A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries

According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%. 2013 IEEE.This work was supported by the Qatar National Library.Scopus2-s2.0-8510734936

β-Sitosterol Glucoside-Loaded Nanosystem Ameliorates Insulin Resistance and Oxidative Stress in Streptozotocin-Induced Diabetic Rats

β-Sitosterol glucoside (SG), isolated from Senecio petasitis (Family Asteraceae), was loaded in self-nanoemulsifying drug delivery systems (SEDDS) in a trial to enhance its solubility and biological effect. Various co-surfactants were tested to prepare a successful SEDDS. The selected SG-loaded SEDDS had a droplet size of 134 ± 15.2 nm with a homogenous distribution (polydispersity index 0.296 ± 0.02). It also demonstrated a significant augmentation of SG in vitro release by 4-fold compared to the free drug suspension. The in vivo insulin sensitivity and antidiabetic effect of the prepared SG-loaded SEDDS were further assessed in streptozotocin-induced hyperglycemic rats. The hypoglycemic effect of SG-loaded nanosystem was evidenced by decreased serum glucose and insulin by 63.22% and 53.11%, respectively. Homeostasis model assessment-insulin resistance (HOMA-IR) index demonstrated a significant reduction by 5.4-fold in the diabetic group treated by SG-loaded nanosystem and exhibited reduced glucagon level by 40.85%. In addition, treatment with SG-loaded nanosystem significantly decreased serum MDA (malondialdehyde) and increased catalase levels by 38.31% and 64.45%, respectively. Histopathological investigations also supported the protective effect of SG-loaded nanosystem on the pancreas. The promising ability of SG-loaded nanosystem to ameliorate insulin resistance, protect against oxidative stress, and restore pancreatic β-cell secretory function warrants its inclusion in further studies during diabetes progression

PERFORMANCE ANALYSIS OF OPTICAL CDMA SYSTEM USING VC CODE FAMILY UNDER VARIOUS OPTICAL PARAMETERS

The intent of this paper is to study the performance of spectral-amplitude coding optical code-division multiple-access (OCDMA) systems using Vector Combinatorial (VC) code under various optical parameters. This code can be constructed by an algebraic way based on Euclidian vectors for any positive integer number. One of the important properties of this code is that the maximum cross-correlation is always one which means that multi-user interference (MUI) and phase induced intensity noise are reduced. Transmitter and receiver structures based on unchirped fiber Bragg grating (FBGs) using VC code and taking into account effects of the intensity, shot and thermal noise sources is demonstrated. The impact of the fiber distance effects on bit error rate (BER) is reported using a commercial optical systems simulator, virtual photonic instrument, VPITM. The VC code is compared mathematically with reported codes which use similar techniques. We analyzed and characterized the fiber link, received power, BER and channel spacing. The performance and optimization of VC code in SAC-OCDMA system is reported. By comparing the theoretical and simulation results taken from VPITM, we have demonstrated that, for a high number of users, even if data rate is higher, the effective power source is adequate when the VC is used. Also it is found that as the channel spacing width goes from very narrow to wider, the BER decreases, best performance occurs at a spacing bandwidth between 0.8 and 1 nm. We have shown that the SAC system utilizing VC code significantly improves the performance compared with the reported codes

Staphylococcus pseudintermedius Sbi paralogs inhibit complement and bind IgM, IgG Fc and Fab

The success of staphylococci as pathogens has been attributed, in part, to their ability to evade their hosts’ immune systems. Although the proteins involved in evasion have been extensively studied in staphylococci affecting humans little characterization has been done with Staphylococcus pseudintermedius, an important cause of pyoderma in dogs. Staphylococcus aureus binder of immunoglobulin (Sbi) interferes with innate immune recognition by interacting with multiple host proteins. In this study, a S. pseudintermedius gene that shares 38% similarity to S. aureus Sbi was cloned from S. pseudintermedius strains representative of major clonal lineages bearing two paralogs of the protein. Binding of immunoglobulins and Fab and Fc fragments as well as interaction with complement was measured. S. pseudintermedius Sbi protein bound IgG from multiple species and canine complement C3, neutralized complement activity and bound to canine IgM and B cells. Evidence from this work suggests Sbi may play an important role in S. pseudintermedius immune evasion

