Using Deep Learning Networks to Predict Telecom Company Customer Satisfaction Based on Arabic Tweets

Information systems are transforming businesses, which are using modern technologies towards new business models based on digital solutions, which ultimately lead to the design of novel socio-economic systems. Sentiment analysis is, in this context, a thriving research area. This paper is a case study of Saudi telecommunications (telecom) companies, using sentiment analysis for customer satisfaction based on a corpus of Arabic tweets. This paper compares, for the first time for Saudi social media in telecommunication, the most popular machine learning approach, support vector machine (SVM), with two deep learning approaches: long short-term memory (LSTM) and gated recurrent unit (GRU). This study used LSTM and GRU with two different implementations, adding attention mechanism and character encoding. The study concluded that the bidirectional-GRU with attention mechanism achieved a better performance in the telecommunication domain and allowed detection of customer satisfaction in the telecommunication domain with high accuracy

GPA as a predictive criterion for the utility of using the high-school grade and selection tests as acceptance criteria in the Yemeni universities of Sana'a and Taiz

The study aimed to identify the predictive validity of admission criteria used at the Universities of Taiz and Sana'a in Republic of Yemen. The study sample consisted of 750 male and female students who enrolled at the Universities of Taiz and Sana'a for the academic year 2008/2009, and spent four years of study for Bachelor Degree and graduated in 2011, and underwent admission tests. Statistical analysis has been conducted using multiple regression analysis. The results showed that the used admission criteria (Secondary school, and admission test grades) predict academic performance of the student. In addition, there were statistically significant differences in the prediction, according to the college variable at Sanaa University students while there were no differences in the prediction according to the sex variable. Based on the search findings, the researchers recommended to continue in using secondary school GPA (Grade Point Average) as admission criteria due to its importance in predicting academic performance of the student

Anticancer property of hexane extract of Suaeda fruticose plant leaves against different cancer cell lines

Purpose: To evaluate the bioactivity of hexane extract of S. fruticosa leaves against the cancer cell lines HepG2, MCF-7, and HCT-116, and to determine the chemical composition-function relationship. Methods: Using the liquid-liquid extraction method, the nonpolarL constituent compounds were isolated from the leaves. The cytotoxicity of the hexane extract was evaluated using an SRB assay. Mechanism of action was verified by observing the appearance of apoptotic bodies using fluorescence microscopy, while anti-proliferative activity was assayed via flow cytometry. Results: The results revealed that secondary metabolites in the hexane extract demonstrated the highest cytotoxicity, and thus anticancer activity, against HCT-116 cells, with an IC50 of 17.15 ± 0.78 mg/mL. The presence of apoptotic bodies indicate an ability to induce apoptosis. Flow cytometry results suggest that the secondary metabolites stalled the cell cycle at the G0/G1 phase. Conclusion: The results indicate that S. fruticosa hexane extract may be considered a potential new source of the anti-cancer compound, momilactone B. Keywords: Anticancer, Apoptosis, Colon Cancer, Liver cancer, Breast cancer, Liquid chromatography–mass spectrometry, Suaeda fruticose, Momilactone

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Contributions to Trajectory Analysis and Prediction: Statistical and Deep Learning Techniques

