Search results for: forecast combination

Authors: Roien Qiam, Shoshi Mizokami

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Parking demand in the Central Business District (CBD) has enlarged with the increase of the number of private vehicles due to rapid economic growth, lack of an efficient public transport and traffic management system. This has resulted in low mobility, poor accessibility, serious congestion, high rates of traffic accident fatalities and injuries and air pollution, mainly because people have to drive slowly around to find a vacant spot. With parking pricing and enforcement policy, considerable advancement could be found, and on-street parking spaces could be managed efficiently and effectively. To evaluate parking demand and making parking policy, it is required to understand the current parking condition and driver’s behavior, understand how drivers choose their parking type and location as well as their behavior toward finding a vacant parking spot under parking charges and search times. This study illustrates the result from an observational, revealed and stated preference surveys and experiment. Attained data shows that there is a gap between supply and demand in parking and it has maximized. For the modeling of the parking decision, a choice model was constructed based on discrete choice modeling theory and multinomial logit model estimated by using SP survey data; the model represents the choice of an alternative among different alternatives which are priced on-street, off-street, and illegal parking. Individuals choose a parking type based on their preference concerning parking charges, searching times, access times and waiting times. The parking assignment model was obtained directly from behavioral model and is used in parking simulation. The study concludes with an evaluation of parking policy.

Keywords: CBD, parking demand forecast, parking policy, parking choice model

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Asmamaw Yehun, Tsegaye Kassa, Addisu Hunegnaw, Martin Vermeer

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There are various models to estimate the neutral atmospheric parameter values, such as in-suite and reanalysis datasets from numerical models. Accurate estimated values of the atmospheric parameters are useful for weather forecasting and, climate modeling and monitoring of climate change. Recently, Global Navigation Satellite System (GNSS) measurements have been applied for atmospheric sounding due to its robust data quality and wide horizontal and vertical coverage. The Global Positioning System (GPS) solutions that includes tropospheric parameters constitute a reliable set of data to be assimilated into climate models. The objective of this paper is, to estimate the neutral atmospheric parameters such as Wet Zenith Delay (WZD), Precipitable Water Vapour (PWV) and Total Zenith Delay (TZD) using six selected GPS stations in the equatorial regions, more precisely, the Ethiopian GPS stations from 2012 to 2015 observational data. Based on historic estimated GPS-derived values of PWV, we forecasted the PWV from 2015 to 2030. During data processing and analysis, we applied GAMIT-GLOBK software packages to estimate the atmospheric parameters. In the result, we found that the annual averaged minimum values of PWV are 9.72 mm for IISC and maximum 50.37 mm for BJCO stations. The annual averaged minimum values of WZD are 6 cm for IISC and maximum 31 cm for BDMT stations. In the long series of observations (from 2012 to 2015), we also found that there is a trend and cyclic patterns of WZD, PWV and TZD for all stations.

Keywords: atmosphere, GNSS, neutral atmosphere, precipitable water vapour

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Authors: Rawa Delshada, Sanaa G. Hamab, Rastee D. Koyeec

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Eighty patients with suspected ischemic heart disease were enrolled in the present study. They were classified into two groups: patients with positive exercise stress test results (n=40) and control group with negative exercise stress test results (n=40). Serum concentration of troponin I, Heart-type Fatty Acid Binding Protein (H-FABP) and Ischemia Modified Albumin (IMA) were measured one hour after performing stress test. Enzyme Linked Immunosorbent Assay was used to measure both troponin I, H-FABP levels, while IMA levels were measured by albumin cobalt binding test. There was no statistically significant difference in the mean concentration of troponin I between two groups (0.75±0.55ng/ml) for patients with positive test result vs. (0.71±0.55ng/ml) for negative test result group with P>0.05. Contrary to our expectation, mean IMA level was slightly higher among control group (70.88±39.76U/ml) compared to (62.7±51.9U/ml) in positive test result group, but still with no statistically significant difference (P>0.05). Median H-FABP level was also higher among negative exercise stress testing group compared the positive one (2ng/ml vs. 1.9ng/ml respectively), but failed to reach statistically significant difference (P>0.05). When quartiles model used to explore the possible association between each study biomarkers with the others; serum H-FABP level was lowest (1.7ng/ml) in highest quartile of IMA and lowest H-FABP (1.8ng/ml) in highest quartile of troponin I but with no statistically significant association (P>0.05). Myocardial ischemia, more likely occurred after exercise stress test, is not capable of causing troponin I release. Furthermore, an increase in H-FABP and IMA levels after stress test are not reflecting myocardial ischemia. Moreover, the combination of troponin I, H-FABP and IMA after measuring their post exercise levels does not improve the diagnostic utility of exercise stress test enormously.

