Search results for: urea deep placement

Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Vishnu Nair, Rohan Kapoor

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Recent commercial aircraft crashes demand a paradigm shift in how the CVDRs are located and recovered, particularly if the aircraft crashes in the sea. CVDR (Cockpit Voice and Data Recorder) is most vital component out of the entire wreckage that can be obtained in order to investigate the sequence of events leading to the crash. It has been a taxing and exorbitantly expensive process locating and retrieving the same in the massive water bodies as it was seen in the air crashes in the recent past, taking the unfortunate Malaysia airlines MH-370 crash into account. The study aims to provide an aid to the persisting problem by improving the buoyant as-well-as the aerodynamic properties of the proposed CVDR encasing. Alongside this the placement of the deployable CVDR on the surface of the aircraft and floatability in case of water submersion are key factors which are taken into consideration for a better resolution to the problem. All of which results into the Deployable-CVDR emerging to the surface of the water-body. Also the whole system is designed such that it can be seamlessly integrated with the current crop of commercial aircraft. The work is supported by carrying out a computational study with the help Ansys-Fluent combination.

Keywords: encasing, buoyancy, deployable CVDR, floatability, water submersion

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Authors: G. E. Forcados, M. L. Shu, C. N. Chinyere

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Acalypha wilkesiana is a medicinal plant commonly used in most parts of West Africa as a decoction in treating several human diseases. Existing literature on its toxicity is predominantly on the organic extracts in contrast to the routine use of hot aqueous extracts as decoction. The aim of this study was to examine the phytochemical profile and sub-acute toxicity of A. wilkesiana leaf extracts in albino rats. Three groups of 8 experimental rats each were administered 300 mg/kg, 600 mg/kg and 1200 mg/kg body weight per day for 14 days while a fourth (control) group took tap-water. On day 15, the rats were sacrificed, and blood collected. Biochemical and hematological parameters were analysed and histopathological examination of liver and kidney were performed. There was significant increase (p<0.05) in the levels of some biochemical parameters (AST, ALT, creatinine, urea) in all the test groups compared to control. Histopathological examination of the liver revealed centrilobular degeneration and necrosis with sinusoidal dilatation as well as polymorphonuclear and mononuclear infiltration, likewise severe glomerular and tubular degeneration and necrosis with hemorrhage in the kidney at all dose levels. The results from this study suggest that aqueous leaf extract of A. wilkesiana is hepatotoxic and nephrotoxic at dose levels of 300 mg/kg and above. Therefore, precautionary measures are necessary for home use of the leaf extract of A. wilkesiana.

Keywords: acute toxicity, A. wilkesiana, aqeous extract, albino rats, biochemical and haematological parameters, histopathological examination

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Authors: Unity Nkateng

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New forms of academic writing such as apprenticeship genres are developing in the field of applied linguistics. However, these perspectives have not adequately addressed the issue of social work students in Botswana. The paper addresses the issue of academic writing with special attention to the types of documents written by University of Botswana (UB) social work students on their fieldwork placement. The research method for this study combines two major research tools in the qualitative inquiry which are text analysis and interviews in order to investigate the context in which the texts are produced. 12 students were consulted and gave their consent for the study. 26 case reports were collected from the Department of Social work at the University of Botswana. The findings show that the case reports students write during their fieldwork placements have 6 moves, which focus on the clients’ story and describe what the students have done and achieved. The significance is that the discrepancy between professional writing and students writing raise questions about the extent to which students are being prepared for professional writing. Students have indicated that their academic writing varies according to the preferences of individual lecturers rather than the requirement of the work situation.

