Search results for: drug property prediction

Authors: Nino Pailodze, Malkhaz Sulashvili, Vladimer Kekenadze, Tea Khutsishvili, Irma Makharashvili, Aleksandre Kekenadze

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In the nearest future among the important problems which Georgia has solve the most important is economic stability, that bases on fiscal policy and the proper definition of the its directions. The main source of the Budget revenue is the national income. The State uses taxes, loans and emission in order to create national income, were the principal weapon are taxes. As well as fiscal function of the fulfillment of the budget, tax systems successfully implement economic and social development and the regulatory functions of foreign economic relations. A tax is a mandatory, unconditional monetary payment to the budget made by a taxpayer in accordance with this Code, based on the necessary, nonequivalent and gratuitous character of the payment. Taxes shall be national and local. National taxes shall be the taxes provided for under this Code, the payment of which is mandatory across the whole territory of Georgia. Local taxes shall be the taxes provided for under this Code, introduced by normative acts of local self-government representative authorities (within marginal rates), the payment of which is mandatory within the territory of the relevant self-governing unit. National taxes have the leading role in tax systems, but also the local taxes have an importance role in tax systems. Exactly in the means of local taxes, the most part of the budget is formatted. National taxes shall be: income tax, profit tax, value added tax (VAT), excise tax, import duty, property tax shall be a local tax The property tax is one of the significant taxes in Georgia. The paper deals with the taxation mechanism that has been operated in Georgia. The above mention has the great influence in financial accounting. While comparing foreign legislation towards Georgian legislation we discuss the opportunity of using their experience. Also, we suggested recommendations in order to improve the tax system in financial accounting. In addition to accounting, which is regulated according the International Accounting Standards we have tax accounting, which is regulated by the Tax Code, various legal orders / regulations of the Minister of Finance. The rules are controlled by the tax authority, Revenue Service. The tax burden from the tax values are directly related to expenditures of the state from the emergence of the first day. Fiscal policy of the state is as well as expenditure of the state and decisions of taxation. In order to get the best and the most effective mobilization of funds, Government’s primary task is to decide the kind of taxation rules. Tax function is to reveal the substance of the act. Taxes have the following functions: distribution or the fiscal function; Control and regulatory functions. Foreign tax systems evolved in the different economic, political and social conditions influence. The tax systems differ greatly from each other: taxes, their structure, typing means, rates, the different levels of fiscal authority, the tax base, the tax sphere of action, the tax breaks.

Keywords: international accounting standards, financial accounting, tax systems, financial obligations

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Authors: Changyin Zhou

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Ba constructions are a special type of constructions in Chinese. Their syntactic behaviors are closely related to their event structural properties. The existing study which treats the semantic function of Ba as causative meets difficulty in treating the discrepancy between Ba constructions and their corresponding constructions without Ba in expressing causativity. This paper holds that Ba in Ba constructions is a functional category expressing affectedness. The affectedness expressed by Ba can be positive or negative. The functional category Ba expressing negative affectedness has the semantic property of being 'expected'. The precondition of Ba construction is the boundedness of the event concerned. This paper, holding the parallelism between motion events and change-of-state events, proposes a syntactic model based on the notions of boundedness and affectedness, discusses the transformations between Ba constructions and the related resultative constructions, and derivates the various Ba constructions concerned.

Keywords: affectedness, Ba constructions, boundedness, event structure, resultative constructions

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Authors: Ridha Zgolli, Ahmed Belhaj, Maroua Ennouri

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This study presents a new method to predict cavitation area that may be eroded. It is based on the post-treatment of URANS simulations in cavitant flows. The most RANS calculations with incompressible consideration are based on cavitation model using mixture fluid with density (ρm) calculated as a function of liquid density (ρliq), vapour or gas density (ρvap) and vapour or gas volume fraction α (ρm = αρvap + (1-α) ρliq). The calculations are performed on hydrofoil geometries and compared with experimental works concerning flows characteristics (size of pocket, pressure, velocity). We present here the used cavitation model and the approach followed to evaluate the value of α fixing the shape of pocket around wall before collapsing.

