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The current work aimed to enlarge freshwater production and improve tubular solar still (TSS) performance via pure and nano-coated (with carbon black nanoparticles) mushrooms. Pure mushrooms were used due to their structure that allows effective vapor generation through achieving good solar absorptivity, capillary effect, and heat localization on the surface. In addition, carbon black coating was proposed to improve surface absorptivity, heat localization, and thermal conductivity. The performance of the developed TSS (DTSS) was evaluated, compared to conventional TSS (CTSS), through a series of tests: first using pure mushrooms, and then via three quantities of carbon black (CB) nanocoating (25, 50, and 75 g/m2). Besides, the thermal performance was analyzed from the heat transfer coefficients, energy and exergy efficiencies points of view. Finally, a feasibility study was conducted by calculating the cost per liter of the yield for all cases. The results revealed that, compared to CTSS, the productivity increased by 59.05%, achieving a saving in production cost by 33.85 % using nano-coated mushrooms (DTSS with 50 g/m2). Besides, the thermal performance was enhanced by 55.38 and 129.95% for energy, and exergy efficiencies, respectively. Hence, the proposed work introduced an effective and feasible improvement in the field of TSSs.

Background: Premature ejaculation (PE) is a significant and common medical problem affecting many men worldwide. While PE is not a life- threatening disorder, its effect on quality-of-life (QoL) issues is significant. Objective: To evaluate the feasibility, safety and efficacy of pulse radiofrequency (PRF) of dorsal nerve of the penis in lifelong premature ejaculation refractory to conventional treatment. Patients and Methods: In this prospective study, all adult males complaining from lifelong PE resistant to conventional treatment, and seeking treatment of PE, at Al-Azhar University Hospitals [Al-Hussein and Sayed Galal]; Cairo; Egypt during the period from December 2019 to July 2020 were studied. Detailed history, physical examination, serum testosterone, serum prolactin were obtained. Patients underwent ultrasound guided pulsed radiofrequency of dorsal nerve of the penis. Any operative complications were recorded. To assess the efficacy of the procedure, patients were evaluated by intra vaginal ejaculatory latency time (IELT) and sex satisfaction scale (SSS). Results: As regard SSS, 3 weeks before intervention, all the subjects (20 subjects) were unsatisfied. Three weeks post intervention, 9 (45%) of subjects were satisfied, while 11 (55%) were unsatisfied. Four months post intervention, 8 (40%) subjects were satisfied, and 12 (60%) were unsatisfied SSS 3 weeks post intervention was significantly different from SSS 3 weeks before intervention and 4 months post intervention (p < 0.001). The mean of IELT was 33.4±19, 188.4±154.9, and 141.5±129.7 seconds at 3 weeks before PRF, 3 weeks after PRF, and 4 months after PRF, respectively. A significant difference was found between IELT pre intervention, 3 weeks and 4 months post intervention (p < 0.001). A strong positive correlation was found between IELT and SSS at 3 weeks and 4 months post intervention (p < 0.001& r = 0.78) and (p < 0.001& r = 0.91) respectively. In our study, 4 (20%) patients had pain that relieved by analgesic and 2 (10%) patients had minimal bleeding at site of needle insertion & resolved by compression. No subjects complained of erectile dysfunction nor loss of sensation, while 2 (10%) patients reported superficial infection in first week post intervention which may contribute to uncontrolled DM, 4 (20%) patients had tingling within the first 3 weeks, and 2 (10%) had numbness within the first 3 weeks. All these complications resolved with conservative treatment. Conclusion: PRF is a hopeful treatments in life long PE. Objective data on change in sensation (biothesiometry) and the short term of objective follow-up data are the significant limitations of this study.

The main objective of the research is identifying the effectiveness of artificial intelligence in the business sphere of Kazakhstan. The urgency of this problem is due to the fact that the Kazakhstani market for artificial intelligence is at the initial stage of development. The main obstacle to the introduction of artificial intelligence is the unpreparedness of managers of small and medium-sized businesses for the application of artificial intelligence technologies and, of course, the high cost of their implementation. In the study, we proceeded from the key thesis that business in Kazakhstan is striving for digital transformation. We set a goal to determine the attitude and degree of readiness of Kazakhstani business to the implementation and practical application of artificial intelligence, to describe the cases of using artificial intelligence by Kazakhstani business, to identify the main questions that arise in business at this stage, to study the legal aspects of using artificial intelligence in business and to present the big picture compliance / inconsistency of the existing legal framework with the goals and objectives of the development of artificial intelligence, provide recommendations for eliminatinge xisting barriers and stimulating businesses to implement the technology. Within the framework of this study, the concept of artificial intelligence is defined in its broadest sense - as a set of technologies for processing various types of data and information, in particular those capable of interpreting such data, extracting knowledge and using it to achieve certain goals.