Keywords: apprenticeship genres, client's voice, material processes, relational possesive processes

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Tabrej Khan, Tamer A. Sebaey, Balbir Singh, M. A. Umarfarooq

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Spread-tow thin-ply-based technology has resulted in the progress of optimized reinforced composite plies with ultra-low thicknesses. There is wide use of composite bolted joints in the aircraft industry for load-bearing structures, and they are regarded as the primary source of stress concentration. The purpose of this study is to look into the bearing strength and structural performance of single shear bolt joint configurations in composite laminates, which are basically a combination of conventional thin-plies and thick-plies in some specific stacking sequence. The placement effect of thin-ply within the configured stack on bearing strength, as well as the potential damages, were investigated. Mechanical tests were used to understand the disfigurement mechanisms of the plies and their reciprocity, as well as to reflect on the single shear bolt joint properties and its load-bearing capacity. The results showed that changing the configuration of laminates by inserting the thin plies inside improved the bearing strength by up to 19%.

Keywords: hybrid composites, delamination, stress concentrations, mechanical testing, single bolt joint, thin-plies

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Authors: Li Bixia, Ren Shouwen, Fu Yanfeng, Tu Feng, Xiaoming Fang, Xueming Wang

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The utilization of dietary crude fiber in different breed pigs is not the same. Su-shan pigs are a new breed formed by crossing Taihu pigs and Yorkshire pigs. In order to understand the resistance of Su-shan pigs to dietary crude fiber, 150 Su-shan pigs with 60 kg of average body weight and similar body conditions were allocated to three groups randomly, and there are 50 pigs in each group. The percentages of dietary crude fiber were 8.35%, 9.10%, and 11.39%, respectively. At the end of the experiment, 15 pigs randomly selected from each group were slaughtered. The results showed as follows: average daily gain of the 9.10% group was higher than that of the 8.35% group and the 11.39% group; there was a significant difference between the 9.10% group and the 8.35% group (p < 0.05. Levels of urea nitrogen, total cholesterol and high density lipoprotein in the 9.10% group were significantly higher than those in the 8.35% group and the 11.39% group (p < 0.05). Ratios of meat to fat in the 9.10% group and the 11.39% group were significantly higher than that in the 8.35% group (p < 0.05). Lean percentage of 9.10% group was higher than that of 8.35% group and 11.39% group, but there was no significant difference in three groups (p > 0.05). The weight of small intestine and large intestine in the 11.39% group was higher than that in the 8.35% group, and the 9.10% group and the difference reached a significant level (p < 0.05). In conclusion, increasing dietary crude fiber properly could reduce fat percentage, and improve the ratio of meat to fat of finishing Su-shan pigs. The digestion and metabolism of dietary crude fiber promoted the development of stomach and intestine of finishing Su-shan pig.

Keywords: Su-shan pigs, dietary crude fiber, growth performance, serum biochemical indexes

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Authors: Nasrud Din, Fawad Saeed, Sajid Hussain, Rai Muhammad Dawood Sultan, Premkumar Sellan, Qasim Khan, Wei Lei

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The continuously increasing external quantum efficiencies of Perovskite light-emitting diodes (LEDs) have received significant interest in the scientific community. The need for monitoring and medical diagnostics has experienced a steady growth in recent years, primarily caused by older people and an increasing number of heart attacks, tumors, and cancer disorders among patients. The application of Perovskite near-infrared light-emitting diode (PeNIRLEDs) has exhibited considerable efficacy in bioimaging, particularly in the visualization and examination of blood arteries, blood clots, and tumors. PeNIRLEDs exhibit exciting potential in the field of blood vessel imaging because of their advantageous attributes, including improved depth penetration and less scattering in comparison to visible light. In this study, we synthesized FAPbI₃ Perovskite doped with different concentrations of 5-Aminovaleric acid (5-AVA) 1-6 mg. The incorporation of 5-AVA as a dopant during the FAPbI₃ Perovskite formation influences the FAPbI3 Perovskite’s structural and optical properties, improving its stability, photoluminescence efficiency, and charge transport characteristics. We found a resulting PL emission peak wavelength of 850 nm and bandwidth of 44 nm, along with a calculated quantum yield of 75%. The incorporation of 5-AVA-modified FAPbI₃ Perovskite into LEDs will show promising results, enhancing device efficiency, color purity, and stability. Making it suitable for various medical applications, including subcutaneous deep vein imaging, blood flow visualization, and tumor illumination.