Keywords: flows, CFD, cavitation, erosion

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Authors: Robin Anderson, Elizabeth Latham, David Nisbet

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Propagation and dissemination of antimicrobial resistant and pathogenic microbes from spoiled silages and composts represents a serious public health threat to humans and animals. In the present study, the antimicrobial activity of the short chain nitro-compound, 2-nitro-1-propanol (9 mM) as well as the medium chain fatty acid, lauric acid, and its glycerol monoester, monolaurin, (each at 25 and 17 µmol/mL, respectfully) were investigated against select pathogenic and multi-drug resistant antimicrobial resistant Gram-positive bacteria common to spoiled silages and composts. In an initial study, we found that growth rates of a multi-resistant Enterococcus faecalis (expressing resistance against erythromycin, quinupristin/dalfopristin and tetracycline) and Staphylococcus aureus strain 12600 (expressing resistance against erythromycin, linezolid, penicillin, quinupristin/dalfopristin and vancomycin) were more than 78% slower (P < 0.05) by 2-nitro-1-propanol treatment during culture (n = 3/treatment) in anaerobically prepared ½ strength Brain Heart Infusion broth at 37oC when compared to untreated controls (0.332 ± 0.04 and 0.108 ± 0.03 h-1, respectively). The growth rate of 2-nitro-1-propanol-treated Listeria monocytogenes was also decreased by 96% (P < 0.05) when compared to untreated controls cultured similarly (0.171 ± 0.01 h-1). Maximum optical densities measured at 600 nm were lower (P < 0.05) in 2-nitro-1-propanol-treated cultures (0.053 ± 0.01, 0.205 ± 0.02 and 0.041 ± 0.01, respectively) than in untreated controls (0.483 ± 0.02, 0.523 ± 0.01 and 0.427 ± 0.01, respectively) for E. faecalis, S. aureus and L. monocytogenes, respectively. When tested against mixed microbial populations during anaerobic 24 h incubation of spoiled silage, significant effects of treatment with 1 mg 2-nitro-1-propanol (approximately 9.5 µmol/g) or 5 mg lauric acid/g (approximately 25 µmol/g) on populations of wildtype Enterococcus and Listeria were not observed. Mixed populations treated with 5 mg monolaurin/g (approximately 17 µmol/g) had lower (P < 0.05) viable cell counts of wildtype enterococci than untreated controls after 6 h incubation (2.87 ± 1.03 versus 5.20 ± 0.25 log10 colony forming units/g, respectively) but otherwise significant effects of monolaurin were not observed. These results reveal differential susceptibility of multi-drug resistant enterococci and staphylococci as well as L. monocytogenes to the inhibitory activity of 2-nitro-1-propanol and the medium chain fatty acid, lauric acid and its glycerol monoester, monolaurin. Ultimately, these results may lead to improved treatment technologies to preserve the microbiological safety of silages and composts.

Keywords: 2-nitro-1-propanol, lauric acid, monolaurin, gram positive bacteria

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Fabrizio Logiurato

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Light Polarization has many technological applications, and its discovery was crucial to reveal the transverse nature of the electromagnetic waves. However, despite its fundamental and practical importance, in high school, this property of light is often neglected. This is a pity not only for its conceptual relevance, but also because polarization gives the possibility to perform many brilliant experiments with low cost materials. Moreover, the treatment of this matter lends very well to an interdisciplinary approach between art, biology and technology, which usually makes things more interesting to students. For these reasons, we have developed, and in this work, we introduce a laboratory on light polarization for high school and undergraduate students. They can see beautiful pictures when birefringent materials are set between two crossed polarizing filters. Pupils are very fascinated and drawn into by what they observe. The colourful images remind them of those ones of abstract painting or alien landscapes. With this multidisciplinary teaching method, students are more engaged and participative, and also, the learning process of the respective physics concepts is more effective.

Keywords: light polarization, optical activity, multidisciplinary education, science and art

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Authors: Haile Nega Mulata, Seifu Daniel, Umeta Melaku, Wendwesson Ergete, Natesan Gnanasekaran

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Background: In Ethiopia, medicinal plants have been used for various human and animal diseases. In this study, we have examined the potential effect of hydroethanolic extract of Calpurnia aurea seed against hepatotoxicity and haematotoxicity induced by Highly Active Antiretroviral Therapy (HAART) drugs in Albino Wistar rats. Methods: We collected Matured dried seeds of Calpurnia aurea from northern Ethiopia (south Tigray and south Gondar) in June 2013. The powder of the dried seed sample was macerated with 70% ethanol and dried using rotavapor. We have investigated the Preliminary phytochemical tests and in-vitro antioxidant properties. Then, we induced toxicity with HAART drugs and gave the experimental animals different doses of the crude extract orally for thirty-five days. On the 35th day, the animals were fasted overnight and sacrificed by cervical dislocation. We collected the blood samples by cardiac puncture. We excised the liver and brain tissues for further histopathological studies. Subsequently, we analysed serum levels of the liver enzymes- Alanine Aminotransferase, Aspartate Aminotransferase, Alkaline Phosphatase, Total Bilirubin, and Serum Albumin, using commercial kits in Cobas Integra 400 Plus Roche Analyzer Germany. We have also assessed the haematological profile using an automated haematology Analyser (Sysmex KX-2IN). Results: A significant (P<0.05) decrease in serum enzymes (ALT and AST) and total bilirubin were observed in groups that received the highest dose (300mg/kg) of the seed extract. And significant (P<0.05) elevation of total red blood cell count, haemoglobin, and hematocrit percentage was observed in the groups that received the seed extract compared to the HAART-treated groups. The WBC count mean values showed a statistically significant increase (p<0.05) in groups that received HAART and 200 and 300mg/kg extract, respectively. The histopathological observations also showed that the oral administration of varying doses of the crude extract of the seed reversed to a normal state. Conclusion: The hydroethanolic extract of the Calpurnia aurea seed lowered the hepatotoxicity and haematotoxicity in a dose-dependent manner. The antioxidant properties of the Calpurnia aurea seed extract may have possible protective effects against the drug's toxicity.

