Classification Arabic Twitter User’s Insights Using Rough Set Theory

Nowadays, people using social media from around the world to share their daily affairs. Arabic twitter for example is a platform where users read, reply, post which known ‘tweets’. Users trading their opinions on different trends that are not equal in important and differed based on their power and interest. Tweets can provide rich information to make decision. The main objective of this paper is to present a framework for making a valuable decision through analyzing social users' insights based on their proximity to a particular trend with highlights their power in this trend. Tweets are exceedingly unstructured that makes it difficult to analyze. Nevertheless, our proposed model differs from previous research in this field it gathered the use of supervised and unsupervised machine learning algorithms. The process of performing this work as follows: classifying users based on the degree of their closeness/interest utilizing Mendelow’s power/interest matrix, rough set theory to eliminate the features that may be found in user profiles to find minimal sets of data. The proposed model applied two attribute reduction algorithms on our dataset to determine the optimal number of reducts for improving decision making from the user replies. In addition to, unsupervised machine learning to group their replies into subcategories such as positive, negative, or neutral. The experimental evaluation shows that Johnson algorithm has reduced the user attributes by 71% than genetic algorithm that utilized in a classification model

Dynamic Modelling by Bond Graph Approach of Convective Drying Phenomena

Drying operations play an important role in food industries. They are often the last operation of the process of manufacturing a product, with a strong influence on the final quality. The processes are numerous and depend on the type and amount of product to be dried and water to be evaporated, the desired final quality, or the desired functionality for the dried product. In this chapter, we present a modeling study of heat transfer during drying a moist agricultural product placed in a hot air flow in a tunnel dryer with partial solar heating. The bond graph approach has been used for system modeling, and it is an object-oriented graphical approach based on an energetic description between subsystems. Some drying tests have been carried out on tomatoes and the experimental results are compared with the theoretical results for the validation of the developed model

Fuel Cell Impedance Model Parameters Optimization using a Genetic Algorithm

The objective of this paper is the PEM fuel cell impedance model parameters identification. This work is a part of a larger work which is the diagnosis of the fuel cell which deals with the optimization and the parameters identification of the impedance complex model of the Nexa Ballard 1200 W PEM fuel cell. The method used for the identification is a sample genetic algorithm and the proposed impedance model is based on electric parameters, which will be found from a sweeping of well determined frequency bands. In fact, the frequency spectrum is divided into bands according to the behavior of the fuel cell. So, this work is considered a first in the field of impedance spectroscopy So, this work is considered a first in the field of impedance spectroscopy. Indeed, the identification using genetic algorithm requires experimental measures of the fuel cell impedance to optimize and identify the impedance model parameters values. This method is characterized by a good precision compared to the numeric methods. The obtained results prove the effectiveness of this approach

Discrete wavelet transform based freezing of gait detection in Parkinson's disease

Wearable on body sensors have been employed in many applications including ambulatory monitoring and pervasive computing systems. In this work, a wearable assistant has been created for people suffering from Parkinson’s disease (PD), specifically with the Freezing of Gait (FoG) symptom. Wearable accelerometers were placed on the person’s body and used for movement measure. When FoG is detected, a rhythmic audio signal was given from the wearable assistant to motivate the wearer to continue walking. Long term monitoring results in collecting huge amounts of complex raw data; therefore, data analysis becomes impractical or infeasible resulting in the need for data reduction. In the present study, Discrete Wavelet Transform (DWT) has been used to extract the main features inherent in the key movement indicators for FoG detection. The discrimination capacities of these features were assessed using, i) Support Vector Machine (SVM) using a linear kernel function, and ii) Artificial Neural Network (ANN) with a two-layer feed-forward with hidden layer of 20 neurons that trained with conjugate gradient back- propagation. Using these two different machine learning techniques, we were capable of detecting FoG with an accuracy of 87.50% and 93.8%, respectively. Additionally, the comparison between the extracted features from DWT coefficients with those using Fast Fourier Transform (FFT) established accuracies of 93.8% and 81.3%, respectively. Finally, the discriminative features extracted from DWT yield to a robust multidimensional classification model compared to models in the literature based on a single feature. The work presented paves the way for reliable, real-time wearable sensors to aid people with PD

Development of certain novel N-(2-(2-(2-oxoindolin-3-ylidene)hydrazinecarbonyl)phenyl)-benzamides and 3-(2-oxoindolin-3-ylideneamino)-2-substituted quinazolin-4(3H)-ones as CFM-1 analogs: design, synthesis, QSAR analysis and anticancer activity.

The reaction of N-(2-(hydrazinecarbonyl)aryl)benzamides 2a, b with indoline-2,3-diones 4ae in acidified ethanolic solution furnished the corresponding N-(2-(2-(2-oxoindolin-3-ylidene)hydrazinecarbonyl)phenyl)benzamides 5aj, respectively. Furthermore, 3-(2-oxoindolin-3-ylideneamino)-2-substituted quinazolin-4(3H)-ones 6aj were prepared by the reaction of 3-amino-2-arylquinazolin-4(3H)-one 3a, b with 4ae. Six derivatives of the twenty newly synthesized compounds showed remarkable antitumor activity against most of the tested cell lines, Daoy, UW228-2, Huh-7, Hela and MDA-MB231. Although these six compounds were more potent than the standard drug (CFM-1), indeed compounds 5b, 5d and 6b were the best candidates with IC50 values in the range 1.866.87, 4.4210.89 and 1.468.60 μg/ml and percentage inhibition in the range 77.188.7, 59.4184.8 and 75.488.0%, respectively. QSAR analyses on the current series of derivatives also have been performed for all five cancer cell lines and thus 10 statistically significant models were developed and internally cross validated

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Robust Diagnosis of a Proton Exchange Membrane Fuel Cell Using Bond Graph Methodology – Physical and Electrical Faults Detection and Isolation

Fuel cells are currently experiencing an invigorating resurgence, both at the industrial and research levels. Diagnosis of stack performance is of importance for proton exchange membrane fuel cell (PEMFC) research. In this paper, a bond graph (BG) approach was used for modelling, simulation and robust diagnosis of a PEMFC. In literature, several PEMFC diagnosis methodologies were outlined in terms of efficiency and applicability. This paper described the linear fractional transformations (LFT) method to make it capable for handling the PEMFC diagnostics; an approach based on LFT-BG was developed to diagnose hydration and cells deterioration faults that may occur within a fuel cell. Simulation and experimental diagnostic testing results of a 1.2 kW Nexa fuel cell were presented and used to show the dynamic behaviour of the system variables and assessing the performance of the observer