common poisoning agents involved in 54 (45%) of the cases followed by sodium hypochlorite, 27 (22.5%), and food poisoning, 19 (15.8%). Among the total patients, 77 (64.2%) were self-poisoned intentionally, 31 (25.8%) were poisoned in an unintentional manner and the rest, 12 (10%), had an unknown manner of poisoning. Mental disorder, 25 (20.8%); family disharmony, 23 (19.2%); and marital disharmony, 19 (15.8%) were the three most common causes of intentional poisoning. In all cases of acute poisoning, both pharmacological and non-pharmacological treatment approaches were used. Cimetidine was the most commonly used pharmacologic treatment, 118 (98.3%), followed by antiemetic, 107 (89.2%); proton pump inhibitor, 87 (72.5%), and atropine, 67 (55.8%). ANOVA did not show a statistically significant difference (P>0.05) in the frequency of poisoning cases across seasons. Conclusion: Among 120 acute poisoning cases, 77 (64.2%) were intentional poisoning cases. Organophosphate poisoning accounts for 45% of the total poisoning cases. The three major reasons for intentional poisoning were mental disorders (20.8%), family disharmony (19.2%) and marital disharmony (15.8%). Cimetidine (98.3%) was the most commonly used pharmacologic treatment of the poisoning cases.
ImprovedNatural-Air-Convection-Cooling Formulas forMediumFrequencyTransformer Design Optimization

This paper provides accurate analytic models for a computationally efficient estimation of the natural convection cooling coefficients for typical geometries, such as those encountered in transformers. Proper thermal coordination is an essential step of any transformer design, especially when it comes to medium frequency operation, where the cooling surfaces are greatly reduced due to the frequency scaling. Available empirical formulas, derived for simple geometries, have a limited applicability to such complex structures. In this work, analytic models are derived based on extensive finite element method simulations that accurately describe the convective heat transfer of the geometries of interest. The proposed models improve accuracy substantially compared to the available models and have several orders of magnitude faster execution referred to finite element method simulations.
DEEP ANALYSIS OF DETECTION AND IDENTIFICATION OF SUSPECT FACE USING DEEP LEARNING IN EDGE AND CLOUD ENVIRONMENT

In recent days, there was a very big challenge in surveillance, mainly due to the increase in crime rates. In our society, safety is one of the basic needs of every human being, so we need a security framework that could help us, to protect from criminals. Now a day, security surveillance was used for the identification of suspect and criminals, but there were some difficulties in the identification of criminals, quickly. In the proposed system, continuous monitoring, detection and identification of suspicious person in the public area through artificial intelligence and Deep Learning along with Edge-Could computing will be easy for identification and tracking of the suspect. Continuous analysis of video footage in the edge-cloud environment will reduce the complexity of the identification of suspicious person. In any case, therefore we need a more intelligent camera to gather human identity and movement. The Haar-cascade classifier and MSCNN are used to detect and identify suspect face in the edge-cloud environment. In this system, from the surveillance video, each and every frames were analyzed to detect the maximum no of human faces using the Haar-Cascade classifier and to calculate the time taken for the detection, recognition and overall time taken for the process completion. The identified human can be analyzed furthermore in the edge and cloud network for tracking the details of the identified human movement on the surveillance system.
Design and Simulation of Hybrid System using Cuckoo MPPT Technique

In contrast to present scenario, the renewable energy sources are accessible at no expense and produce power environmental friendly. Around early afternoon the WPS fulfills its heap and gives extra energy to the capacity or to the grid. On location energy generation is without a doubt went with minimization of ecological contamination, decrease of misfortunes in power system transmission and distribution. The main theme of this paper is to propose a demonstrating and planning of grid interfaced hybrid solar-wind energy system. This is an issue particularly in little powerful system because of the limitation on the inverter markets. Inverters which are used in these sorts of energy systems work on grid or off grid. In this investigation, a novel power the board methodology has been created by structuring a wind-PV mixture system to work both as a self-ruling system and as a grid-associated system. The structured Power Management Unit performs estimation from different focuses in the system and as per this estimation; it gives a compelling energy exchange to burdens and grid. The steadiness of the smaller scale grid, power quality and voltage direction is checked by Matlab and test results. This paper also presents a concept for effective utilization of Distribution Generation systems as a part of smart grid environment, to improve the system reliability by providing effective generation under islanding mode, and also to provide controlling services in the grid-tied mode. To meet this limitations, this proposed flexible system is implemented a control technique with power-voltagecurrent parameters.
Detection of honey adulteration by near infrared spectroscopy coupled with random forest method.