Keywords: cardiac biomarkers, ischemic heart disease, troponin I, ischemia modified albumin, heart-type fatty acid binding protein, exercise stress testing

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Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

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The purpose of work is to study the phenomenon of agitation of storm waves at basin caused by different directions of waves relative to the current provision thrown numerical model based on the equation in shallow water using Boussinesq model MIKE 21 BW. According to the diminishing effect of penetration of a wave optimal solution will be available to be reproduced in reduced model. Another alternative arrangement throws will be proposed to reduce the agitation and the effects of the swell reflection caused by the penetration of waves in the harbor.

Keywords: agitation, Boussinesq equations, combination, harbor

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Authors: N.Benachour, S. Chouchane, J. P. Chopart

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The zinc-nickel deposition on stainless steel substrate was obtained in a chloride bath composed of ZnCl2 (1.8M), NiCl2.6H2O (1.1M), boric acid H3BO3 (1M) and NH4Cl (4M). One configuration was studied the amplitude or field B (0.5 et1T) is parallel to the surface of the working electrodes .the other share the study of various layer was carried out by XRD. The study of the effect of ammonium chloride in combination with the magnetohydrodynamic effect gave several deposits supposedly good physical properties.

Keywords: ammonium chloride, magnetic field, nickel-zinc alloys, co-deposition

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Authors: Kiran Bala Das

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This paper examines the Ukraine wheat export condition after Russian-Ukrainian military confrontation. The political conflict in Ukraine and the recent military intervention of Russia in Crimea is raising concern full effect of the events there is still uncertain, but some hints can be seen in the wheat market by analyzing the trend and pattern of Ukraine wheat export. Crimea is extremely important as it is where most of Ukraine grain exported by ship from its ports of the black sea. Ukraine is again seeking to establish itself a significant exporter of agricultural product with its rich black soil, it is chornozem the top soil layer that makes the country soil so fertile and become one of the major exporter of wheat in the world, its generous supplier of wheat make Ukraine 'Bread basket of Europe'. Ukraine possesses 30% of the world’s richest black soil; its agricultural industry has huge potential especially in grains. European Union (EU) is a significant trading partner of Ukraine but geopolitical tension adversely affects the wheat trade from black sea, which threatens Europe breadbasket. This study also highlights an index of export intensity to analyze the intensity of existing trade for the period 2011-2014 between Ukraine and EU countries. The result show export has intensified over the years, but this year low trade intensity. The overall consequence is hard to determine but if the situation deteriorates and Ukraine cutoff export, international wheat price will hike and grain prices (wheat) also come under the current circumstances and the recent development indicates how the grain market get affected and Agri future now in danger in Ukraine, and its forecast that Ukraine harvest low wheat crop this year and projected decline in export of wheat.

Keywords: breadbasket of Europe, export intensity index, growth rate, wheat export

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Authors: Maha Z. Rizk, Sami A. Fattah, Heba M. Darwish, Sanaa A. Ali, Mai O. Kadry

