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A Detailed Calcareous nannofossil study conducted on the Bekhme Formation that is cropped out at the Southern limb of the Bekhere anticline, eastern ward of the Dohuk city near Bajlur village, northern Iraq. The Bekhme Formation is composed of marly limestone, Organic limestone and fossiliferous limestone. The nannopaleontological classification of these calcareous nannofossils led us to determine thirty-four genus/species belonging to nine families. The nannobiostratigraphic analysis suggested three calcareous nannofossils biozones from oldest to youngest are: (1) Quadrum sissinghii Interval Zone; (2) Quadrum trifidum Interval Zone; (3) Tranolithus phacelosus Interval Zone. These biozones suggest that the studied section from the Bekhme Formation is the Middle to Late Campanian, and it refers to temperature fluctuations which considered as possible warm time period at subtropical areas.

The top management (TM) is very important to the company. It is concerned with planning, directing, monitoring results, and adjusting strategic plans. This research intends to investigate the connection between TM characteristics and financial company performance. Specifically, this research explores the role of women in this relationship. The sample of the study is 368 firm-year-observations from the listed financial sectors in Saudi Arabia for the 2014–2021 years. The ordinary least squares (OLS) regression analysis is used to determine if TM characteristics have an impact on firm performance. The moderating effect of women in TM on the connection between TM qualities and performance is also tested in this research. This research confirms the primary theories by using attentional tests in conjunction with additional data. We discover that the size of TM has a statistically significant detrimental impact on performance according to the major models. We also find the significant effects of accounting and finance experience of TM, professional certificate of TM, education level of TM, and women in TM on performance. Further analysis reveals that omen in TM has a moderator effect on TM characteristics and company performance. The study contains new empirical data to comprehend how management- and governance-specific elements impact the effectiveness of performance in Saudi Arabia enterprises. In addition, the representation of women in TM has an impact on the association between TM characteristics and performance.

Globally, the Fintech industry has grown in importance as a means of staying ahead of changes in the financial sector. This study aims to assess the influence of Fintech on Saudi Arabia’s finan-cial corporate performance. The sample of the study is the financial service industry comprising banks and insurance companies. Data are gathered from the annual reports and DataStream for the years from 2014 to 2021. Banks and insurance companies make up the bulk of the financial ser-vices industry, which serves as a good representative sample. The associations between the in-dependent and dependent variables in five models are examined using ordinary least squares (OLS) and feasible generalized least squares (FGLS) regressions. The results reveal that Fintech has a positive impact on corporate performance. The results of additional tests are consistent with the main findings. We contributed to the literature and associated theories by shedding light on the impact of fintech on the financial sector. The findings have ramifications for a wide range of parties. Researchers can utilize the study and findings to learn more about Fintech in the context of Saudi Arabia, which stands for the Gulf region and the Arab World.

The purpose of this research was to examine the effects of digital transformation, digital leadership, and entrepreneurial motivation on business decision making and business process performance in the Greater Amman Municipality. The study's hypotheses were put to the test and proven through a variety of quantitative analysis and data processing techniques. The research hypotheses were evaluated using a Structural Equation Model. Participants in this research are managers from the middle and high echelons of the Greater Amman Municipality, who were responsible for making decisions in their respective divisions. One hundred and eighty middle and upper-level managers with at least eight years' experience in public service were recruited for this study from the Greater Amman Municipality. Distributed questionnaires and the deliberate sampling technique were used to compile this data. Decision making had a positive effect on the business process performance, and the results of hypothesis testing data processing using Structural Equation Model indicated that digital transformation, digital leadership, and the motivation of the business environment all had positive and significant effects on business decision making and business process performance in the Greater Amman Municipality. To conduct their research, the authors opted to focus on digital transformation, which includes four primary components: process transformation, business model change, domain transformation, and cultural transformation. The following dimensions commander, communicator, collaborator, and co-creator were all indicative of digital leadership. Entrepreneurial motivation could be seen in the word’s communication, hassle-free work environment, mastering the art of constructive criticism, and trust among others. The novel aspect of this study is the model developed to explain the interplay between digital transformation, digital leadership, and entrepreneurial motivation, and how these factors influence business decision making and business process performance at Greater Amman Municipality.

