Search results for: metallurgical image processing

Authors: Ž. Vaštag, Lj. Popović, S. Popović

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After cold pressing of pumpkin oil, the defatted oil cake (PUOC) was utilized as raw material for processing of bio-functional hydrolysates. In this study, the in vitro bioactivity of an alcalase (AH) and a pepsin hydrolysate (PH) prepared from the major pumpkin 12S globulin (cucurbitin) are compared. The hydrolysates were produced at optimum reaction conditions (temperature, pH) for the enzymes, during 60min. The bioactivity testing included antioxidant and angiotensin I converting enzyme inhibitory activity assays. The hydrolysates showed high potential as natural antioxidants and possibly antihypertensive agents in functional food or nutraceuticals. Additionally, preliminary studies have shown that both hydrolysates could exhibit modest α-amylase inhibitory activity, which indicates on their hypoglycemic potential.

Keywords: cucurbitin, alcalase, pepsin, protein hydrolysates, in vitro bioactivity

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Authors: M. Kalyan Raj, Vinay Umesh Rao, M. Sudhakar

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The work focuses on the development of a directly compressible controlled release co-processed excipient using melt granulation technique. Erodible wax matrix systems are fabricated in which three different types of waxes are co processed separately with Maize starch in different ratios by melt granulation. The resultant free flowing powder is characterized by FTIR, NMR, Mass spectrophotometer and gel permeation chromatography. Also, controlled release tablets of Aripiprazole were formulated and dissolution profile was compared with that of the target product profile given in Zysis patent (Patent no. 20100004262) for Aripiprazole once a week formulation.

Keywords: co-processing, hot melt extrusion, direct compression, maize starch, stearic acid, aripiprazole

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Authors: H. Nagendra, Vinod Kumar, S. Mukherjee

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In this study, the potential benefits of playing action video game among congenitally deaf and dumb subjects is reported in terms of EEG ratio indices. The frontal and occipital lobes are associated with development of motor skills, cognition, and visual information processing and color recognition. The sixteen hours of First-Person shooter action video game play resulted in the increase of the ratios β/(α+θ) and β/θ in frontal and occipital lobes. This can be attributed to the enhancement of certain aspect of cognition among deaf and dumb subjects.

Keywords: cognitive enhancement, video games, EEG band powers, deaf and dumb subjects

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Authors: Abdulali Bashir Ben Saleh

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The powdered beet red roots (PBRR) were used as an adsorbent to remove dyes namely methylene blue dye (as a typical cationic or basic dye) from aqueous solutions. The present study shows that used beet red roots powder exhibit adsorption trend for the dye. The adsorption processes were carried out at various conditions of concentrations, processing time and a wide range of pH between 2.5-11. Adsorption isotherm equations such as Freundlich, and Langmuir were applied to calculate the values of respective constants. Adsorption study was found that the currently introduced adsorbent can be used to remove cationic dyes such as methylene blue from aqueous solutions.

Keywords: beet red root, removal of deys, methylene blue, adsorption

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Authors: Sefa Aksu, Ünal Kızıl

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For this study, a town based soil database created in Gümüşçay District of Biga Town, Çanakkale, Turkey. Crop and livestock production are major activities in the district. Nutrient management is mainly based on commercial fertilizer application ignoring the livestock manure. Within the boundaries of district, 122 soil sampling points determined over the satellite image. Soil samples collected from the determined points with the help of handheld Global Positioning System. Labeled samples were sent to a commercial laboratory to determine 11 soil parameters including salinity, pH, lime, organic matter, nitrogen, phosphorus, potassium, iron, manganese, copper and zinc. Based on the test results soil maps for mentioned parameters were developed using remote sensing, GIS, and geostatistical analysis. In this study we developed a GIS database that will be used for soil nutrient management. Methods were explained and soil maps and their interpretations were summarized in the study.

Keywords: geostatistics, GIS, nutrient management, soil mapping

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Authors: Maxat Bokambayev, Bella Tussupova, Aisha Mamyrova, Erlan Izbasarov

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Optimization of business processes in trading companies is reviewed in the report. There is the presentation of the “Wholesale Customer Order Handling Process” business process model applicable for small and medium businesses. It is proposed to apply the algorithm for automation of the customer order processing which will significantly reduce labor costs and time expenditures and increase the profitability of companies. An optimized business process is an element of the information system of accounting of spare parts trading network activity. The considered algorithm may find application in the trading industry as well.