Keywords: calpurnia aurea, hepatotoxicity, haematotoxicity, antioxidant, histopathology, HAART

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Authors: Sacha Joseph-Mathews, Leili Javadpour

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In the tourism industry, hoteliers spend millions annually on marketing and positioning efforts for their respective hotels, all in an effort to create a specific image in the minds of the consumer. Yet despite extensive efforts to seduce potential hotel guests with sophisticated advertising messages generated by hotel entities, consumers continue to mistrust corporate branding, preferring instead to place their trust in the reviews of their consumer peers. In today’s complex and cluttered marketplace, online reviews can serve as a mediator for consumers who do not have actual knowledge and experiences with the brand, but are in the process of deciding whether or not to engage in a consumption exercise. Traditionally, consumers have used online reviews as a source of comfort and confirmation of a product/service’s positioning. But today, very few customers make any purchase decisions without first researching existing user reviews, making reviews more of a necessity, rather than a luxury in the purchase decision process. The influence of user generated content (UGC) is amplified in the tourism industry; as more than a third of potential hotel guests will not book a room without first reading a review. As corporate branding becomes less relevant and online reviews become more important, how much of the consumer’s stay expectations are being dictated by existing UGC? Moreover, as hotel guest experience a hotel through the lens of an existing review, how much of their stay and in turn their review, would have been influenced by those reviews that they read? Ultimately, there is the potential for UGC to dictate what potential guests will be most critical about, and or most focused on during their stay. If UGC is a stronger influencer in the purchase decision process than corporate branding, doesn’t it have the potential to dictate, the entire stay experience by influencing the expectations of the guest prior to them arriving on the property? For example, if a hotel is an eco-destination and they focus their branding on their website around sustainability and the retreat nature of the hotel. Yet, guest reviews constantly discuss how dissatisfactory the service and food was with no mention of nature or sustainability, will future reviews then focus primarily on the food? Using text analysis software to examine over 25,000 online reviews, we explore the extent to which new reviews are influenced by wording used in previous reviews for a hotel property, versus content generated by corporate positioning. Additionally, we investigate how distinct hotel related UGC is across different types of tourism destinations. Our findings suggest that UGC can have a greater impact on future reviews, than corporate branding and there is more cohesiveness across UGC of different types of hotel properties than anticipated. A model of User Generated Content Influence is presented and the managerial impact of the power of online reviews to trump corporate branding and shape future user experiences is discussed.

Keywords: user generated content, UGC, corporate branding, online reviews, hotels and tourism

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: Justie Su-Tzu Juan, Ming-Jheng Li, Ching-Fen Lee, Ruei-Yu Wu

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A secret image sharing scheme is a way to protect images. The main idea is dispersing the secret image into numerous shadow images. A secret image sharing scheme can withstand the impersonal attack and achieve the highly practical property of multiuse  is more practical. Therefore, this paper proposes a verifiable and detectable secret image-sharing scheme called VDGMSISS to solve the impersonal attack and to achieve some properties such as encrypting multi-secret images at one time and multi-use. Moreover, our scheme can also be used for any genera access structure.

Keywords: multi-secret image sharing scheme, verifiable, de-tectable, general access structure

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Authors: Somayeh Nezami

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The management of financial and human resources of police in metropolitans requires many information and exact plans to reduce a rate of crime and increase the safety of the society. Geographical Information Systems have an important role in providing crime maps and their analysis. By using them and identification of crime hot spots along with spatial presentation of the results, it is possible to allocate optimum resources while presenting effective methods for decision making and preventive solutions. In this paper, we try to explain and compare between some of the methods of hot spots analysis such as Mode, Fuzzy Mode and Nearest Neighbour Hierarchical spatial clustering (NNH). Then the spots with the highest crime rates of drug smuggling for one province in Iran with borderline with Afghanistan are obtained. We will show that among these three methods NNH leads to the best result.