A causa de l’estreta relació entre la vida de les persones i determinades ubicacions geogràfiques, les dades històriques sobre trajectòries d’una persona contenen informació valuosa que es pot utilitzar per descobrir els seus estils de vida i hàbits. L’ús generalitzat de dispositius mòbils amb capacitat de localització ha impulsat la mineria de trajectòries (trajectory mining), la qual se centra en la manipulació, el processament i l’anàlisi de dades de trajectòries per facilitar l’extracció de coneixement a partir de l’històric de les trajectòries d’una persona. Basant-nos en aquesta anàlisi, fins i tot es pot arribar a predir quina serà la probable propera localització d’una persona. Amb aquestes tècniques, s’obre la porta a la millora dels actuals serveis basats en la ubicació i a l’aparició de nous models de negoci, basats en notificacions riques relacionades amb la predicció adequada de les futures ubicacions dels usuaris. Aquesta tesi tracta sobre la predicció de la ubicació i el descobriment de regions significatives a les zones de moviment de les persones. Proposa diversos models de predicció, basant-se en diferents tècniques d'aprenentatge automàtic (com ara les cadenes de Markov, les xarxes neuronals recurrents i les xarxes neuronals convolucionals), tot considerant diferents mètodes de representació d'entrada (embedding learning i one hot vector). A més, el model de predicció utilitza la attention technique (tècnica d’atenció), que té com a objectiu alinear els intervals de temps en les trajectòries de les persones que són rellevants per a una ubicació específica. La tesi també proposa un esquema de codificació temporal per capturar les característiques del comportament del moviment. Addicionalment, analitza l'impacte de l'aprenentatge de la representació espacial-temporal mitjançant l'avaluació de diferents arquitectures. Finalment, l’anàlisi de la trajectòria i la predicció de localització s’apliquen a la monitorització en temps real per a persones grans.Debido a la estrecha relación entre la vida de las personas y determinadas ubicaciones geográficas, los datos históricos sobre trayectorias de una persona contienen información valiosa que se puede utilizar para descubrir sus estilos de vida y hábitos. El uso generalizado de dispositivos móviles con capacidad de localización ha impulsado la minería de trayectorias (trajectory mining), la cual se centra en la manipulación, el procesamiento y el análisis de datos de trayectorias para facilitar la extracción de conocimiento a partir de el histórico de las trayectorias de una persona. Basándonos en este análisis, incluso se puede llegar a predecir cuál será la probable próxima localización de una persona. Con estas técnicas, se abre la puerta a la mejora de los actuales servicios basados ​​en la ubicación y en la aparición de nuevos modelos de negocio, basados ​​en notificaciones ricas relacionadas con la predicción adecuada de las futuras ubicaciones de los usuarios. Esta tesis trata sobre la predicción de la ubicación y el descubrimiento de regiones significativas en las zonas de movimiento de las personas. Propone varios modelos de predicción, basándose en diferentes técnicas de aprendizaje automático (como las cadenas de Markov, las redes neuronales recurrentes y las redes neuronales convolucionales), considerando diferentes métodos de representación de entrada (embedding learning y one hot vector). Además, el modelo de predicción utiliza la attention technique (técnica de atención), que tiene como objetivo alinear los intervalos de tiempo en las trayectorias de las personas que son relevantes para una ubicación específica. La tesis también propone un esquema de codificación temporal para capturar las características del comportamiento del movimiento. Adicionalmente, analiza el impacto del aprendizaje de la representación espacial-temporal mediante la evaluación de diferentes arquitecturas. Finalmente, el análisis de la trayectoria y la predicción de localización se aplican a la monitorización en tiempo real para personas mayores.Due to the relationship between people’s daily life and specific geographic locations, the historical trajectory data of a person contains lots of valuable information that can be used to discover their lifestyle and regularity. The generalisation in the use of mobile devices with location capabilities has fueled trajectory mining: the research area that focuses on manipulating, processing and analysing trajectory data to aid the extraction of higher level knowledge from the trajectory history of a user. Based on this analysis, even the person’s next probable location can be predicted. These techniques pave the way for the improvement of current location-based services and the rise of new business models, based on rich notifications related to the right prediction of users’ next location. This thesis addresses location prediction as well as the discovery of significant regions in person’s movement area. It proposes various models to predict the future state of people movement, based on different machine learning techniques (such as Markov Chains, Recurrent Neural Networks and Convolutional Neural Networks) and considering different input representation methods (embedding learning and one-hot vector). Moreover, the attention technique is used in the prediction model, aiming at aligning time intervals in people’s trajectories that are relevant to a specific location. Furthermore, the thesis proposes a time encoding scheme to capture movement behavior characteristics. In addition to that, it analyses the impact of Space-Time representation learning through evaluating different architectural configurations. Finally, trajectory analysis and location prediction is applied to real-time smartphone-based monitoring system for seniors

Contributions to Trajectory Analysis and Prediction: Statistical and Deep Learning Techniques