Background: Parapneumonic pleural effusion is a relatively common entity and continues to be a major cause of morbidity in children. However, managing this disease is still a matter of controversy between surgical and non-surgical options. With the advancement of mini-invasive surgery, video-assisted thoracoscopic surgery (VATS) has become a mainstay in the treatment of parapneumonic effusion in children. This study aimed to evaluate the clinical characteristics and pathological features of parapneumonic pleural effusion in children and to explore the feasibility and safety of the thoracoscopic approach in the pediatric population. Methods: The clinical data of all patients who underwent VATS for parapneumonic effusion between 2007 and 2021 were analyzed retrospectively. Factors that were documented included demographic criteria, clinical manifestations, preoperative examinations, therapeutic procedures, intraoperative findings, postoperative complications, and outcomes. Results: Totally, 35 patients with a mean age of 5.14 ± 3.9 years were operated on thoracoscopically. The mean duration of evolution before VATS was 9 days ± 4. All children were hospitalized in a Pediatric Continuing Care Unit. Antibiotic therapy was administrated in combination in all cases. Corticosteroid therapy was used in 2 patients. Thoracentesis was performed in 6 patients. Thoracostomy tube drainage was placed before surgery in 11 patients. The average duration of drainage before VATS was 6 days ± 4. VATS decortication and/or debridement was indicated as second-line in 23 patients. The average duration of the surgery was 51 minutes (20 min - 115 min). There is no conversion to open surgery and no intraoperative procedure-dependent complication. 4 children have early complications after the VATS and one patient had a late postoperative complication. There were no deaths during the hospital stay or follow-up. Conclusion: In skilled hands, VATS is safe, feasible, and effective in the management of parapneumonic pleural effusion in children with excellent outcomes.

A novel series of urea Schiff base derivatives were synthesized via the condensation of o-phenylenediamine, naphthyl isocyanate and appropriate aryl aldehyde. The results of the in vitro cytotoxic activities of compounds 5a–h against cancer cells lines PC3, SKOV-3 and HeLa, revealed that almost all compounds exhibited good to moderate activities Compound 5g owing bromine atom at p-position displayed higher activity compared to homolog 5b possessing chlorine atom due to adequate diameter of bromine which is more favourable than chlorine for the inhibition activity. In addition, compound 5h is the best candidate of this series exhibiting excellent activity for three cancer cells lines. Compound 5h demonstrated also an excellent activity with IC50 value of 0.6±0.3μg/mL for prostate cancer cell line PC3 and it is considered more effective than the standard drug doxorubicin Dox (IC50 = 2.6±0.03μg/mL). The most active compound 5h displayed the best activity against ovarian cancer cell line SKOV3 with IC50 = 1.8±0.2μg/mL. This results are higher than clinically used drug Dox (IC50. 2.2±0.02μg/mL). The results of screening activities cytotoxic effect toward cervix cancer cell line HeLa, affirm that compound 5h manifest an activity with IC50 value of 2.2±0.4μg/mL comparable to Dox (IC50. 1.9±0.04μg/mL). In the current study, in vivo acute oral toxicity assessment of urea Schiff base hybrid compounds 5a – h indicated that there was no mortality on treated female mice during 14 days assessment test compared with the vehicle-treated group confirming the safety with LD50 greater than 2000 mg/kg. In the actual study, the results affirmed that compounds 5a–h manifested in vivo no toxicity to saint cells, the compounds 5b, 5g and 5h presented higher anticancer activities against three cancer cells which authorizes promoters to use them as candidate anticancer agents.

Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) were fabricated using Plumeria alba leaf extracts to control the mosquito Aedes aegypti. Synthesized AgNPs and AuNPs were characterized by ultraviolet-visible spectroscopy, Fourier-Transform Infrared Radiation (FTIR) spectroscopy, X-ray diffraction (XRD), and transmission electron microscope (TEM) analysis. Susceptibility levels of Ae. aegypti mosquito larvae to the plant extract P. alba and its silver nanoparticles (AgNPs) and gold nanoparticles were determined. The AgNPs and AuNPs spectra displayed their maximum absorption at 300 nm and 500 nm, respectively. The larval mortality of AgNPs and AuNPs were highly effective LC50 were 69.9592 ppm and 88.2635 ppm compared to the aqueous leaf extract of P. alba LC50 was 178.4713 ppm. Furthermore, no significant effects of nanoparticle preparations of P. alba extract 10,000 ppm dose up to one week revealed neither toxic signs nor death within seven days of administration. However, there were no apparent signs of delayed toxicity when the rats were observed for an additional seven days. Current studies revealed that the P. alba leaf extract, AgNPs and AuNPs are biologically safe on animals and eco-friendly for control of Aedes aegypti mosquito.

Background: Low back pain is a very common health problem amongst population and a major cause of disability that affects work performances and well-being. It is can be acute, sub-acute or chronic. It is affects all ages of the population and is the most common reason for medical consultations. The main aims of study:To assessment the level of nurses` knowledge of lower back pain exercises, to identify the difference between nurse`s knowledge level in lower back pain exercise according to demography variables and knowing the percentage of nurses who pass and not pass regarding lower back pain and it`s exercises. Methodology: This study included a sample of (60) nurses, (26) males and (34) females, this study was conducted on the nursing staff in various hospitals. The questionnaire was used to collect the required information and it is composed of (24) questions about lower back pain and it`s exercises in different cases, regarding the score (I strong agree=2, I agree=1, I don't agree=0) where total scores=48 and also include variables (level of education, and years of experiences). And use the statistical program (spss) to analyze the results. The data were analyzed using the Statistical Package for Social Sciences (SPSS), version 26. Conclusions · More than half of the sample they got (16 to 31) score was having medium level of knowledge. · Sample study got (83 %) pass regarding information of lower back pain domain and sample study got (72 %) pass regarding exercise domain of lower back pain. Recommendations · Add physiotherapy in the curricula of health institutes and colleges of nursing. Give training courses for nurses who working at hospital in another unites about how deal with lower back pain and it`s exercises&nbsp

The present paper proposes a novel methodology of turbocharging automotive engines to reach targeted performance. The actual method is tested and validated against simulation test results of two turbocharged diesel engines; engine I, three cylinders, 1.5 L, and engine II, six cylinders, 5.9 L. The present procedure is subdivided into four key parts; namely, database construction, selection procedure, turbocharger preliminary design, and engine modeling. Based on geometric dimensions and aerodynamic parameters provided by the preliminary design procedure, 3D geometries of the turbine and compressor are generated for each studied engine. After integrating previous data into a constructed turbocharger database, two turbochargers are selected for the engine I, while only one turbocharger for the engine II. The findings show that, at the engine speed of 4000 rpm, engine I matched with the adequate turbocharger reached a target power about 2.7%, compared to the original turbocharger equipping engine I. Furthermore, engine II reached a rated power of 299.3 kW at 2500 rpm which is slightly under the original one by 2.64 kW. The superimposition of the engine operating area on compressor and turbine maps provided satisfactory results in terms of turbocharger-engine output performance, fuel consumption, secure functioning and engine thermal strength. Finally, the main advantage of the developed methodology consists of its ability to be applied at both earlier and last stages of the engine turbocharging process or to find new adequate turbochargers to replace the original one for economic, mechanical or for safety reasons.

By transferring power-hungry huge data centers to lightweight Internet of Things (IoT) mobile devices, mobile edge computing (MEC) has completely changed the IoT. For MEC, to optimize economic gains and motivate profit-oriented entities, the joint resource allocation and network economics problem must be solved, and the joint issue is limited by local constraints, namely, the edge server only serves multiple nearby mobile devices, which is restricted by its available energy. The article studies the jointly issue of network economics and energy allocation in MEC, where mobile device apply for offloading at a purported bid and an edge server supplies its restricted serving at an asking price. In particular, this paper puts forward two dynamic pricing double auction strategies in the MEC system, i.e., a double auction according to the break-even mechanism (DABM) and a more practical double auction based on dynamic pricing mechanism (DADPM) to decide the matching between mobile devices and edge servers, and the pricing strategy for high-priced economic profit in the case of local restricts. Theoretical analysis shows that the proposed two algorithms have properties such as budget balance, individual rationality, economic benefit, authenticity. Extensive simulation experiments evaluate the efficiency of the system, and results verify that the proposed two schemes will greatly make better the economic benefits of MEC.