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Although it is known that nano-TiO2 and other nanoparticles can induce liver toxicity, the mechanisms and the molecular pathogenesis are still unclear. The present study investigated some biochemical indices of nano-sized Titanium dioxide (TiO2 NPS) toxicity in mice liver and the ameliorative efficacy of individual and combined doses of idebenone, carnosine and vitamin E. Nano-anatase TiO2 (21 nm) was administered as a total oral dose of 2.2 gm/Kg daily for 2 weeks followed by the afore-mentioned antioxidants daily either individually or in combination for 1month. TiO2-NPS induced a significant elevation in serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hepatic oxidative stress biomarkers [lipid peroxides (LP), and nitric oxide levels (NOX), while it significantly reduced glutathione reductase (GR), reduced glutathione (GSH) and glutathione peroxidase(GPX) levels. Moreover the quantitative RT-PCR analysis showed that nano-anatase TiO2 can significantly alter the mRNA and protein expressions of the fibrotic factors TGF-B1, VEGFand Smad-2. Histopathological examination of hepatic tissue reinforced the previous biochemical results. Our results also implied that inflammatory responses and liver injury may be involved in nano-anatase TiO2-induced liver toxicity Tumor necrosis factor-α (TNF-α) and Interleukin -6 (IL-6) and increased the percent of DNA damage which was assessed by COMET assay in addition to the apoptotic marker Caspase-3. Moreover mRNA gene expression observed by RT-PCR showed a significant overexpression in nuclear factor relation -2 (Nrf2), nuclear factor kappa beta (NF-Kβ) and the apoptotic factor (bax), and a significant down regulation in the antiapoptotic factor (bcl2) level. In conclusion idebenone, carnosine and vitamin E ameliorated the deviated previously mentioned parameters with variable degrees with the most pronounced role in alleviating the hazardous effect of TiO2 NPS toxicity following the combination regimen.

Keywords: Nano-anatase TiO2, TGF-B1, SMAD-2

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Authors: Intisar, Khalifa, Salim, Al Busaidi

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Recently, the synergic combination of low salinity water flooding with polymer flooding has been a subject of paramount interest for the oil industry. Numerous studies have investigated the efficiency of enhanced oil recovery using low salinity polymer flooding (LSPF). However, there is no clear conclusion that can explain the incremental oil recovery, determine the main factors controlling the oil recovery process, and define the relative contribution of rock/fluids or fluid/fluid interactions to extra oil recovery. Therefore, this study aims to perform a systematic investigation of the interactions between oil, polymer, low salinity and sandstone rock surface from pore to core scale during LSPF. Partially hydrolyzed polyacrylamide (HPAM) polymer, Boise outcrop, a crude oil sample and reservoir cores from an Omani oil field, and brine at two different salinities were used in the study. Several experimental measurements including static bulk measurements of polymer solutions prepared with brines of high and low salinities, single phase displacement experiments, along with rheological, total organic carbon and ion chromatography measurements to analyze ion exchange reactions, polymer adsorption, and viscosity loss were used. In addition, two-phase experiments were performed to demonstrate the oil recovery efficiency of LSPF. The results revealed that the incremental oil recovery from LSPF was attributed to the combination of the reduction in the water-oil mobility ratio, an increase in the repulsion forces between crude oil/brine/rock interfaces and an increase in pH of the aqueous solution. In addition, lowering the salinity of the make-up brine resulted in a larger conformation (expansion) of the polymer molecules, which in turn resulted in less adsorption and a greater in-situ viscosity without any negative impact on injectivity. This plays a positive role in the oil displacement process. Moreover, the loss of viscosity in the effluent of polymer solutions was lower in low-salinity than in high-salinity brine, indicating that an increase in cations concentration (mainly driven by Ca2+ ions) has stronger effect on the viscosity of high-salinity polymer solution compared with low-salinity polymer.

Keywords: polymer, heavy oil, low salinity, COBR interactions

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Authors: Olga Bernikova, Oleg Redkin