Keywords: perovskite light-emitting diodes, deep vein imaging, blood flow visualization, tumor illumination

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Authors: Charles Simplice Mbatsogo Mebo

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With growth picking up again, Africa seems increasingly attractive for its own nationals who return home through new opportunities available for them. The purpose of our research paper is to understand the role of the international partners in Cameroon, with regards to their support for the return and reintegration of migrants. We, therefore, questioned the relevance and effectiveness and efficacy of international instruments in reintegrating returnees to Cameroon. After our analysis that was conducted on the basis of a documentary exploration, interviews, and field surveys, it appears that the contribution of the international partners in Cameroon is proven in relation to their participation in the financing and placement of returned experts. However, their contribution remains insufficient due to their low level of deployment and the insignificant impact of their investments on the reintegration of Cameroonian Diasporas. The research also reveals some exogenous and endogenous constraints that hinder international institutions' actions in terms of accompanying migrants returning to Cameroon. Finally, for a better management of the returnees' issue, it is necessary to set up a mechanism to raise awareness and a coordination system of all international actors involved. It is also relevant to reform the migration policy, build institutional capacities, and improve the juridical-administrative and economic environment so as to favor co-development in Cameroon.

Keywords: international partners, returnees, diaspora, migration policy, co-development

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Authors: Mahmoud S. Zaghloul, Mohammed M. Khalaf, Wael N. Thabet, Haidy N. Asham

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Background. Hypertrophic scarring is a difficult problem for burn patients, and scar management is an essential aspect of outpatient burn therapy. Post-burn pathologic scars involve functional and aesthetic limitations that have a dramatic influence on the patient’s quality of life. The aim was to investigate the use of extracorporeal shock wave therapy (ESWT), which targets the fibroblasts in scar tissue, as an effective modality for scar treatment in burn patients. Subjects and methods: forty patients with post-burn scars were assigned randomly into two equal groups; their ages ranged from 20-45 years. The study group received ESWT and traditional physical therapy program (deep friction massage, stretching exercises). The control group received traditional physical therapy program (deep friction massage, stretching exercises). All groups received two sessions per week for six successful weeks. The data were collected before and after the same period of treatment for both groups. Evaluation procedures were carried out to measure scar thickness using ultrasonography and Vancouver Scar Scale (VSS) was completed before and after treatment. Results: Post-treatment results showed that there was a significant improvement difference in scar thickness in both groups in favor of the study group. Percentage of improvement in scar thickness in the study group was 42.55%, while it was 12.15% in the control group. There was also a significant improvement difference between results obtained using VSS in both groups in favor of the study group. Conclusion: ESWT is effective in management of pathologic post burn scars.

Keywords: extracorporeal shock wave therapy, post-burn scars, ultrasonography, Vancouver scar scale

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Authors: Shams Ul Hussain Zaheer, Bakht Rahman, Shehla Shams, Bibi Alia

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Arabic literature, from the very past, is divided into two basic parts: prose and poetry. It will not be wrong if it is said that this division of literature is found even in the era of ignorance (before-Islam). In this period, prose was given a kind of ignorance while poetry was given much significance since people showed deeper interest in its melodious impact while listening and singing as compared to prose writing. Because poetry was directly appealing to the emotions of the people, it was celebrated as universal genre and prose remained in a subordinate position due to its diction. Despite this attitude towards the genre of prose, some of the prosaic arts were orally transmitted from one generation to another during the era of ignorance. Later on, in the Omayyad and Abbasside periods, when literature was properly classified, this art was given its proper placement in the history. In this connection, there are three important aspects of this genre i.e. will, tales, and sacerdotal words. This paper traces the historical background of these categories and how they contributed to the modern understanding of literature in terms of its diction, themes, and kinds of prose writing. This is a descriptive and qualitative research which will add insight into the role these terms can play in understanding the thinking and inclination of people in the days of ignorance.