Today’s healthcare system relies on MRI (medical resonance imaging) for early diagnosis and treatment planning. For open MRI systems to achieve resolutions of about a hundred microns, a high voltage is required, as well as a specialized power supply. NP0 (Negative-Positive-Zero) ceramic is selected for the fabrication of adjustable capacitors. Specifically, it stands for which is a classification based on the temperature coefficient of capacitance (TCC) of the ceramic material used in the capacitor. NP0 capacitors have a TCC of 0 ±30 ppm/°C, which means that their capacitance value does not change significantly with temperature and frequency. They are known for their stability and low losses, making them ideal for applications that require high accuracy and reliability, such as timing circuits for RF applications. In this paper, MgTiO-CaTiO ceramic is used to make an adjustable capacitor with desired properties for MRI systems. To enhance the dielectric properties of MgTiO3 ceramics,CaTiO3 was added in varying concentrations. After pressing and sintering, the resulting samples were tested using a vector network analyzer in the frequency range of 10 MHz to 130 MHz.The adjustable capacitor fabricated using high co-fired NP0 ceramic may have been used for MRI applications such as tuning circuits and matching networks, where precise capacitance values and low loss are critical[1]. MRI systems with resonance frequencies of 128 MHz require trimmers with ceramic cores.

Sensitive and environmental friendly sequential method for the determination of nitrate in vegetables was proposed. The method was based on the use of a new combination of Griess reagents (sulfanilic acid and N(1-napthyl) ethylenediamine dihydrochloride) in solid form added directly to nitrate solution in acid medium. The method was optimized for the amounts of charcoal, zinc, diazo-coupling reagents and pH. The validated method had detection and quantitation limits of 0.022 and 0.066 ppm respectively. The dynamic linear range extended between 0.088 and 1.2 ppm (R2 = 0.9999). The method recovery in aqueous solutions was between 96.25% ∓4.40% and 99.70% ∓ 4.20% for seven measurements with an average of 98.52% ∓4.11% indicating high method accuracy and optimum conversion of nitrate to nitrite. The method was successfully applied for the determination of nitrate in two leafy crops (mint and coriander) and two root vegetables (white radish and carrot) collected from five different local markets in the capital of Yemen, Sana’a. The average recovery values of nitrate in the four vegetables matrices ranged between 95.63% and 107.50%. The assessment of nitrate in the vegetables samples revealed that carrots contained the least amount of nitrate (77.90 mg/kg) among the tested vegetables flowed by white radish (641.84 mg/kg), mint (786.86 mg/kg) and coriander (1740.00 mg/kg). The data of the present study confirmed that the proposed method has the required sensitivity to determine nitrate bellow the regulated level in various kinds of leafy and root vegetables.

Collecting and managing taxes is a critical keystone to protecting a country’s financial intensity and developing a country’s tax system, where value-added tax (VAT) has proven to be a nurturing and steady foundation of income for governments. VAT is a primary source of financial gain in developing nations, which differs from economic income in developed nations, where economic income is primarily derived from tax income. This study examines the influence of the new VAT on non-financial Saudi-listed companies. We compared and examined the impact of the newly introduced 15% VAT in various non-financial sectors. Using charts, tables, appropriate variance analysis, and ARIMA model in the Difference in Difference approach. Regarding data, we targeted 2019 before the new VAT, the discovery of COVID-19, and 2020 after the new VAT and during the COVID-19 pandemic. Results show that a sharp increase in VAT (with other unmeasured variables in this study) has a significant positive/negative impact on inter-industry volatility in corporate financial reporting metrics and was even more dramatic with the COVID-19 disaster. The proposed 10% VAT increase has caused significant fluctuations in companies’ metrics, for example, a decrease in average profits—2.16%, an escalation in more inactive companies, and a higher probability of bankruptcy among companies. Such an outcome would undoubtedly affect unemployment and possibly lead to a reduction in tax revenues in the long run. The consequences of an unexpected VAT increase raise the question of whether they accurately reflect the natural tax system in Saudi Arabia. Therefore, the Saudi government may consider the practical implications of outcomes before taking the next step in interventions in non-financial sectors.