Keywords: business processes, discrete-event simulation, management, trading industry

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Authors: A. I. O. Zaid, A. M. Attieh, S. M. A. Al Qawabah

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Aluminum and its alloys solidify in columnar structure with large grain size which tends to reduce their mechanical strength and surface quality. They are, therefore, grain refined by addition of either titanium or titanium plus boron to their melt before solidification. Equal channel angular pressing, ECAP, process is a recent forming method for producing heavy plastic deformation in materials. In this paper, the effect of molybdenum addition to aluminum grain refined by Ti+B on its metallurgical and mechanical characteristics are investigated in the as cast condition and after pressing by the ECAP process. It was found that addition of Mo or Ti+B alone or together to aluminum resulted in grain refining of its microstructure in the as cast condition, as the average grain size was reduced from 139 micron to 46 micron when Mo and Ti+B are added together. Pressing by the ECAP process resulted in further refinement of the microstructure where 32 micron of average grain size was achieved in Al and the Al-Mo microalloy. Regarding the mechanical strength, addition of Mo or Ti+B alone to Al resulted in deterioration of its mechanical behavior but resulted in enhancement of its mechanical behavior when added together, increase of 10% in flow stress was achieved at 20% strain. However, pressing by ECAP addition of Mo or Ti+B alone to Al resulted in enhancement of its mechanical strength but reduced its strength when added together.

Keywords: ECAP, aluminum, cast, mechanical characteristics, Mo grain refiner

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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This paper discusses on power spectral density (PSD) characteristics which are extracted from three-dimensional (3D) electroencephalogram (EEG) models. The EEG signal recording was conducted on 150 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, the values of maximum PSD were extracted as features from the model. These features are analysed using mean relative power (MRP) and different mean relative power (DMRP) technique to observe the pattern among different brain balancing indexes. The results showed that by implementing these techniques, the pattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 1 to index 5 for left frontal (LF) and right frontal (RF).

Keywords: power spectral density, 3D EEG model, brain balancing, mean relative power, different mean relative power

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Authors: Justyna Żywiołek, Marek Matulewski

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Data, is information processed by enterprises for primary and secondary purposes as processes. Thanks to processing, the sales process takes place; in the case of the surveyed companies, sales take place online. However, this indirect form of contact with the customer causes many problems for both customers and furniture manufacturers. The article presents solutions that would solve problems related to the analysis of data and information in the order fulfillment process sent to post-warranty service. The article also presents an analysis of threats to the security of this information, both for customers and the enterprise.

Keywords: ordering furniture online, information security, furniture industry, enterprise security, risk analysis

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Authors: Aziz Sezgin, Yuksel Hacioglu, Nurkan Yagiz

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Sliding mode controller for a vehicle active suspension system is designed in this study. The widely used quarter car model is preferred and it is aimed to improve the ride comfort of the passengers. The effect of the actuator time delay, which may arise due to the information processing, sensors or actuator dynamics, is also taken into account during the design of the controller. A sliding mode controller was designed that has taken into account the actuator time delay by using Smith predictor. The successful performance of the designed controller is confirmed via numerical results.

Keywords: sliding mode control, active suspension system, actuator, time delay, vehicle

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Authors: Hatim Elhag

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Banking and Financial Service Institutions are possibly the most advanced in terms of technology adoption and use it as a key differentiator. With high levels of business process automation, maturity in the functional portfolio, straight through processing and proven technology outsourcing benefits, Banking sector stand to benefit significantly from Cloud computing capabilities. Additionally, with complex Compliance and Regulatory policies, combined with expansive products and geography coverage, the business impact is even greater. While the benefits are exponential, there are also significant challenges in adopting this model– including Legal, Security, Performance, Reliability, Transformation complexity, Operating control and Governance and most importantly proof for the promised cost benefits. However, new architecture designed should be implemented to align this approach.

Keywords: security, cloud, banking sector, cloud computing

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Authors: Sudhakar Saroj, Satya Vir Singh

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Undoped and Fe doped TiO₂, Ti₁₋ₓFeₓO₂ (x=0.00, 0.01, 0.03, 0.05, 0.07 and 0.09) have been synthesized by solution combustion method using Titanium (IV) oxide as a precursor, and also were characterized by XRD, DRS, FTIR, XPS, SEM, and EDX. The formation of anatase phase of undoped and Fe TiO₂ nanoparticles were confirmed by XRD, and the average crystallite size was determined by Debye-Scherer's equation. The DRS analysis indicates the shifting of light absorbance in visible region from UV region with increasing the doping concentration in TiO₂. The vibrational band of the Ti-O lattice was confirmed by the FT-IR spectrum. The XPS results confirm the presence of elements of titanium, oxygen and iron in the synthesized samples and determine the binding energy of elements. SEM image of the above-synthesized nanoparticles showed the spherical shape of nanoparticles. The purities of the synthesized nanoparticles were confirmed by EDX analysis. The photocatalytic activities of the synthesized nanoparticles were tested by studying the degradation of dye (Direct Blue 199) in the photocatalytic reactor. The Ti₀.₉₇Fe₀.₀₃O₂ photocatalyst shows highest photodegradation activity among all the synthesized undoped and Fe doped TiO₂ photocatalyst.

