Application of Ozone and Peroxone Processes for Naphthenic Acids Degradation in Oil Sands Process-Affected Water: Characterization of Water Before and After Treatment

Appling ozone (O3) with high doses (>100 mg/L) to remove naphthenic acids (NAs) from oil sands process-affected water (OSPW); limits its application and feasibility in the OSPW remediation. To decrease the required doses and their associated costs, this study examined the application of ozone (O3) and peroxone (hydrogen peroxide/ozone; H2O2:O3) processes for the treatment of OSPW using mild oxidant doses (i.e., ozone doses of 30 and 50 mg/L and H2O2 doses of 10, 11 and 20 mg/L). The performance of both processes was compared in terms of structure reactivity of NAs, the dominant pathways for removal, the kinetics of individual NA species and variation of compositions and abundance of species before and after treatment. To attain/ensure better characterization for the contaminants of concern (NAs) in water samples, the initial phase of this research encompassed examining two different analytical methods (Fourier transform infrared (FTIR) spectroscopy and ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOFMS)) with different extraction/pre-treatment methods for samples; liquid-liquid extraction (LLE) and solid-phase extraction (SPE). A correlation between these methods was developed to implement the best techniques available for sample analysis. The results after examining groundwater and OSPW samples showed higher recovery of classical and oxidized NAs or (Ox-NAs) and naphthenic acid fraction compounds (NAFCs) for SPE compared to LLE, regardless the water source and quantification methods (i.e., FTIR and UPLC-TOFMS). However, higher abundance for classical NAs (O2-NAs) was found in LLE than SPE (e.g., OSPW samples: (63.1±2.1%) versus (58.5±3.0%)). A strong correlation was observed between the UPLC-TOFMS and FTIR which highlights the possibility of using FTIR and Fluka as a standard with LLE pretreated samples as an affordable substitute to the high resolution techniques (e.g. UPLC-TOFMS). In the second phase of this research, the structure reactivity and reaction pathways during ozonation and peroxone treatment were investigated. Suppressing the hydroxyl radical (•OH) pathway by adding the scavenger tert-butyl alcohol did significantly reduce the degradation in all treatments, while molecular ozone contribution was 50% and 35% for O2-NAs and Ox-NAs, respectively. Structure reactivity was observed with a degradation increase for both O2-NAs and Ox-NAs with the increase of both carbon (n) and hydrogen deficiency (i.e., |-Z| numbers, double bond equivalent (DBE)) for all treatments. The variations in the compositions of treated water were evaluated using two different high resolution mass spectrometry methods; UPLC-TOFMS and Fourier transform ion cyclotron resonance. Assessing two markers (O2S:O3S:O4S and O2:O4 ratios) revealed changes and similarities of the peroxone treated water (i.e., 20 mg/L H2O2: 50 mg/L O3 at 1:2 ratio) to natural waters. Both ratios decreased from 2.7:4.8:2.1 and 3.59 in raw OSPW to 0:1.4:0.5 and 0.7, respectively, becoming close to the reported ratios in natural waters. Although peroxone (1:2) 20+50 (i.e., 20 mg/L H2O2: 50 mg/L O3) and 50 mg/L ozone were the two most effective treatments to degrade O2-NAs and Ox-NAs (e.g., for O2-NAs: 86% and 84%, respectively) as well as to reduce the toxicity toward Vibrio fischeri (40% and 50%, respectively), the fastest kinetics treatments were observed at peroxone (1:1) 20+30 (i.e., 20 mg/L H2O2: 30 mg/L O3) and 30 mg/L ozone (i.e., reaction rate constant of 0.236 min-1 and 0.251 min-1, respectively). The increase of the DBE increased the reaction rate constant, specifically at DBE = 7-9 with similar values at DBE =3-6. With respect to in vitro assays, while the highest production of nitrite (i.e., attributed as the lowest toxicity effects on the goldfish primary kidney macrophages) was observed in peroxone (1:2) 11+30 (i.e., 11 mg/L H2O2: 30 mg/L O3) followed by peroxone (1:3) 10+50 (i.e., 10 mg/L H2O2: 50 mg/L O3), their O2-NA degradation was the lowest, 47% and 61%, respectively. The residual toxic effects after different ozone and peroxone processes, suggest that part of OSPW toxicity may be caused by specific compounds of NAs (i.e., similar reduction (50%) was achieved in both toxicity and abundance in O2 species with carbon 15-26) and/or generated by-products (e.g., O3S classes at DBE = 4 and C9H12O2 at DBE = 4). Although by-products were generated, slight enhancement in the biodegradability and the reduction of chemical oxygen demand was achieved in peroxone at 1:2 ratio compared to ozone, suggesting the possibility of using combined OSPW remediation approaches (i.e., peroxone coupled with biological process)