Keywords: Arabic literature, era of ignorance, prose, special arts, analysis

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Weera Chotithammaporn, Bannasorn Santhan

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The purpose of this study was to investigate the students’ satisfaction towards the counseling services of the Faculty of Industrial Technology, Suan Sunandha Rajabhat University. The sample group consisted of 311 students coming for counseling services during September to October 2012 BE to complete the questionnaires developed by the researcher. The data were analyzed to find percentage, arithmetic mean, and SD, from which it can be concluded that: 1) Personal information including gender, GPA, department, year of the study, and hometown revealed that most of the students in the Faculty of Industrial Technology, Suan Sunandha Rajabhat University were female with the GPA between 2.01 and 2.50 and studied in the Department of Interior and Exhibition Design and Graphic and Multimedia Design. Most of them were in the first year of the study and came from the southern part of Thailand. 2) The level of students’ satisfaction towards the counseling services of the Faculty of Industrial Technology, Suan Sunandha Rajabhat University was in overall at high level with the highest aspect on IT services followed by follow-up and evaluation service, counseling service, individual personal data collecting service, and personal placement service respectively.

Keywords: satisfaction, students, counseling service, Faculty of Industrial Technology

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Nail Nazarov, Vladimir Zobov, Alexandra Vyshtakalyuk, Vyacheslav Semenov, Irina Galyametdinova, Vladimir Reznik

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There have been studied effects of xymedon and six new pyrimidine derivatives, that are close and distant analogs of xymedon, on rats' working capacity in the test 'swimming to failure'. It has been shown that a single administration of the studied compounds did not have a statistically significant effect in the test. In the conditions of multiple intraperitoneal administration of the studied pyrimidine derivatives, the compound L-ascorbate, 1-(2-hydroxyethyl)-4.6-dimethyl-1.2-dihydropyrimidine-2-one had the lowest toxicity and the most pronounced actoprotective effect. Introduction in the dose of 20 mg/kg caused a statistically significant increase 440 % in the duration of swimming of rats on the 14th day of the experiment compared with the control group. Multiple administration of the compound in the conditions of physical load did not affect leucopoiesis but stimulates erythropoiesis resulting in an increase in the number of erythrocytes and a hemoglobin level. The substance introduction under mixed exhausting loads prevented such changes of blood biochemical parameters as reduction of glucose, increased of urea and lactic acid levels, what indicates improvement in the animals' tolerability of loads and an anti-catabolic effect of the compound. Absence of hepato and cardiotoxic effects of the substance has been shown. This work was performed with the financial support of Russian Science Foundation (grant № 14-50-00014).

Keywords: actoprotectors, physical working capacity, pyrimidine derivatives, xymedon

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Authors: Hussein Bizimana, Osman Sönmez

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Constructed wetlands are current the most widely recognized waste water treatment option, especially in developing countries where they have the potential for improving water quality and creating valuable wildlife habitat in ecosystem with treatment requirement relatively simple for operation and maintenance cost. Lack of grey waste water treatment facilities in Kigali İnstitute of Science and Technology in Rwanda, causes pollution in the surrounding localities of Rugunga sector, where already a problem of poor sanitation is found. In order to treat grey water produced at Kigali İnstitute of Science and Technology, with high BOD concentration, high nutrients concentration and high alkalinity; a Horizontal Sub-surface Flow pilot-scale constructed wetland was designed and can operate in Kigali İnstitute of Science and Technology. The study was carried out in a sedimentation tank of 5.5 m x 1.42 m x 1.2 m deep and a Horizontal Sub-surface constructed wetland of 4.5 m x 2.5 m x 1.42 m deep. The grey waste water flow rate of 2.5 m3/d flew through vegetated wetland and sandy pilot plant. The filter media consisted of 0.6 to 2 mm of coarse sand, 0.00003472 m/s of hydraulic conductivity and cattails (Typha latifolia spp) were used as plants species. The effluent flow rate of the plant is designed to be 1.5 m3/ day and the retention time will be 24 hrs. 72% to 79% of BOD, COD, and TSS removals are estimated to be achieved, while the nutrients (Nitrogen and Phosphate) removal is estimated to be in the range of 34% to 53%. Every effluent characteristic will meet exactly the Rwanda Utility Regulatory Agency guidelines primarily because the retention time allowed is enough to make the reduction of contaminants within effluent raw waste water. Treated water reuse system was developed where water will be used in the campus irrigation system again.