Keywords: direct blue 199, nanoparticles, TiO₂, photodegradation

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: R. M. R. Hussein

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Sustainable urban waterfront development is one of the most interesting phenomena of urban renewal in the last decades. However, there are still many cities whose visual image is compromised due to the lack of a sustainable urban waterfront development, which consequently affects the place of those cities globally. This paper aims to reimagine the role of waterfront areas in city design, with a particular focus on Egypt, so that they provide attractive, sustainable urban environments while promoting the continued aesthetic development of the city overall. This aim will be achieved by determining the main principles of a sustainable urban waterfront and its applications. This paper concentrates on sustainability assessment rating systems. A number of international case-studies, wherein a city has applied the basic principles for a sustainable urban waterfront and have made use of sustainability assessment rating systems, have been selected as examples which can be applied to the urban waterfronts in Egypt. This paper establishes the importance of developing the design of urban environments in Egypt, as well as identifying the methods of sustainability application for urban waterfronts.

Keywords: sustainable urban waterfront, green infrastructure, energy efficient, Cairo

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Authors: Olomon Wu, Hung Lu, Nick Wilkins, Daniel Kerr, Zekeriya Aliyazicioglu, H. K. Hwang

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Multiple Input Multiple Output (MIMO) radar has received increasing attention in recent years. MIMO radar has many advantages over conventional phased array radar such as target detection, resolution enhancement, and interference suppression. In this paper, the results are presented from a simulation study of MIMO Uniformly-Spaced Linear Array (ULA) antennas. The performance is investigated under varied parameters, including varied array size, Pseudo Random (PN) sequence length, number of snapshots, and Signal to Noise Ratio (SNR). The results of MIMO are compared to a traditional array antenna.

Keywords: MIMO radar, phased array antenna, target detection, radar signal processing

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Zahra Afshordi

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Instagram, as an everyday life social network, hosts from the ultrasound image of an unborn fetus to the pictures of newly placed gravestones and funerals. It is a platform that allows its users to create a second identity independently from and at the same time in relation to the real space identity. The motives behind this identification are what this article is about. This article studies the motivations of Instagram users mourning for celebrities with a focus on the death of MohammadReza Shajarian. The Shajarian’s death had a wide reflection on Instagram Persian-speaking users. The purpose of this qualitative survey is to comprehend and study the user’s motivations in posting mourning and memorializing content. The methodology of the essay is a hybrid methodology consisting of content analysis and open-ended interviews. The results highlight that users' motives are more than just simple sympathy and include political protest, gaining cultural capital, reaching social status, and escaping from solitude.

Keywords: case study, celebrity, identity, Instagram, mourning, qualitative survey

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Authors: Weitao Lin

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To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method.

Keywords: natural language processing, Chinese event detection, rules matching, dependency parsing

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Authors: Yiwei Li, Mingyu Gao

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Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x.

Keywords: hybrid memory, heterogeneous systems, dram cache, graphics processing units

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Authors: C. R. Ríos-Soberanis, V. M. Moo-Huchin, R. J. Estrada-Leon, E. Perez-Pacheco

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Polymers from renewable resources have attracted an increasing amount of attention over the last two decades, predominantly due to two major reasons: firstly environmental concerns, and secondly the realization that our petroleum resources are finite. Finding new uses for agricultural commodities is also an important area of research. Therefore, it is crucial to get new sources of natural materials that can be used in different applications. Ramon tree (Brosimum alicastrum) is a tropical plant that grows freely in Yucatan countryside. This paper focuses on the seeds recollection, processing and starch extraction and characterization in order to find out about its suitability as biomaterial. Results demonstrated that it has a high content of qualities to be used not only as comestible but also as an important component in polymeric blends.

Keywords: biomaterials, characterization techniques, natural resource, starch

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Akkouche Karim, Nekmouche Aghiles, Bouzid Leyla

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This study focuses on the development of a multifunctional Expert System (ES) called post-seismic damage inspection tool (PSDIT), a powerful tool which allows the evaluation, the processing and the archiving of the collected data stock after earthquakes. PSDIT can be operated by two user types; an ordinary user (engineer, expert or architect) for the damage visual inspection and an administrative user for updating the knowledge and / or for adding or removing the ordinary user. The knowledge acquisition is driven by a hierarchical knowledge model, the Information from investigation reports and those acquired through feedback from expert / engineer questionnaires are part.