Keywords: GIS, Hot spots, nearest neighbor hierarchical spatial clustering, NNH, spatial analysis of crime

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Yoshio Kurosawa, Takao Yamaguchi

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High frequency automotive interior noise above 500 Hz considerably affects automotive passenger comfort. To reduce this noise, sound insulation material is often laminated on body panels or interior trim panels. For a more effective noise reduction, the sound reduction properties of this laminated structure need to be estimated. We have developed a new calculate tool that can roughly calculate the sound absorption and insulation properties of laminate structure and handy for designers. In this report, the outline of this tool and an analysis example applied to floor mat are introduced.

Keywords: automobile, acoustics, porous material, transfer matrix method

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Authors: John Vergara, Giuseppe Palmese

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Fundamental studies aimed at elucidating the key contributions to corrosion performance are needed to make progress toward effective and environmentally compliant corrosion control. Epoxy/amine systems are typically employed as barrier coatings for corrosion control. However, the hardening agents used for coating applications can be very complex, making fundamental studies of water and oxygen permeability challenging to carry out. Creating model building blocks for epoxy/amine coatings is the first step in carrying out these studies. We will demonstrate the synthesis and characterization of model amine building blocks from saturated fatty acids and simple amines such as diethylenetriamine (DETA) and Bis(3-aminopropyl)amine. The structure-property relationship of thermosets made from these model amines and Diglycidyl ether of bisphenol A (DGBEA) will be discussed.

Keywords: building block, amine, synthesis, characterization

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Authors: Akinyemi Olufunminiyi Akinbobola

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This paper analytically discusses the disciplinary problems among Adeyemi College of Education Students in the Ondo State of Nigeria. The paper posits that the causes and types of disciplinary problems experienced by the students are determinacy of disciplinary measures to be taken. The study used a questionnaire titled: Disciplinary Problem Questionnaire (DPQ) to collect data. Five hundred (500) students were randomly sampled in the five schools in the college. The results showed that drug addiction, school curriculum, cultism, peer group influence, overcrowded classroom, political, social, and economic among others are disciplinary problems experienced in the study area. The study made recommendations on how to improve the situation.

Keywords: challenges in higher institutions, disciplinary problems, social vices, students’ indiscipline

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Authors: Aziatul Niza Sadikin, Norasikin Othman, Mohd Ghazali Mohd Nawawi, Umi Aisah Asli, Roshafima Rasit Ali, Rafiziana Md Kasmani

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Non-woven fibrous filter media from empty fruit bunches were fabricated by using chitosan as a binder. Chitosan powder was dissolved in a 1 wt% aqueous acetic acid and 1 wt% to 4 wt% of chitosan solutions was prepared. Chitosan-filled empty fruit bunches filter media have been prepared via wet-layup method. Thermogravimetric analysis (TGA) was performed to study various thermal properties of the fibrous filter media. It was found that the fibrous filter media have undergone several decomposition stages over a range of temperatures as revealed by TGA thermo-grams, where the temperature for 10% weight loss for chitosan-filled EFB filter media and binder-less filter media was at 150oC and 300oC, Respectively.

Keywords: empty fruit bunches, chitosan, filter media, thermal property

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Wiem Gadri

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This study delves into the intriguing properties of Pisot and Salem numbers within the framework of formal Laurent series over finite fields, a domain where these numbers’ spectral charac-teristics, Λm(β) and lm(β), have yet to be fully explored. Utilizing a methodological approach that combines algebraic number theory with the analysis of power series, we extend the foundational work of Erdos, Joo, and Komornik to this new setting. Our research uncovers bounds for lm(β), revealing how these depend on the degree of the minimal polynomial of β and thus offering a novel characterization of Pisot and Salem formal power series. The findings significantly contribute to our understanding of these numbers, highlighting their distribution and properties in the context of formal power series. This investigation not only bridges number theory with formal power series analysis but also sets the stage for further interdisciplinary research in these areas.