Keywords: constructed wetlands, hydraulic conductivity, grey waste water, cattails

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Authors: Nadia A. Mohamed, Zakaria El-Khayat, Wagdy K. B. Khalil, Mehrez E. El-Naggar

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Drug nephrotoxicity is still a problem for patients who have taken drugs for elongated periods or permanently. Ultrasound-assisted sol−gel method was used to prepare hollow structured poroussilica nanoemulsion loaded with sodium salicylate as a model drug. The work was extended to achieve the target of the current work via investigating the protective role of this nanoemulsion model as anti-inflammatory drug or ginger for its antioxidant effect against cisplatin-induced nephrotoxicity in male albino rats. The results clarify that the nanoemulsion model was synthesized using ultrasonic assisted with small size and well stabilization as proved by TEM and DLS analysis. Additionally, blood urea nitrogen (BUN), Serum creatinine (SC) and Urinary total protein (UTP) were increased, and the level of creatinine clearance (Crcl) was decreased. All those were met with disorders in oxidative stress and downregulation in the expression of the nephrin gene. Also, histopathological changes of the kidney tissue were observed. These changes back to normal by treatment with silica nanoparticles loaded sodium salicylate (Si-Sc-NPs), ginger or both. Conclusions oil/water nanoemulsion of (Si-Sc NPs) and ginger showed a protective and promising preventive strategy against nephrotoxicity due to their antioxidant and anti-inflammatory effects, and that offers a new approach in attenuating drug induced nephrotoxicity.

Keywords: sodium salicylate nanoencapsulation, nephrin mRNA, drug nephrotoxicity, cisplatin, experimental rats

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Authors: Nadia A. Mohamed, Mehrevan M. Abdel-Moniem

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Zingiber officinale (ginger) has been cultivated for medicinal purposes due to their various proprieties both in vitro and in vivo, so we designed to evaluate the ginger’s potential effect on nephrin m RNA expression in cisplatin-induced nephrotoxic rats. Method: Forty male albino rats were divided into group I was injected (IP) with one ml saline, group II(cisplatin) injected (IP) with a single dose of 12 mg/kg cisplatin, group III (ginger) received (PO) 310 mg/kg for 30 successive days, and group IV(cisplatin and ginger) rats received ginger extract (310 mg/kg) daily for 20 successive days (PO), and then on day 20 of ginger extract administration each rat was injected(IP) with a single dose of 12 mg/kg cisplatin. The blood was sampled to assess urea, creatinine (SC), while the levels of malondialdehyde (MDA), nitric oxide (NO) and paraoxonase (PON1) were measured in kidney tissue homogenate. Expression of urinary nephrin gene (nephrin mRNA) was detected using qRT-PCR. Results: Treatment with ginger significantly decreased the levels of kidney function parameters as well as MDA and NO elevated by cisplatin injection, while PON1 was significantly reduced in the cisplatin group. However, the protection of male rats with ginger significantly increased the levels of nephrin gene expression and PON1 compared with the cisplatin-treated group. Our results generated a proposal on the ameliorating effect of ginger on nephrin mRNA gene expression reduction in cisplatin-induced nephrotoxicity.