This article aims to develop a methodology in which alternative projects are prioritized and selected using appropriate methods of multi-criteria decision-making in organizations responsible for managing and developing health infrastructure. This study addresses a particular gap in implementing a systematic methodology for prioritizing and selecting projects in the health sector. The methodology developed proposes an approach based on the AHP multi-criteria decision support method for decision-makers and stakeholders to prioritize and select projects in the health sector. The problem was modeled using the Expert Choice software which allows a good “integration of the decision-maker” in the decision-making process making it possible to intervene in the research and decisions to identify the most efficient and potentially profitable health projects that are useful to the community.

The energy industry is undergoing a digital transformation, whose goals include increased operational efficiency and reduced energy extraction costs. Data science and machine learning (ML) are enabling the drilling engineering community to contribute to the success of these goals. An ML-based digital solution has been developed to assist the drilling engineer select an optimum bottomhole assembly (BHA) and drilling fluid technology during the well design phase. Traditionally, these selections depended on offset well analysis, which is a manual and time-consuming undertaking. As an alternative, the new digital solution, launched in the form of a web app, automatically selects similar offset wells, and evaluates the available BHA and drilling fluid options from those similar wells. The web app displays these options to the drilling engineer, who is now empowered to make fully informed data-driven decisions.To power the new digital solution, an extensive effort was made to gather, clean, and prepare global operational data into a new database. This operational database includes the selection decisions and performance results of drill bits, motor power sections, rotary steerable systems, BHA configurations, and drilling fluids. After the drilling engineer defines the parameters of the planned drilling run, a multidimensional distance-based approach is used to automatically select the most similar previous drilling runs within the context of the technology selection. The drilling engineer can also fine tune the offset selection based on experience using filters in the web app. Once the most similar offset runs are determined, the technology selection decisions are scored for numerous key performance indicators (KPIs). These KPIs, along with user-defined weights, drive the overall scores. Finally, technology selection recommendations are based on the overall scores and other contextual data such as local availability and cost.The new digital solution has been deployed to a global group of drilling engineers. Feedback sessions are held regularly, and the development team uses this feedback to rapidly iterate and improve user experience. While today's drilling engineers have access to a vast amount of data and information, it often cannot be used in a practical and efficient way. The new solution places all previous drilling system technology selection choices and results into the hands of the drilling engineers, allowing them to make their best decisions. This approach demonstrates how ML and innovative software deployment methods can truly assist the human decision-making process and succeed in accomplishing the goals of digital transformation.To our knowledge, this is a unique approach to drilling system design optimization. Not only is the approach unique, but the database developed as a portion of this effort is likely the largest drilling operations database within the industry. This paper presents all phases of the project, including the details of database creation, data preparation, development of the ML models, and the creation and iteration of the user interface. Finally, this paper presents the future of this effort as part of the company's vision to be our customers’ performance partner of choice.

Childhood-onset systemic lupus erythematosus (cSLE) is a rare multisystem autoimmune disease characterized by inflammatory and autoimmune reaction in several organs. The diagnosis can be difficult due to the heterogeneity of clinical manifestations. Early diagnosis can lead to a proper diagnosis and treatment to control inflammation and to avoid life-threatening complications.