Objective To study honey adulteration by near infrared spectroscopy-chemometrics method.Methods Transflectance mode with integrating sphere was used to collect NIR spectra.Savitzky-Golay first derivative method was employed to preprocess the raw NIR spectra.A Random Forest model was established based on the correlation between the NIR spectra and reference value.Results The prediction accuracy of the proposed method was 100% for training samples and 95% for test samples.Conclusion The proposed method is fast and non-destructive,and it provides a novel efficient and environmental friendly approach for the rapid qualitative detection of honey adulteration.
A CONVOLUTION NEURAL NETWORK BASED SMART INTELLIGENT WEED DETECTION SYSTEM

Artificial intelligence, explicitly deep learning, is a quickly developing examination field today. One of its different applications is object acknowledgment, utilizing PC vision. The blend of these two innovations prompts the motivation behind this proposal. In this paper, a system for the identification of different crops and weeds implemented. Weed management is one of the costliest input to the agriculture and its one of the unmechanised area. Weeds are the plants, that normally growing in a incorrect position which participate with crop for water, luminosity, nutrients and room, causing decrease in yield. It can also cause a trouble in agriculture. Weed picking is not the cost effective because of the laborious job in fields. Weeds can also crowd pests and diseases that can extend to cultivated crops. The most important step in this area is to bring mechanization is to detection of weed in agricultural field. Weed can be detected by using convolution neural network in opencv. Convolution neural network uses special image processing techniques. Weed in agricultural field can be detected by its properties such as Size, Shape, Spectral Reflectance, Texture features. In this paper we are demonstrating weed detection by its Size and colour features.
Decoupled dynamic model of an aerial manipulator

In aerial manipulators, precision may be jeopardized by the forces and torques that the manipulator transfers to the UAV. In this research, a simplified decoupled dynamic model of a UAV and a 1-DOF manipulator is developed and validated against a coupled dynamic model. Then, a decoupled model is proposed for a 3-DOFs manipulator. With both the decoupled and coupled model, a lateral displacement of the system is generated during the manipulation in hovering flight, which needs to be compensated. Keywords—UAV, aerial manipulation, robot
The role of betaFtz-F1 in the dissociation of the fat body of Drosophila melanogaster during the prepupal/pupal transition

Larval fat body dissociation is a major event during the metamorphosis of Drosophila melanogaster. The dissociation of the fat body results in a separation of fat tissue into individual cells distributed throughout the body and occurs at the same time as many other changes in the morphology of the fly. Despite detailed studies of the morphological changes that occur in the fat body, there is still much unknown concerning the genetic regulation of this event. Thus, the focus of my work is on possible genes controlling the transition of the fat body to individual fat cells. Many of the events of larval fat body dissociation occur simultaneously to events controlled by βftz-f1, such as abdominal muscle contractions and head eversion. By ectopically expressing βftz-f1 using the UAS/Gal 4 system, I observed the levels of RNA expression of βftz-f1 and the early genes E93, E74A, and BR-C. The results indicated that, while βftz-f1 was ectopically expressed, the early genes are most likely not being expressed in the fat body.
Malicious Intrusion Detection Using Machine Learning Schemes

Wireless networks are continuously facing challenges in the field of Information Security. This leads to major researches in the area of Intrusion detection. The working of Intrusion detection is performed mainly by signature based detection and anomaly based detection. Anomaly based detection is based on the behavior of the network. One of the major challenge in this domain is to identify and detect the malicious node in wireless networks. The intrusion detection mechanism has to analyse the behavior of the node in the network by means of the several features possessed by each node. Intelligent schemes are the need of the hour in such scenario. This paper has taken a standard dataset for studying the features of the wireless node and reduced the features by applying the most efficient Correlation Attribute feature selection method. The machine learning algorithms are applied to obtain an effective training model which is then applied on the testing dataset to validate the model. The accuracy of the model is determined by the performance parameters such as true positive rate, false positive rate and ROC area. Neural network, bagging and decision tree algorithm RepTree are giving promising results in comparison with other classification algorithms.
Family-Owned Businesses in Saudi Arabia: Challenges and Solutions from a Legal Perspective