Background: Brachial plexus block is frequently used for ambulatory upper limb surgery. It can significantly reduce pain and nausea, allowing for faster discharge from hospital when compared with general anesthesia (GA). Doing this block through ultrasound has higher index of safety, allowing providers to identify the appropriate point of injection and performing this block with greater accuracy under direct visualization. Objective: the aim of the study was to compare the efficacy of bupivacaine plus dexamethasone versus bupivacaine plus dexmedetomidine as adjuvants on block characteristics including onset and duration of sensory and motor blockade, duration of postoperative analgesia, post-operative pain score and sedation score. Patients and Methods: This prospective double – blinded, randomized controlled trial included a total of 60 patients divided to two groups, each group contained 30 patients. Group 1 (dexmedetomidine group) had received 1μg/kg of dexmedetomidine + 20 ml bupivacaine 5%. Group 2 (dexamethasone group) had received dexamethasone + 20 ml of bupivacaine5%. Results: In this study the onset and duration of sensory and motor blockade was statistically shorter in dexamethasone group when compared to dexmedetomidine group. Conclusion: It could be concluded that addition of dexmedetomidine to bupivacaine prolongs the time of block and analgesia duration longer than dexamethasone, but the onset of block was shorter when dexamethasone was added to bupivacaine.

Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors' performance. Designing high-performance processors is a complex task and requires preliminary verification and analysis of the model level, usually used in analytical and simulation modeling. The refinement of extreme programming is an unfortunate challenge. Few experts disagree with the synthesis of access points. This article demonstrates that Internet QoS and 16-bit architectures are always incompatible, but it's the same situation for write-back caches. The solution to this problem can be implemented by analyzing simulation models of different complexity in combination with the analytical evaluation of individual algorithms. This work is devoted to designing a multi-parameter simulation model of a multi-process for evaluating the performance of cache memory algorithms and the optimality of the structure. Optimization of the structures and algorithms of the cache memory allows you to accelerate the interaction of the memory process and improve the performance of the entire system.

Management Information Systems and technology have an important role in upgrading the efficiency of a company's operations and supply chains, which contributes to its profitability. It structures and regulates provided data to manage the system of a company. This research is aimed to determine the influence of Management Information Systems and IT on the business profitability based on the example of Air Astana flag carrier of Kazakhstan.The paper tests the hypothesis whether the presence of management information systems affect company profitability or not. As local company, Air Astana was taken, and the main data is from a financial statement on their website. The least squares method is used, and the coefficient of MIS is taken as a dummy variable, so that we can prove our hypothesis. The main goal of this article is to show the effectiveness and efficiency of management information systems on the example of a local company.

Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.

This article considers the task of handwritten text recognition using attention-based encoder&ndash, decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value &lt, 0.05) in the sensitivity (recall) over the tests dataset. The proposed method&rsquo, s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

Nowadays, the business market is more complicated and comprises many challenges; it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on K-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. K-means algorithm identifies k- number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and K-means algorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.

Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems is the lack of answers to any given medical/health-related question. The Internet is full of forums that allow people to ask some specific questions and get great answers for them. Nevertheless, browsing these questions to locate a similar case to your own question, also finding a satisfying accurate answer is difficult and timeconsuming task. This research will introduce a solution to these problems by automating the process of generating qualified answers to these questions and creating a kind of digital doctor. Furthermore, this research will train an end-to-end model using the framework of RNN and the encoder decoder to generate sensible and useful answers to a small set of medical/health issues. The proposed model was trained and evaluated using data from various online services, such as WebMD, HealthTap, eHealthForums, and iCliniq.