Context: The possible toxic effects of monosodium glutamate (MSG) on ovarian functions have not been thoroughly evaluated in contrast to testicular functions. Few studies documented that MSG showed histological alterations. Aims: To investigate the subacute effects of oral MSG (2 g/kg) on estrogen level and numbers of ovulated oocytes and histological changes in ovary of Sprague-Dawley (SD) rats. Methods: Virgin young adult SD female rats with a regular estrous cycle were randomly assigned to either MSG-treated group or control group, and the duration of treatment was 14-16 days for oral administration MSG or vehicle (distilled water), respectively. Results: Oral MSG treatment with doses of 2 g/kg/day showed significantly (p

In this study, we aimed to examine the effect of visual realism on soccer players’ memorization of soccer tactics according to their level of expertise and visuospatial abilities. We divided 48 volunteers into novice and expert soccer players and had them first perform a multitask visuospatial abilities (VSA) test and then undergo training with three dynamic soccer scenes, each presented with varied levels of realism (schematic, moderately realistic, and highly realistic). We then tested players’ memorization and reproduction of the soccer scenes and measured their visual processing with eye-tracking glasses to identify their cognitive processes during memorization. We found that reducing visual realism improved visual processing and memorization when compared to higher realism ( p < .001). Second, both higher (vs. lower) player expertise and higher (vs. lower) VSA influenced visual processing and enhanced memorization efficiency ( p < .001). Third, there were significant interaction effects between visual realism, player expertise, and player VSA ( p < .001) such that players with high VSA benefited more from reduced (vs. accentuated) visual realism than did players with low VSA. Thus, increasing visual realism can hinder tactical learning effectiveness, especially for learners who lack domain expertise and visuospatial abilities. Practically speaking, coaches and educators might improve their communications by tailoring tactical instructions to learners’ cognitive skills.

Africa, like the rest of the world, has been impacted by the coronavirus disease 2019 (COVID-19) pandemic. However, only a few studies covering this subject in Africa have been published.We conducted a retrospective study of critically ill adult COVID-19 patients-all of whom had a confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection- admitted to the intensive care unit (ICU) of Habib Bourguiba University Hospital (Sfax, Tunisia).A total of 96 patients were admitted into our ICU for respiratory distress due to COVID-19 infection. Mean age was 62.4±12.8 years and median age was 64 years. Mean arterial oxygen tension (PaO2)/fractional inspired oxygen (FiO2) ratio was 105±60 and ≤300 in all cases but one. Oxygen support was required for all patients (100%) and invasive mechanical ventilation for 38 (40%). Prone positioning was applied in 67 patients (70%). Within the study period, 47 of the 96 patients died (49%). Multivariate analysis showed that the factors associated with poor outcome were the development of acute renal failure (odds ratio [OR], 6.7; 95% confidence interval [CI], 1.75-25.9), the use of mechanical ventilation (OR, 5.8; 95% CI, 1.54-22.0), and serum cholinesterase (SChE) activity lower than 5,000 UI/L (OR, 5.0; 95% CI, 1.34-19).In this retrospective cohort study of critically ill patients admitted to the ICU in Sfax, Tunisia, for acute respiratory failure following COVID-19 infection, the mortality rate was high. The development of acute renal failure, the use of mechanical ventilation, and SChE activity lower than 5,000 UI/L were associated with a poor outcome.

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

The most common neurological symptom is a headache, which may be a sign of something dangerous like a brain tumor or something relatively harmless like a migraine or tension headache. Although migraine is more common and more debilitating, most of population suffer from it. Since most of population are continuously under pressure, it was important to assess the prevalence of migraine headaches among this population. Lack of research concentrating on migraine headache among population encouraged this study.

Background: Chronic kidney disease is a worldwide public health issue which is associated with an increased risk of end-stage renal failure and cardiovascular disease. Systemic inflammation exists during chronic renal failure. Recent researches have highlighted the pivotal role of inflammation between renal and cardiovascular disease. The aim of our study is to determine the inflammatory profile of the patient suffering from chronic kidney disease and the influence of hemodialysis on this profile. Methods: We carried out a cross sectional study on 93 patients in the Nephrology Department at Hedi Chaker University Hospital, Sfax, South of Tunisia. Among those patients, 72 patients underwent hemodialysis and 21 patients had chronic kidney disease at stage 3. Clinical data and antecedents were collected. Biological samples were taken after informing the patients and taking their consent. Biological data consisted in lipid profile, albumin rate, hemoglobin rate, uric acid concentration and the usual markers of inflammation noting sedimentation rate, C - reactive protein and orosomucoid. Results: Hemodialysis group of the 72 patients had mean hemodialysis vintage of 54.6 ± 43 months. The inflammatory profile was worse in hemodialysis patients compared to chronic kidney disease patients. Both sedimentation rate, C - reactive protein and orosomucoid were higher in hemodialysis group than in chronic kidney disease group with 71 ± 35.3 mm vs. 42.1 ± 15.5 mm (p < 0.05); 14.6 ± 28.7 mg/l vs. 6.7 ± 8 mg/l (p = 0.02); 1.3 ± 0.7g/l vs. 0.9 ± 0.4 g/l (p = 0.01), respectively. Conclusion: Inflammation increases in dialysis patient. It deserves the nephrologist’s consideration in order to minimize its harmful effects. The monitoring of inflammation markers must be integrated into the nephrologist’s medical practice.