Article history Submitted: 16 December, 2020 Revised: 22 December, 2020 Accepted: 24 December, 2020 Abstract Performance of family-owned businesses has been widely addressed in the context of developed countries. However, limited studies have been conducted in emerging economies. The purposes of this paper were to analyses the significant challenges facing the family businesses in Saudi Arabian marketplace and to look at corporate governance as one of the solutions to reform their issues and ensure their continuous for the following generations. To achieve this end, the research based on a critical analysis method that starts by discussing the definition of family-owned companies, then analysing their characteristics. A succession planning issue, management problems, protection of shareholders' rights, and transparency requirements would be addressed. The key findings were that limited studies have been conducted in the context of emerging market economies although family ownership in emerging countries is common. Hence, any current evidence provides an insight into family businesses in the emerging economies, and Saudi Arabia is valuable.
Intra- and Cross-Species Transmission of Astroviruses

Astroviruses are non-enveloped, single-stranded RNA viruses that infect mammalian and avian species. In humans, astrovirus infections are one of the most common causes of gastroenteritis in children. Infection has also been linked to serious neurological complications, especially in immunocompromised individuals. More extensive disease has also been characterized in non-human mammalian and avian species. To date, astroviruses have been detected in over 80 different avian and mammalian hosts. As the number of hosts continues to rise, the need to understand how astroviruses transmit within a given species as well as to new host species becomes increasingly important. Here, we review the current understanding of astrovirus transmission, the factors that influence viral spread, and the potential for cross-species transmission. Additionally, we highlight the current gaps in knowledge and areas of future research that will be key to understanding astrovirus transmission and zoonotic potential.
F lex i ble a nd recon ﬁ g u r a ble robo ti c i n sp ec ti on i n ma n u f a c tu r i ng

Having a generic and flexible software infrastructure for inspection robots is the main goal of the SPIRIT project. SPIRIT is a EU funded project whose main tangible result is a software framework that includes an offline and an inline framework. The offline part is used for simulating the robotic workcell and configure the whole inspection task, e.g. select the robot and the inspection sensor and finally compute robot trajectory. The inline part instead is responsible for reproducing the simulated task on the real robot, it also includes innovative features like local and global re-planning, real-time data mapping for backprojecting sensor information on the 3D CAD model of the inspected part. In the project four different demonstrators have been developed, they all belongs to different industrial settings, i.e. automotive, aerospace and manufacture industry. Such high level of diversity demonstrates how the SPIRIT framework is general and flexible enough to be used in heterogeneous environments without prototyping and developing the entire workcell from scratch each time.
Greening the Supply Chain: A Way towards a New Way

The word ‘going green’ have become the integral part for businesses as now a days the public awareness regarding sustainability is immense. In the recent time the buyers are very much Vigilant about the health & green lifestyle. Green clothing i.e. eco-friendly is drawing more attention among the buyers & provides them healthier choices. Those enterprises who are putting their effort to raise their environmental performances bear the pressure & the driving forces as a result of globalisation. Therefore the Green supply chain management is a recent technique for creating a sustainable supply chain. The main purpose of this paper is to review the literature on green supply chain & steps to be taken by the enterprises to achieve social, environmental & economic benefits.
An Overview: Inquiry Based Science Learning Models in Empowering Creative Thinking Skills High School Student