Keywords: buildings, earthquake, seismic damage, damage assessment, expert system

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Mohammad Shakir, Reshma Jolly, Mohammad Shoeb Khan, Noor-E-Iram

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A nanocomposite system incorporating dextrin into nano-hydroxyapatite/chitin matrix (n-HA/DX/CT) has been successfully synthesized via co-precipitation route at room temperature for the application in bone tissue engineering by investigating biocompatibility, cytotoxicity and mechanical properties. The FTIR spectra of n-HA/DX/CT nanocomposite indicated a considerable intermolecular interaction between the various components of the system. The results of XRD, TEM and TGA/DTA revealed that the crystallinity, size and thermal stability of the n-HA/DX/CT scaffold has decreased and increased respectively. The result of SEM image of the n-HA/DX/CT scaffold indicated that the incorporation of dextrin affected the surface morphology while considerable in-vitro bioactivity has been observed in n-HA/DX/CT based on SBF study, referring a step towards possibility of making direct bond to living bone if implanted. Moreover, MTT assay suggested the non-toxic nature of n-HA/DX/CT to murine fibroblast L929 cells. The swelling study of n-HA/DX/CT scaffold indicated the low swelling rate for n-HADX/CT. All these results have paved the way for n-HA/DX/CT to be used as a competent material for bone tissue engineering.

Keywords: autograft, chitin, dextrin, nanocomposite

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Authors: José Leandro Melgar Néris, Claudinéia Brazil, Luciane Teresa Salvi, Isabel Cristina Damin

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The impact the climate change is not recent, the main variable in the hydrological cycle is the sequence and shortage of a drought, which has a significant impact on the socioeconomic, agricultural and environmental spheres. This study aims to characterize and quantify, based on precipitation climatic projections, the rainy and dry events in the region of the Uruguay River Basin, through the Standardized Precipitation Index (SPI). The database is the image that is part of the Intercomparison of Model Models, Phase 5 (CMIP5), which provides condition prediction models, organized according to the Representative Routes of Concentration (CPR). Compared to the normal set of climates in the Uruguay River Watershed through precipitation projections, seasonal precipitation increases for all proposed scenarios, with a low climate trend. From the data of this research, the idea is that this article can be used to support research and the responsible bodies can use it as a subsidy for mitigation measures in other hydrographic basins.

Keywords: climate change, climatic model, dry events, precipitation projections

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Authors: Bharath R., Tharun Sai N., Bhuvan G.

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This project focuses on developing a Parallel Computing method aimed at optimizing matrix multiplication through GPU acceleration. Addressing algorithmic challenges, GPU programming intricacies, and integration issues, the project aims to enhance efficiency and scalability. The methodology involves algorithm design, GPU programming, and optimization techniques. Future plans include advanced optimizations, extended functionality, and integration with high-level frameworks. User engagement is emphasized through user-friendly interfaces, open- source collaboration, and continuous refinement based on feedback. The project's impact extends to significantly improving matrix multiplication performance in scientific computing and machine learning applications.

Keywords: matrix multiplication, parallel processing, cuda, performance boost, neural networks

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Yu Cheng-Yu

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‘Heterotopia’, ‘Visual Cultural Art Education’ and ‘New Media’ of these three subjects seemingly are irrelevant. In fact, there are synchronicity and intertextuality inside. In addition to visual culture, art education inspires students the ability to reflect on popular culture image through visual culture teaching strategies in school. We should get involved in the community to construct the learning environment that conveys visual culture art. This thesis attempts to probe the heterogeneity of space and value from Michel Foucault and to research sustainable development strategy in ‘New Media Art Village’ heterogeneity from Jean Baudrillard, Marshall McLuhan's media culture theory and social construction ideology. It is possible to find a new media group that can convey ‘Visual Culture Art Education’ around the National Taipei University of Technology in this commercial district that combines intelligent technology, fashion, media, entertainment, art education, and marketing network. Let the imagination and innovation of ‘New Media Art Village’ become ‘implementable’ and new media Heterotopia of inter-subjectivity with the engagement of big data and digital media. Visual culture art education will also bring aesthetics into the community by New Media Art Village.

Keywords: social construction, heterogeneity, new media, big data, visual culture art education

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Authors: Eda Kocakaya, Sebahat Seker, Dogan Argun

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The aim of this study is to determine the process of information technologies and the connections between concepts in accounting management services in Turkey. The objective of this study is to determine the adaptation and evaluation process of information technologies and the connections between concepts and differences in accounting management services in Turkey. The situation and determination of the IT applications of Accounting Management were studied. The applications of • Billing • Order Processing • Accounts Receivable/Payable Management • Contract Management • Bank Account Management Were discussed in this study. The IT applications were demonstrated and realized in actual accounting services. The sectoral representative's companies were selected, and the IT application was searched by bibliometric analysis.

Keywords: management, accounting, information technologies, adaptation

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