Endoscopic third ventriculostomy for obstructive hydrocephalus

Introduction: Obstructive hydrocephalus has long been managed by valve-regulated shunts. These shunts are associated with a myriad of short and long-term complications. This has fueled interest in Endoscopic Third Ventriculostomy (ETV) which provides a more physiological pathway for cerebrospinal fluid (CSF) diversion while avoiding many shunt-related complications. Aim: The objective of this study is to analyze the outcomes of ETV at our institution, focusing on the indications, success rates, and short-term complications. Methods: Between July 2010 and September 2015, 47 patients with obstructive hydrocephalus underwent ETV at the Neurosurgery Teaching Hospital in Baghdad/ Iraq. We retrospectively analyzed the data of these patients using hospital health records. Simple statistics were performed using  SPSS Version 20. A standardized surgical technique was employed in all cases. Results: The mean age was 4.4 years (range 40 days - 38 years). The male: female ratio was 1.23:1 (55% males and 45% females). The most common cause of obstructive hydrocephalus in patients undergoing ETV was aqueductal stenosis (62%; N=29). The second most common cause was posterior fossa tumours (23%; N=11). The overall success rate for ETV was 68%. The net post-operative complication rate was 13% (N=6). CSF leak and seizures were the only two charted post-operative complications at the rates of 9%(N=4) and 4% (N=2), accordingly. No deaths were recorded. Conclusion: ETV is a viable alternative to shunt insertion in a select group of patients with obstructive hydrocephalus, with acceptable success rate and safety profile

Risk Factors of Knee Osteoarthritis in Patients attending Rheumatology Clinic in Mosul

Background: Osteoarthritis can be described as the last common pathway of processes that leads to joint failure. This illness also has a significant social and economic impact on all civilizations, and has become a growing burden on health-care systems in usually healthy economies with an aging population. Methods: A case-control study design conducted from the 1st, November 2020 to the 31st, May 2021 in Ibn-Sena and Al-Salam Teaching Hospitals in Mosul, Iraq. A total of 100 individuals diagnosed with knee Osteoarthritis represented the cases of this study, and another 100 individuals free from knee Osteoarthritis were recruited as controls. Objectives:  the aim of current study was to measure the frequency of cases related to each assumed risk factor of knee Osteoarthritis among the study sample and to assess the causes of knee Osteoarthritis. Results: The majority of cases and controls (72 % and 53 %, respectively) were between 45 and 64 years. Also, females represented 76% of cases and 57% of controls. It was found that 74% of cases and 81% of controls lived in city. Married people made up 93% of cases and 79% of controls. The manual workers made up 82% of cases and 27% of controls. Also, there was no link between smoking, systemic illnesses and knee osteoarthritis. Females over 45 years accounted for 65% of cases and 31% of controls. Overweight and obese people were twice as likely as the control group to develop knee Osteoarthritis. Trauma in the past had strong link with the development of knee Osteoarthritis. Also, only over half of the patients (51) had a positive family history of Osteoarthritis, whereas only 17% of the controls had a positive family history of Osteoarthritis. Conclusion: Osteoarthritis occurs more frequently among old married females. Overweight and obesity found to be a risk factor for Osteoarthritis. Also, manual labor increases the chances of developing Osteoarthritis. Athletes are also less likely to develop Osteoarthritis. Trauma and Family history of Osteoarthritis Increase the risk of Osteoarthritis

Scheduling and Communication Schemes for Decentralized Federated Learning

Federated learning (FL) is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. One central server is not enough, due to problems of connectivity with clients. In this paper, a decentralized federated learning (DFL) model with the stochastic gradient descent (SGD) algorithm has been introduced, as a more scalable approach to improve the learning performance in a network of agents with arbitrary topology. Three scheduling policies for DFL have been proposed for communications between the clients and the parallel servers, and the convergence, accuracy, and loss have been tested in a totally decentralized mplementation of SGD. The experimental results show that the proposed scheduling polices have an impact both on the speed of convergence and in the final global model.Comment: 32nd International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, 202

Ellagic acid: A potent glyoxalase-I inhibitor with a unique scaffold

The glyoxalase system, particularly glyoxalase-I (GLO-I), has been approved as a potential target for cancer treatment. In this study, a set of structurally diverse polyphenolic natural compounds were investigated as potential GLO-I inhibitors. Ellagic acid was found, computationally and experimentally, to be the most potent GLO-I inhibitor among the tested compounds which showed an IC50 of 0.71 mol L–1. Its binding to the GLO-I active site seemed to be mainly driven by ionic interaction via its ionized hydroxyl groups with the central Zn ion and Lys156, along with other numerous hydrogen bonding and hydrophobic interactions. Due to its unique and rigid skeleton, it can be utilized to search for other novel and potent GLO-I inhibitors via computational approaches such as pharmacophore modeling and similarity search methods. Moreover, an inspection of the docked poses of the tested compounds showed that chlorogenic acid and dihydrocaffeic acid could be considered as lead compounds worthy of further optimization