Creative thinking skills are low due to media, non-specific learning models, learning activities are limited to the inability to accommodate creative thinking. The model offered is a Science-based inquiry model. The aim is to analyze the level of the syntactic Wenning (LoI) of the same, the potential for Investigative Learning in empowering creative thinking skills through study of literature. Its method with descriptive qualitative inductive pattern analysis techniques. Based on an analysis of 67 articles from 2007-2020, that Inquiry Learning empowers creative thinking skills, science learning activities in joint scientific work in each syntax becomes an important argument in the development of inquiry learning models. The importance of confrontation issues is to explore students' ideas from unusual things before observational activities can be met, collect data and verify on smoothness, flexibility, elaboration and redefinition abilities; collect data for testing hypotheses about originality and flexibility; organize and formulate explanations for the smoothness, flexibility, elaboration and analysis of the investigation process regarding elaboration. Implementation of Inquiry Learning has the potential to explore original ideas through problems, although not yet an indicator of creative thought that can be accommodated, the offer to change the syntax of Inquiry studies at the stage of confrontation is very important.
MOBILE APPLICATION BASED DETECTION OF DDOS ATTACK WITH ENHANCED RANDOM FOREST ALGORITHM.

Clients and Internet Service Providers (ISPs) are continually influenced by DDoS. DDoS are the attacks which are generally accomplished digital attacks with a thought process to adjourn the objective administrations and framing them inaccessible for clients. As indicated by a ZDNet composition, in February 2020, "Amazon said its AWS Shield administration alleviated the biggest DDoS attack ever recorded, halting a 2.3 Tbps attack. " Before this attack, the world had encountered the most prominent recorded Distributed-DoS attack was 1.7 Tbps, which itself uprooted the previous attacks on GitHub. In General, there are different sorts of DDoS attacks and each attack utilizes an alternate convention and the attacker utilizes botnet to implement attacks like this. Subsequently, this will be exceptionally hard for the associations to manage such attacks and request 3rd-parties to make sure about them-selves from attacks of DDOS. To dispense with the 3rd-parties, Our propounded framework utilizes ML algorithms to recognize the approaching parcel is vindictive or not and use in-app notification to inform the user about the Blacklist. The vital advantage of in-app notification is that boycotted IP addresses are viably put away, and utilization of such foundation gives a bit of leeway of additional security instruments over present DDoS frameworks. Our document has assessed three distinct calculations, for example, the KNN, DT Classifier, RF calculation to discover the better characterizing calculation. The Tree-based Classifier method utilized for highlight election to help the calculated time. From these three calculations, RF gives a precision around ninety five percent of continuous traffic examination.
Investigating population genomic structure of salmon lice (Lepeophtheirus salmonis) in northern Norway

Salmon louse (Lepeophtheirus salmonis) is a parasitic copepod that causes significant damage to Atlantic salmon (Salmo salar) and incurs significant costs to the salmonid aquaculture industry. In Norway, the industry has been coping with salmon lice issues with various methods such as chemical treatments, but it has resulted in an acquisition of resistance in salmon lice towards the existing delousing treatments. In order to maximize the effectiveness of the treatments, it is important to understand population structuring of salmon lice, and factors that are driving the structure, since recent studies suggested that several Atlantic salmon lice populations are sharing genetic materials coding for resistance toward delousing treatments. The objective of this study was to investigate whether salmon lice in northern Norway display population structure and differentiation at small geographical scales. We identified 25,795 robustly supported SNPs among salmon lice from 8 different locations in northern Norway. While only very weak structure was observed based on the full SNP dataset (25,795 SNPs), relatively weak, but highly significant, population structure was observed using 303 important SNPs identified using a random forest classification approach. No significant correlation between genetic differentiation and geographical distance was observed. To our knowledge, it was the first study to reveal the significant population structure of salmon lice at such small spatial scales. Our results suggested that the observed population structure was a result of dispersal and mixing of salmon lice, as well as selective pressure such as delousing treatments and local environment. Our results are potentially helpful to make population specific delousing treatments, where the treatments are designed depending on the genetic characteristics of the targeted salmon lice population.
CKD-Tree: An Improved KD-Tree Construction Algorithm

Data structures such as VP-Tree, R-Tree and KD-Tree builds an index of all the data available in the offline phase and uses that indexed tree to search for and answer nearest neighbor queries or to classify the input query. We use a Lightweight Coreset algorithm to reduce the actual data size used to build the tree index, resulting in a faster index building time. We improve on already available Nearest Neighbor based Classification techniques and pit our classification method against the widely accepted, state of the art data structures such as VP-Tree, R-Tree and KD-Tree. In terms of speed the proposed method out performs the compared data structures, as the size of the data increases.