Several studies have been published to analyze different approaches to traditional text classification methods. Most of these studies cover the application of certain methods of semantic terminology to textual classification to a certain extent. However, they are not specifically aimed at the classification algorithms for semantic text and their advantages over the traditional text classification.As it is known, Kazakhstan is a multinational country and Russian is a transnational language. Therefore, it was decided to develop a tool for processing the incoming text in different languages. Actually, there are some existing tools that process text in English, but they do not support some foreign languages, especially that are in use in Kazakhstan.The main goal is to analyze some Machine Learning (ML) algorithms and develop a sentimental text processing tool using those algorithms to make a web page for text processing through categorization and sentimental analysis. It was investigated that the best way to implement Machine Learning and Natural Language Processing (NLP) algorithms on Python is to create a web-page using Django framework. The principle of work is to get the text as input, process it with ML algorithms and send result as a «.json» file.

This paper aims to propose a conceptual design model for sustainable hybrid renewable stand-alone energy system (HRSES) to meet the electricity demand of a large-scale reverse osmosis desalination plant in Baltim, Egypt. The model investigates the feasibility of different HRSES alternatives and develop a fuzzy-based multicriteria decision-making model for meticulously selecting the optimal energy solution. Both zinc-bromine flow battery and turbine-pumped hydro energy storage technologies are integrated independently with wind, solar, and diesel power sources. Firstly, the proposed model uses HOMER software to execute an energy-economic-ecological optimization analysis for studying the practicability and components sizing of nine HRSES alternatives. Second, an integration between Fuzzy-AHP and Fuzzy-VIKOR decision-making methods is executed to choose the best design considering ten performance criteria. In the second stage, the fuzzy environment is engaged to expedite decision-makers to express their ratings in linguistic terms and to achieve more sensible and accurate results. Among ten feasible alternatives, the results reveal that the optimal system consists of 5 × 20-kW wind turbines, 328-kW photovoltaic array, 100-kW diesel generator, 112 batteries and 235-kW converter. This system has the best economic performance among all alternatives with least NPC, COE, and payback-period of $1,048,046, 0.101$/kWh and 1.1 yr, respectively. Besides, it has a treasured share of renewable of 95.55%; hence, it produces a realistic CO2 of 25,426.46 kg/year. Lastly, the sensitivity analysis illuminates that the future load growth and low-interest rate hurt upcoming investments while the projected reduction in the cost of energy storages has an encouraging influence on financing decisions.

Several human genetic variants, HLA antigens and alleles are reportedly linked to post-schistosomal hepatic disorder (PSHD), but the results from these reports are highly inconclusive. In order to estimate overall associations between human genetic variants, HLA antigens, HLA alleles and PSHD, we systematically reviewed and performed a meta-analysis of relevant studies in both post-schistosomal hepatic disorder and post-schistosomal non-hepatic disorder patients. PubMed, Scopus, Google Scholar, The HuGE Published Literature database, Cochrane Library, and manual search of reference lists of articles published before July 2009 were used to retrieve relevant studies. Two reviewers independently selected articles and extracted data on study characteristics and data regarding the association between genetic variants, HLA antigens, HLA alleles and PSHD in the form of 2×2 tables. A meta-analysis using fixed-effects or random-effects models to pooled odds ratios (OR) with corresponding 95% confidence intervals were calculated only if more than one study had investigated particular variation. We found 17 articles that met our eligibility criteria. Schistosoma mansoni and Schistosoma japonicum were reported as the species causing PSHD. Since human genetic variants were only investigated in one study, these markers were not assessed by meta-analysis. Thus, only HLA-genes (a total of 66 HLA markers) were conducted in the meta-analysis. Our meta-analysis showed that human leucocyte antigens HLA-DQB1*0201 (OR=2.64, P=0.018), DQB1*0303 (OR=1.93, P=0.008), and DRB1*0901 (OR=2.14, P=0.002) alleles and HLA-A1 (OR=5.10, P=0.001), A2 (OR=2.17, P=0.005), B5 (OR=4.63, P=0.001), B8 (OR=2.99, P=0.02), and B12 (OR=5.49, P=0.005) serotypes enhanced susceptibility to PSHD, whereas HLA-DQA1*0501 (OR=0.29, P≤0.001) and DQB1*0301 (OR=0.58, P=0.007) were protective factors against the disease. We further suggested that the DRB1*0901-DQB1*0201, DRB1*0901-DQB1*0303 and A1-B8 haplotypes enhanced susceptibility to PSHD, whereas DQA1*0501-DQB1*0301 linkage decreased the risk of PSHD. The result improved our understanding of the association between the HLA loci and PSHD with regard to pathogenic or protective T-cells and provided novel evidence that HLA alleles may influence disease severity., Parasitology International, 60(4), pp.347-356; 2011