Keywords: Pisot numbers, Salem numbers, formal power series, over a finite field

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: Anandkumar Rshindhe

Abstract:

Patents are considered as good monopoly in India. It is a mechanism by which the inventor is encouraged to do invention and also to make available to the society at large with a new useful technology. Patent system does not provide any protection to the invention itself but to the claims (rights) which the patentee has identified in relation to his invention. Thus the patentee is granted monopoly to the extent of his recognition of his own rights in the form of utilities and all other utilities of invention are for the public. Thus we find both benefit to the inventor and the public at large that is the ultimate consumer. But developing any such technology is not free of cost. Inventors do a lot of investment in the coming out with a new technologies. One such example if of Pharmaceutical industries. These pharmaceutical Industries do lot of research and invest lot of money, time and labour in coming out with these invention. Once invention is done or process identified, in order to protect it, inventors approach Patent system to protect their rights in the form of claim over invention. The patent system takes its own time in giving recognition to the invention as patent. Even after the grant of patent the pharmaceutical companies need to comply with many other legal formalities to launch it as a drug (medicine) in market. Thus major portion in patent term is unproductive to patentee and whatever limited period the patentee gets would be not sufficient to recover the cost involved in invention and as a result price of patented product is raised very much, just to recover the cost of invent. This is ultimately a burden on consumer who is paying more only because the legislature has failed to provide for the delay and loss caused to patentee. This problem can be effectively remedied if Patent Term extension is done. Due to patent term extension, the inventor gets some more time in recovering the cost of invention. Thus the end product is much more cheaper compared to non patent term extension.The basic question here arises is that when the patent period granted to a patentee is only 20 years and out of which a major portion is spent in complying with necessary legal formalities before making the medicine available in market, does the company with the limited period of monopoly recover its investment made for doing research. Further the Indian patent Act has certain provisions making it mandatory on the part of patentee to make its patented invention at reasonable affordable price in India. In the light of above questions whether extending the term of patent would be a proper solution and a necessary requirement to protect the interest of patentee as well as the ultimate consumer. The basic objective of this paper would be to check the implications of Extending the Patent term on Indian Consumers. Whether it provides the benefits to the patentee, consumer or a hardship to the Generic industry and consumer.

Keywords: patent term extention, consumer interest, generic drug industry, pharmaceutical industries

Procedia PDF Downloads 451

Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

Abstract:

This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

Procedia PDF Downloads 323

Authors: Lukas Reznak, Maria Reznakova

Abstract:

Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

Procedia PDF Downloads 248

Authors: S. Ivanova, K. Ivanov

Abstract:

Bemitil (2-ethylthiobenzimidazole hydrobromide) is a synthetic adaptogen and actoprotector, with wide-ranging pharmacological activities such as nootropic, antihypoxic, antioxidant, immunostimulant. The intake of Bemitil increases mental and physical performance and could be applied under either normal or extreme conditions. Until 2017 Bemitil was not considered as doping and was used by professional athletes more than 30 years because of its high efficiency and safety. The drug was included in WADA monitoring programme for 2018, and most likely it would be included in WADA Prohibited List for 2019. Usually, a substance/method is included in WADA Prohibited List if it meets any two of the following three criteria: the potential to enhance or enhances sports performance/ potential health risk to the athlete/ violates the spirit of sport. Bemitil has high performance-enhancing potential, but it is also safe- it is controversial whether it should be considered as doping.

Keywords: doping, bemitil, sport, actoprotector

Procedia PDF Downloads 474

Authors: Gee-Cheol Kim, Joo-Won Kang

Abstract:

Structural characteristics of spatial structure are different from that of rahmen structures and it has many factors that are unpredictable experientially. Both horizontal and vertical earthquake should be considered because of seismic behaviour characteristics of spatial structures. This experimental study is conducted about seismic response characteristics of roof structure according to the effect of columns or walls, through scale model of arch structure that has the basic dynamic characteristics of spatial structure. Though remarkable response is not occurred for horizontal direction in the region of higher frequency than the region of frequency that seismic energy is concentrated, relatively large response is occurred in vertical direction. It is proved that seismic response of arch structure with column is varied according to property of column.

Keywords: arch structure, seismic response, shaking table, spatial structure

Procedia PDF Downloads 367

Authors: Philip Robert Alldritt

Abstract:

The continuing increase in human migration is at crisis levels in all areas of the planet. The causes are varied, and the risks are high for the migrants. Migration has been ongoing since the beginning of human emergence on the planet, but for the first time in our historic memory has the, migration reached this level of critical mass. The causes are many. Climate collapse, economic opportunity, drug cartel activity, political upheaval, and gang wars. Many locations are seemingly “within reach” of the migrants, and the push factors are so loaded with hopelessness that almost anyone would be willing to risk anything to improve their conditions. There is no argument about that mass migrations are occurring and will increase in the future. The solutions to this increase are complex. This paper will examine the causes of migration and attempt to provide some reasonable solutions to mitigate the migrations with equitable outcomes that may guide immigration policy in impacted areas.

Keywords: immigration, crisis, climate, cartels

Procedia PDF Downloads 73