A causa de l’estreta relació entre la vida de les persones i determinades ubicacions geogràfiques, les dades històriques sobre trajectòries d’una persona contenen informació valuosa que es pot utilitzar per descobrir els seus estils de vida i hàbits. L’ús generalitzat de dispositius mòbils amb capacitat de localització ha impulsat la mineria de trajectòries (trajectory mining), la qual se centra en la manipulació, el processament i l’anàlisi de dades de trajectòries per facilitar l’extracció de coneixement a partir de l’històric de les trajectòries d’una persona. Basant-nos en aquesta anàlisi, fins i tot es pot arribar a predir quina serà la probable propera localització d’una persona. Amb aquestes tècniques, s’obre la porta a la millora dels actuals serveis basats en la ubicació i a l’aparició de nous models de negoci, basats en notificacions riques relacionades amb la predicció adequada de les futures ubicacions dels usuaris. Aquesta tesi tracta sobre la predicció de la ubicació i el descobriment de regions significatives a les zones de moviment de les persones. Proposa diversos models de predicció, basant-se en diferents tècniques d'aprenentatge automàtic (com ara les cadenes de Markov, les xarxes neuronals recurrents i les xarxes neuronals convolucionals), tot considerant diferents mètodes de representació d'entrada (embedding learning i one hot vector). A més, el model de predicció utilitza la attention technique (tècnica d’atenció), que té com a objectiu alinear els intervals de temps en les trajectòries de les persones que són rellevants per a una ubicació específica. La tesi també proposa un esquema de codificació temporal per capturar les característiques del comportament del moviment. Addicionalment, analitza l'impacte de l'aprenentatge de la representació espacial-temporal mitjançant l'avaluació de diferents arquitectures. Finalment, l’anàlisi de la trajectòria i la predicció de localització s’apliquen a la monitorització en temps real per a persones grans.Debido a la estrecha relación entre la vida de las personas y determinadas ubicaciones geográficas, los datos históricos sobre trayectorias de una persona contienen información valiosa que se puede utilizar para descubrir sus estilos de vida y hábitos. El uso generalizado de dispositivos móviles con capacidad de localización ha impulsado la minería de trayectorias (trajectory mining), la cual se centra en la manipulación, el procesamiento y el análisis de datos de trayectorias para facilitar la extracción de conocimiento a partir de el histórico de las trayectorias de una persona. Basándonos en este análisis, incluso se puede llegar a predecir cuál será la probable próxima localización de una persona. Con estas técnicas, se abre la puerta a la mejora de los actuales servicios basados ​​en la ubicación y en la aparición de nuevos modelos de negocio, basados ​​en notificaciones ricas relacionadas con la predicción adecuada de las futuras ubicaciones de los usuarios. Esta tesis trata sobre la predicción de la ubicación y el descubrimiento de regiones significativas en las zonas de movimiento de las personas. Propone varios modelos de predicción, basándose en diferentes técnicas de aprendizaje automático (como las cadenas de Markov, las redes neuronales recurrentes y las redes neuronales convolucionales), considerando diferentes métodos de representación de entrada (embedding learning y one hot vector). Además, el modelo de predicción utiliza la attention technique (técnica de atención), que tiene como objetivo alinear los intervalos de tiempo en las trayectorias de las personas que son relevantes para una ubicación específica. La tesis también propone un esquema de codificación temporal para capturar las características del comportamiento del movimiento. Adicionalmente, analiza el impacto del aprendizaje de la representación espacial-temporal mediante la evaluación de diferentes arquitecturas. Finalmente, el análisis de la trayectoria y la predicción de localización se aplican a la monitorización en tiempo real para personas mayores.Due to the relationship between people’s daily life and specific geographic locations, the historical trajectory data of a person contains lots of valuable information that can be used to discover their lifestyle and regularity. The generalisation in the use of mobile devices with location capabilities has fueled trajectory mining: the research area that focuses on manipulating, processing and analysing trajectory data to aid the extraction of higher level knowledge from the trajectory history of a user. Based on this analysis, even the person’s next probable location can be predicted. These techniques pave the way for the improvement of current location-based services and the rise of new business models, based on rich notifications related to the right prediction of users’ next location. This thesis addresses location prediction as well as the discovery of significant regions in person’s movement area. It proposes various models to predict the future state of people movement, based on different machine learning techniques (such as Markov Chains, Recurrent Neural Networks and Convolutional Neural Networks) and considering different input representation methods (embedding learning and one-hot vector). Moreover, the attention technique is used in the prediction model, aiming at aligning time intervals in people’s trajectories that are relevant to a specific location. Furthermore, the thesis proposes a time encoding scheme to capture movement behavior characteristics. In addition to that, it analyses the impact of Space-Time representation learning through evaluating different architectural configurations. Finally, trajectory analysis and location prediction is applied to real-time smartphone-based monitoring system for seniors

Innovative integration of a series-module membrane distillation plant with a double-effect absorption refrigerator