Keywords: nephrin mRNA, ginger, cisplatin, nephrotoxicity

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Authors: Diane Ezeh-Aruah

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This study examined newspaper coverage of President Muhammadu Buhar’s anti-corruption crusade, a case study of Guardian, Nation, Sun and Vanguard newspapers. It assessed the frequency of coverage given to President Buhari’s war against corruption, the prominence of coverage, the angles/framing of topics and the direction of the news stories. The determinants of the prominence of coverage were page placement, length of the story, illustrations and story types. The author made use of agenda setting and framing theories. The research was carried through the method of survey, by distribution of copies of the questionnaire. The result of this study showed that the media gave adequate coverage of President Buhari’s anti-corruption war, even though the reports were not many in the early stages of the law enactment, but the coverages lacked prominence as most of the major stories were not given front page coverage; they lacked pictorial illustrations and not exhaustive enough to be impactful. Newspaper organizations are therefore encouraged to include humanistic angles in their corruption stories rather than focus highly on political angles. They should adopt the elements of investigative and interpretative journalism in their coverage of corruption news.

Keywords: newspaper, coverage, president Muhammadu Buhari, anti-corruption campaign

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Aulia F. Hadining, Atya Nur Aisha, Dimas Kurninatoro Aji

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Utilization of social media as a marketing tool is growing rapidly, including for SMEs. Social media allows the user to give product evaluation and recommendations to the public. In addition, the social media facilitate word-of-mouth marketing communication. One of the social media that can be used is Facebook, with Facebook Ads. This study aimed to evaluate the location of Facebook Ads, to obtain an appropriate advertising design. There are three alternatives location consist of desktop, right-hand column and mobile. The effectiveness and efficiency of advertising will be measured based on advertising metrics such as reach, click, Cost per Click (CUC) and Unique Click-Through-Rate (UCTR). Facebook's Ads Manager was used for seven days, targeted by age (18-24), location (Bandung), language (Indonesia) and keywords. The result was 13,999 total reach, as well as 342 clicks. Based on the results of comparison using ANOVA, there was a significant difference for each placement location based on advertising metrics. Mobile location was chosen to be successful ads, because it produces the lowest CUC, amounting to Rp 691,- per click and 14% UCTR. Results of this study showed Facebook Ads was useful and cost-effective media to promote the product of SME, because it could be view by many people in the same time.

Keywords: marketing communication, social media, Facebook Ads, mobile location

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Abdullah Abu-Tineh, Hissa Sadiq, Fatma Al-Mutawah, Youmen Chaaban

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The past 20 years have seen a proliferation of empirical research into various licensure systems. Extensive quantitative work investigates these systems of appraisal from different countries, but there is far less research on the implementation of the Qatari licensure system and the adoption of professional standards. In this paper, we provided a quantitatively and qualitatively descriptive look at the process that moves educators from their point of entry into the profession through their certification as accomplished professionals. Specifically, we focused on the perceptions of teachers and school leaders on the licensure system currently adopted by Ministry of Education and Higher Education in Qatar. The paper aims to inform progress towards a system of reliable, valid, and nationally appropriate teacher and school leader evaluation procedures. Such a system can support decision-making based on a common, comprehensive set of standards that ensures the placement of only the most effective educators in Qatari schools. This paper was made possible by NPRP grant # (NPRP7-1224-5-178) from the Qatar national research fund (a member of Qatar foundation) to Abdullah M. Abu-Tineh. The statements made herein are solely the responsibility of the author.

Keywords: licensure system, professional standards, professional portfolio, educator voice

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Clotilde Guyon, Nada Jmari, Yen-Chin Li, Jean Denoyel, Noriyuki Fujikado, Christophe Blanchet, David Root, Matthieu Giraud