Seventeen samples of Hartha Formation in Balad (1) well, central Iraq, are studied on the basis of stratigraphic ranges of the recorded calcareous nannofossils for twenty species belonging to twelve genera. The studied section reveals three biozones arranged from oldest to youngest as follows; (1) Calculites ovalis Interval Biozone (CC19), (2) Ceratolithoides aculeus Interval Biozone (CC20), (3) Quadrum sissinghii Interval Biozone (CC21). These Biozones are correlated with other calcareous nannofossils biozones from both local and regional sections, leading to conclude the age of the Middle Campanian. Rerecorded eighteen ostracode species that belong to eleven genera are identified, all of which were previously recorded from Iraq and adjacent regions. The occurrence of these species leads to conclude a continental shelf environment, while they are typical of inner shelf-outer shelf depth.

Cloud computing enables clients to acquire cloud resources dynamically and on demand for their cloud applications and services. For cloud providers, especially, Software as a Service (SaaS) providers, the prediction of future cloud resource requirements, such as CPU usage for their cloud applications, to implement client requests is a complex task because it depends on incoming workloads. Due to workload fluctuations, it is difficult for SaaS cloud providers to predict or forecast future demand for resource usage in the next time interval and, accordingly, to allocate the required resources. Furthermore, cloud computing systems consist of many virtual machines (VMs), which increases the complexity of the prediction problem due to the correlations that exist between the large workload data in these VMs. Therefore, accurate resource usage forecasting remains a challenge, and relatively few studies have explored the prediction of CPU usage for VMs in cloud data centers. This paper proposes an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads. The effectiveness of the proposed DCRNN-based deep learning model was evaluated using experiments on a real-world dataset of PlanetLab’s CPU usage traces. The results indicate that the proposed approach outperformed other existing deep learning models, achieving a mean absolute percentage error of 0.18 and root-mean-square error of 2.40.

Almost 5% of the world's population develops an autoimmune disease (AID), it is considered the fourth leading cause of disability for women, who represent 78% of cases. The sex ratio when it comes to the most prevalent AID varies from 9:1 in systemic lupus erythematosus (SLE) to 13:1 in endemic Tunisian pemphigus foliaceus (PF).To test the potential involvement of skewed x-inactivation in the pathogenesis of Tunisian PF, we analyzed the methylation status of a highly polymorphic CAG repeat in the androgen receptor gene and evaluated the x chromosome inactivation (XCI) patterns in peripheral blood-leukocyte-derived DNA samples of female patients with PF (n = 98) compared to healthy control (HC) subjects (n = 150), as well as female patients with SLE (n = 98) were enrolled as a reference group.XCI status was informative for 50 of the 98 PF patients (51%) and 70 of the 150 HC women (47%). Extremely skewed XCI patterns were more frequent in PF and SLEwomen than HC, but the difference was statistically significant only in women with SLE. No statistical difference was observed in XCI patterns between PF and SLE patients. PF phenotype-XCI correlation analysis revealed that (i) skewed XCI patterns may be involved in the disease's subtype and (ii) it was more pronounced in the endemic group than the sporadic one. Furthermore, preferential XCI showed an increase in heterozygote genotypes of PF's susceptibility polymorphisms in immunity-related X genes (FOXP3, AR, and TLR7) in PF patients compared to HC.Our results suggest that skewed XCI could lead to hemizygosity of X-linked alleles that might unmask X-linked deleterious alleles.