This study investigates the powering of a series-multistage membrane distillation (DCMD) plant by the heat released from absorber and condenser of a double-effect vapor-absorption refrigerator (VAR). In such a way, two products (freshwater and cooling effect) are obtained with low energy consumption. The feed of the DCMD plant is split to cool the VAR condenser and absorber and get sufficient heat to drive the series-module DCMD plant. Under the applied conditions, the proposed system shows performance better than the standalone VAR and DCMD systems. Also, series-multistage DCMD performs better than the parallel one suggested in the literature under the same operating conditions. The optimal performance indices show that the produced water is 1443 L/h, the cooling capacity is 123.4 ton of refrigeration (TR), the coefficient of performance (COP) is 1.09, the gained output ratio (GOR) is 2.42, the overall energy efficiency (energy utilization factor, EUF) is 3.50, the freshwater cost is 3.87 $/m3, and the cooling effect cost is 0.0047$/kWh

Distribution of ABO and Rh blood groups among pregnant women attending the obstetrics and gynecology clinic at the Jordan University Hospital

Abstract The ABO and D antigen status of red blood cells (Rh blood grouping systems) are important hematological classification systems that categorize blood groups according to the presence or absence of certain erythrocytic antigens. These antigens affect the outcomes of blood transfusions as well as various hematological and immunological diseases. We aimed to study ABO and Rh blood group distribution among pregnant women visiting the antenatal care clinic at Jordan University Hospital (JUH) in Amman, Jordan. A retrospective analysis of all pregnant women delivering at the Jordan University Hospital (JUH) between October 1, 2016, and September 31, 2021. ABO and D antigen status of red blood cells (Rh blood groups) were summarized and documented. 20,136 pregnant women data were analyzed, the O blood group was the most prevalent (n = 7840, 38.9%), followed by A (n = 7506, 37.3%). For the D antigen status, the Rh-positive (Rh+) category was the most common (n = 18,159, 90.2%). For the (O) blood group; O-Rh+ type was the most prevalent (90.1%). Determining the blood group type accurately helps eliminate the critical consequences of both ABO and Rh incompatibility and offers clinicians an opportunity to take timely prophylactic measures. In our analyses O and Rh+ blood groups were the most prevalent

Gender-based differences in burnout during the COVID-19 pandemic: Are female nurses more prone to burnout than males? A meta-analysis

The aim was to investigate the gender-based difference in burnout of nurses during the Covid-19 pandemic. Successful and valuable strategies can be designed to improve nurses’ well-being and to identify, treat, and prevent burnout by recognizing gender-related differences. A systemic search was conducted from electronic databases (PubMed/Medline, Cochrane Library, and Google Scholar) from the inception to 12th FEB 2022. All statistical analyses were conducted in Review Manager 5.4.1. Studies meeting inclusion criteria were selected. A random-effect model was used when heterogeneity was observed to pool the studies, and the results were reported via the standard mean difference (SMD) and corresponding 95% confidence interval (CI). Six cross-sectional studies were selected for meta-analysis. There was significant SMD for burnout in males compared with in females (SMD= −0.10 [−0.20, −0.00]; p= 0.04; I2= 84%). The results of the meta-analysis suggested that the overall burnout rate was more significant in male nurses than in female nurses during the COVID-19 pandemic. There was no difference in emotional exhaustion or personal achievement in both genders. The depersonalization score was more significant in males

Visual versus fully automated assessment of left ventricular ejection fraction

Introduction: The aim of this study is to compare three different methods commonly used in the assessment of left ventricle (LV) ejection fraction (EF) by echocardiography. Methodology: all patients underwent full echocardiography imaging that includes assessment of LVEF using M-mode, Automated EF (Auto-EF) through tracing the myocardial borders during systole and diastole, and visual EF estimation by two readers. Results: We enrolled 268 patients. Auto-EF measurement was feasible in 240 (89.5%) patients. The averaged LVEF was (52%12) with the visual assessment, (51%11) with Auto-EF and (57%13) with M-mode. Using Bland-Altman analysis we found that the difference between the mean visual and the Auto-EF was not significant [−0.3% (−0.5803–0.0053), p = 0.054]. However, we found a significant difference in the mean EF between the visual versus M-mode and Auto-EF versus M-mode with the mean differences: [−2.4365(−2.9946–1.8783), p < 0.0001] and [−2.1490 (−2.7348–1.5631), p < 0.0001] respectively. Inter-observer variability analysis of the visual EF assessment between the two readers showed that intraclass correlation coefficient was 0.953, (95% confidence interval: 0.939–0.965, p < 0.0001), with excellent correlation between the two readers: R = 0.911, p < 0.0001). Conclusion: The two-dimensional echocardiographic methods using Biplane Auto-EF or visual assessment were significantly comparable, whereas M-mode results in an overestimation of the LV ejection fraction