Lips tracking identification of a correct pronunciation of Quranic alphabets for tajweed teaching and learning

Mastering the recitation of the Holy Quran is an obligation among Muslims. It is an important task to fulfill other Ibadat like prayer, pilgrimage, and zikr. However, the traditional way of teaching Quran recitation is a hard task due to the extensive training time and effort required from both teacher and learner. In fact, learning the correct pronunciation of the Quranic letters or alphabets is the first step in mastering Tajweed (Rules and Guidance) in Quranic recitation. The pronunciation of Arabic alphabets is based on its points of articulation and the characteristics of a particular alphabet. In this paper, we implement a lip identification technique from video signal acquired from experts to extract the movement data of the lips while pronouncing the correct Quranic alphabets. The extracted lip movement data from experts helps in categorizing the alphabets into 5 groups and in deciding the final shape of the lips. Later, the technique was tested among a public reciter and then compared for similarity verification between the novice and the professional reciter. The system is able to extract the lip movement of the random user and draw the displacement graph and compare with the pronunciation of the expert. The error will be shown if the user has mistakenly pronounced the alphabet and suggests ways for improvement. More subjects with different backgrounds will be tested in the very near future with feedback instructions. Machine learning techniques will be implemented at a later stage for the real time learning application. Menguasai bacaan Al-Quran adalah satu kewajipan di kalangan umat Islam. Ia adalah satu tugas yang penting untuk memenuhi Ibadat lain seperti solat, haji, dan zikir. Walau bagaimanapun, cara tradisional pengajaran bacaan Al-Quran adalah satu tugas yang sukar kerana memerlukan masa latihan dan usaha yang banyak daripada guru dan pelajar. Malah, mempelajari sebutan yang betul bagi huruf Al-Quran adalah langkah pertama dalam menguasai Tajweed (Peraturan dan Panduan) pada bacaan Al-Quran. Sebutan huruf Arab adalah berdasarkan cara penyebutan tiap-tiap huruf dan ciri-ciri huruf tertentu. Dalam kertas ini, kami membina teknik pengenalan bibir dari isyarat video yang diperoleh daripada bacaan Al Quran oleh pakar-pakar untuk mengekstrak data pergerakan bibir ketika menyebut huruf Al-Quran yang betul. Data pergerakan bibir yang diekstrak daripada pembacaan oleh pakar membantu dalam mengkategorikan huruf kepada 5 kumpulan dan dalam menentukan bentuk akhir bibir. Kemudian, teknik ini diuji dengan pembaca awam dan kemudian bacaan mereka dibandingkan untuk pengesahan persamaan bacaan antara pembaca awam dan pembaca Al-Quran profesional. Sistem ini berjaya mengambil pergerakan bibir pengguna rawak dan melukis graf perbezaan sebutan mereka apabila dibandingkan dengan sebutan pakar. Jika pengguna telah tersilap menyebut sesuatu huruf, kesilapan akan ditunjukkan dan cara untuk penambahbaikan dicadangkan. Lebih ramai pengguna yang mempunyai latar belakang yang berbeza akan diuji dalam masa terdekat dan arahan maklum balas akan diberi. Teknik pembelajaran mesin akan dilaksanakan di peringkat seterusnya bagi penggunaan pembelajaran masa nyata

Aggression is a dominant behavior among adolescents in Al-Sadr city, Baghdad-Iraq

Background: Aggression is any behavior intending to harm another living being that desires to avoid such harm. Aggression is considered as a public health problem worldwide. Exposure to violence is a determinant of aggression. Al-Sadr city, which is an overcrowded residential area in Baghdad, was exposed to violence through wars, civil war, and widespread inter-personal violence). This situation was an impetus to study aggression in this city. Objectives: To estimate the prevalence of aggression among secondary school students in Al-Sadr city, Baghdad. Methodology: A cross-sectional study was carried out in Al-Sadr city in Baghdad. A total of 720 adolescent secondary schools students from both genders were included in the study. They were selected by multistage random sampling. Their age was ranged from 13-21 years. Data was collected from 1st of February 2019 to 30 of April 2019 through a self-reported questionnaire (Aggression-Problem Behavior Frequency Scale). Results: The mean age of participants was 16.2±2.04 years. Of all participants, 84.3% were assessed to be aggressive; including 329 (91.4%) males and 278 (77.2%) females. Males showed significantly higher aggression than females (p=0.001). Absence from school and low academic achievement were dominating among aggressive students (88.2% and 86.9%, respectively). Non-physical Aggression was the most common type of aggression (76.4%). Conclusion: A high prevalence of aggression (mainly non-physical) was found, which was significantly associated with gender, being higher among males. Aggression was negatively affecting school achievement