The Impact of Political Variables on the Ottoman-European Hegemonic Conflict in the Mediterranean before the Battle of Lepanto in 1571 CE

The political variables that contributed to the development of hostilities between European states and the Ottoman Empire in the region will be examined in the paper The Impact of Political Variables on the Ottoman- European Hegemonic Conflict in the Mediterranean before the Battle of Lepanto in 1571 CE . The paper examines how these elements affected the state-state struggle and concentrates on the time leading up to the Battle of Lepanto in 1571 CE. The study will go into the alliances and diplomatic ties between states that contributed to the conflicts escalation as well as other political and military factors. The study will also look at how the conflict has affected the societies involved and the political ties between governments. The study will employ archival materials and a range of references to present a thorough overview of the conflict and its effects on the political variables. Additionally, both analytical and inductive methodologies will be used in our work

Digital Processing of Satellite Imagery ‎to Determine the Spatial Distribution ‎‎of ‎Vegetation Cover in the Neighborhoods ‎of Ramadi City

Objectives:1- The study aims to find out  the vegetation cover in the city of Ramadi is one of the important means in collecting data that document these sites geographically and classifying them Determine the effectiveness of using modern geographical techniques in representing the vegetation in the city of Ramadi.2- Building a geographic database (Geodatabase) to represent single-reference spatial data for vegetation in the city of Ramadi.3- Analyzing and evaluating vegetation patterns in the neighborhoods of Ramadi.Method:1- Study and analyze modern geographical tools and techniques available to represent vegetation cover.2- Collecting single-reference spatial data for the vegetation cover of the city of Ramadi.3- Applying and testing geographical methods for drawing high-resolution vegetation maps.4- Developing a geographic database that contains layers of spatial data related to vegetation.5- Analyzing spatial data to determine vegetation patterns and analyze gaps and challenges.6- Evaluate the results and provide recommendations to improve the representation of vegetation in the city of Ramadi.Results:1-The study confirmed that remote sensing techniques and geographic information systems are considered an important and accurate source of information about studying the vegetation of the study area during the period 2021..3- The study emphasizes the importance of geographic information systems technology in the process of improvement and classification because it facilitated many difficulties for the researcher by reducing time, effort, and speed of completion.4- Remote sensing is considered an important scientific resource in detecting changes in land cover and land uses through the rich data it provides, which is characterized by comprehensiveness and accuracy in determining changes in the features of the Earth’s surface, with the possibility of monitoring these changes over successive periods of time.Conclusion:1- Enhancing the use of modern geographical techniques in representing vegetation in the city of Ramadi, with a focus on developing high-accuracy mapping methods.2- Strengthening efforts to collect single-reference spatial data on vegetation in the city of Ramadi, which contributes to building an integrated geographical database.3- Stimulating future research and studies to use spatial data and geographical analysis techniques to understand and analyze vegetation patterns in the city of Ramadi in a more accurate and detailed manner.4- Strengthening cooperation between stakeholders, including the local government, universities and research institutions, to develop innovative strategies and solutions to represent and manage the green cover in the city

Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls. Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the Radiology Society of North America (RSNA), and the Kaggle Chest X-ray Database for COVID-19 (n = 240), Viral Pneumonia (n = 274), and Normal Controls (n = 267). Images were resized, normalized, without any augmentation, and arranged in m batches of 16 images before supervised training, testing, and cross-validation of the DCNN classifier. Results: The ResNet-34 had a diagnostic accuracy as of the receiver operating characteristic (ROC) curves of the true-positive rate versus the false-positive rate with the area under the curve (AUC) of 1.00, 0.99, and 0.99, for COVID-19 and Viral Pneumonia patient and Normal control CXR images; respectively. This accuracy implied identical high sensitivity and specificity values of 100, 99, and 99% for the three groups, respectively. ResNet-34 achieved a success rate of 100%, 99.6%, and 98.9% for classifying CXR images of the three groups, with an overall accuracy of 99.5% for the testing subset for diagnosis/prognosis. Conclusions: Based on this high classification precision, we believe the output activation map of the final layer of the ResNet-34 is a powerful tool for the accurate diagnosis of COVID-19 infection from CXR images

Multicenter Study of Brucellosis in Egypt

Brucellosis causes appreciable economic losses in livestock. Examination of milk and tissues from animals in Egypt for Brucella spp. showed increased prevalence rates of serologically reactive animals. All isolates were B. melitensis biovar 3. One Brucella sp. was isolated from milk of serologically nonreactive buffaloes

LA LITERATURA INFANTIL EN ESPAÑA Y EN EL MUNDO ÁRABE: LA FUNCIÓN DIDÁCTICA Y LINGÜÍSTICA DEL CUENTO EN ESPAÑA Y JORDANIA

In this comparative study between Spain and the Arab world, specifically Jordan, the didactic functions of children's stories will be analyzed. This study has been carried out from surveys to know the influence and importance of this type of children's literature in the education of children.  En este estudio comparativo entre España y el mundo árabe, concretamente Jordania, se analizarán las funciones didácticas de los cuentos para niños. Este estudio se ha realizado a partir de encuestas para conocer la influencia y la importancia de este tipo de literatura infantil en la educación de los niños. Abstract In this comparative study between Spain and the Arab world, specifically Jordan, the didactic functions of children's stories will be analyzed. This study has been carried out from surveys to know the influence and importance of this type of children's literature in the education of children.        

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112

EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning

A neuro-developmental disorder known as autism spectrum disorder (ASD) affects a significant portion of the global population. Those with ASD frequently struggle to interact and communicate with others and may engage in restricted or repetitive behaviors or interests. The symptoms of autism begin early in childhood and can continue into adulthood. Machine learning and deep learning (DL) models are employed in clinical research for the early identification and diagnosis of ASD. However, the majority of the existing models lack interpretability in their results for ASD diagnosis. The explainable artificial intelligence (XAI) concepts can be used to provide transparent and understandable explanations for models’ decisions. In this work, we present an explainable autism spectrum disorder model based on DL for autism disorder detection in toddlers and children. The primary objective of this study is to better understand and interpret the classification process and to discern the significant features that contribute to the prediction of ASD. The proposed model is divided into two distinct components. The first component employs a DL model for autism disorder detection. The second uses an XAI technique known as shapley additive explanations (SHAP) to emphasis key characteristics and explain the model’s outcomes. The model showed perfect performance on the training set, with an accuracy of 1 and a receiver operating characteristic score of 1. On the test set, the model achieved an accuracy score of 0.9886, indicating that it performed nearly as well as on the training set. The experimental results demonstrate that the proposed model has the capability to accurately predict and diagnose ASD while also providing explanatory insights into the obtained results. Furthermore, the results indicate that the proposed model performs competitively compared to the state-of-the-art models in terms of accuracy and F1-score. The results highlight the efficacy and potential of the proposed model in accurately predicting ASD in binary classification tasks