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Aire induces ectopic expression of a large repertoire of tissue-specific antigen (TSA) genes in thymic medullary epithelial cells (MECs), driving immunological self-tolerance in maturing T cells. Although important mechanisms of Aire-induced transcription have recently been disclosed through the identification and the study of Aire’s partners, the fine transcriptional functions underlied by a number of them and conferred to Aire are still unknown. Alternative cleavage and polyadenylation (APA) is an essential mRNA processing step regulated by the termination complex consisting of 85 proteins, 10 of them have been related to Aire. We evaluated APA in MECs in vivo by microarray analysis with mRNA-spanning probes and RNA deep sequencing. We uncovered the preference of Aire-dependent transcripts for short-3’UTR isoforms and for proximal poly(A) site selection marked by the increased binding of the cleavage factor Cstf-64. RNA interference of the 10 Aire-related proteins revealed that Clp1, a member of the core termination complex, exerts a profound effect on short 3’UTR isoform preference. Clp1 is also significantly upregulated in the MECs compared to 25 mouse tissues in which we found that TSA expression is associated with longer 3’UTR isoforms. Aire-dependent transcripts escape a global 3’UTR lengthening associated with MEC differentiation, thereby potentiating the repressive effect of microRNAs that are globally upregulated in mature MECs. Consistent with these findings, RNA deep sequencing of actinomycinD-treated MECs revealed the increased stability of short 3’UTR Aire-induced transcripts, resulting in TSA transcripts accumulation and contributing for their enrichment in the MECs.

Keywords: Aire, central tolerance, miRNAs, transcription termination

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Authors: Mehmet Ali Kisaçam, P. Sema Temizer Ozan, Ayşe Doğan, Gonca Ozan, F. Sarper Türker

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Cardiovascular disease which is one of the most common health problems worldwide has crucial importance because of its’ morbidity and mortality rates. Nitric oxide synthase and arginase use L-arginine as a substrate and produce nitric oxide (NO), citrulline and urea, ornithine respectively. Endothelial dysfunction is characterized by reduced bioavailability of vasodilator and anti-inflammatory molecule NO. The purpose of the study to assess endothelial function via arginase activity and NO levels in patients undergoing coronary artery bypass grafting (CABG) surgery. The study was conducted on 26 patients (14 male, 12 female) undergoing CABG surgery. Blood samples were collected from the subjects before surgery, after the termination and after 24 hours of the surgery. Arginase activity and NO levels measured in collected samples spectrophotometrically. Arginase activity decreased significantly in subjects after the termination of the surgery compared to before surgery data. 24 hours after the surgery there wasn’t any significance in arginase activity as it compared to before surgery and after the termination of the surgery. On the other hand, NO levels increased significantly in the subject after the termination of the surgery. However there was no significant increase in NO levels after 24 hours of the surgery, but there was an insignificant increase compared to before surgery data. The results indicate that after the termination of the surgery vascular and endothelial function improved and after 24 hours of the surgery arginase activity and NO levels returned to normal.

Keywords: arginase, bypass, cordiopulmonary, nitric oxide

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Authors: B. Medani Amna, M. A. Elbadwi Samia, E. Amin Ahmed

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Variable plants present in nature are used by simple rural and urban people, researchers and drug manufacturers for medicinal purposes. Garad is one of the most commonly used in Sudan for both treatment and prophylaxis of infections in the respiratory, urinogenital tracts and the skin. Water exctracts from Acacia nilotica bods were used in this very experiment to test for their toxicity to Nubian goats at two dose rates under proper experimental conditions. The clinical, pathological, haematological and biological changes in Nubian goats given daily oral doses of 1 and 5 g/kg body weight of Acacia nilotica to two groups of test goats. The goats of the control group were undosed with Acacia nilotica.Other than the dose co-related mortality rates, the clinical signs were observed to be salivation, staggered gait, intermittent loss of voice and low appetite. On histopathological testing, the main lesions were hepatic centrolobular necrosis and fatty changes associated with the significant changes in GGT and ALP are indicating hepatic dysfunction.Renal malfunction is indicated by haemorrhages in addition to the change in the urea concentration. The congested, haemorrhagic, emphysematous, edematous and cyanotic lungs may contribute to the development of dyspnea. Acacia nilotica poisoning may lead to an immunosuppression pointed out by the lymphocyte infiltration. On evaluation of the above results, Acacia nilotica was considered toxic to Nubian goats at the above mentioned doses. Future work for Acacia nilotica was forwarded and practical implications of the result were highlighted.

Keywords: Acaia nilotica, toxicity data, Nubian goats, Garad

Procedia PDF Downloads 459