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Globalization is a process of worldwide economic, political and cultural integration. Obviously, this phenomenon has both positive and negative issues. This article analyzes the impact of the modern process of globalization on the national traditions of language teaching and research. In this context, the problem of the ratio of local to global can be viewed from several sides. Firstly, since English is the language of over 80 percent of scientific and technical research worldwide, what should be the language of science in certain region? Secondly, language 'globality' is not always associated with English, because intercultural communications may have their regional peculiarities. For example, in the Arab world, Modern Standard Arabic can also be regarded as 'global' phenomenon, since the mother-tongue languages of the population are local Arabic dialects. In addition, the correlation 'local' versus 'global' is manifested not only in the linguistic sphere but also in the methodology used in language acquisition and research. Thus, the major principles of the Arabic philological tradition, which goes back to the 7th century, are still spread in the modern Arab world. At the same time, the terminology and methods of language research that are peculiar to this tradition are quite far from the issues of general linguistics that underlies the description of all the languages of the world. The present research relies on a comparative analysis of sources in Arabic linguistics, including original works in Arabic dating back to the 12th-13th centuries. As a case study, interaction of local and global is also considered on the example of the Arabic teaching and research in Russia. Speaking about the correlation between local and global it is possible to forecast development of two parallel tendencies: the spread of the phenomena of globalization on one hand, and local implementation of a language policy aimed at preserving native languages, including Arabic, on the other.

Keywords: Arabic, global, language, local, tradition

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Vijaya Madhuri Devraj, Swarnalatha Guditi, Kiran Kumar Bokara, Gangadhar Taduri

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Cell-based strategies may open therapeutic approaches that promote tolerance through manipulation of macrophages to increase long-term transplant survival rates and minimize side effects of the current immune suppressive regimens. The aim of the present study was, therefore, to test and compare the therapeutic potential of MSC and Tregs on macrophage polarization to develop an alternate cell-based treatment option in kidney transplantation. In the current protocol, macrophages from kidney transplant recipients with graft dysfunction were co-cultured with MSCs and Treg cells with and without cell-cell contact on transwell plates, further to quantitatively assess macrophage polarization in response to MSC and Treg treatment over time, M1 and M2 cell surface markers were used. Additionally, multiple soluble analytes were analyzed in cell supernatant by using bead-based immunoassays. Furthermore, to confirm our findings, gene expression analysis was done. MSCs induced the formation of M2 macrophages more than Tregs when macrophages M0 were cultured in transwell without cell contact. From this, we deduced the mechanism that soluble factors present in the MSCs condition media are involved in skewing of macrophages towards type 2 macrophages; similarly, in co-culture with cell-cell contact, MSCs resulted in more M2 type macrophages than Tregs. And an important finding of this study is the combination of both MSC-Treg showed significantly effective and consistent results in both with and without cell contact setups. Hence, it is suggestive to prefer MSCs over Tregs for secretome-based therapy and a combination of both for either therapy for effective transplantation outcomes. Our findings underline a key role of Tregs and MSCs in promoting macrophage polarization towards anti-inflammatory type. The study has great importance in prolongation of allograft and patient survival without any rejection by cell-based therapy, which induce self-tolerance and controlling infection.

Keywords: graft rejection, graft tolerance, macrophage polarization, mesenchymal stem cells, regulatory T cells, transplant immunology

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Stephen L. Roberts

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This article offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The article traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This article demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This article argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the article also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this article demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice of global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance.

Keywords: algorithms, global health, pandemic, surveillance

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Authors: Najmeh Hoseini

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Strokerecovery happens through neuroplasticity, which is highly influenced by the environment, including neuro-rehabilitation. Transcranial direct current stimulation (tDCS) may enhance recovery by modulating neuroplasticity. With tDCS, weak direct currents are applied noninvasively to modify excitability in the cortical areas under its electrodes. Combined with functional activities, this may facilitate motor recovery in neurologic disorders such as stroke. The purpose of this case study was to examine the effect of tDCS combined with 30 minutes of traditional physical therapy (PT)on arm function following a stroke. A 29-year-old male with chronic stroke involving the left middle cerebral artery territory went through the treatment protocol. Design The design included 5 weeks of treatment: 1 week of traditional PT, 2 weeks of sham tDCS combined with traditional PT, and 2 weeks of tDCS combined with traditional PT. PT included functional electrical stimulation (FES) of wrist extensors followed by task-specific functional training. Dual hemispheric tDCS with 1 mA intensity was applied on the sensorimotor cortices for the first 20 min of the treatment combined with FES. Assessments before and after each treatment block included Modified Ashworth Scale, ChedokeMcmaster Arm and Hand inventory, Action Research Arm Test (ARAT), and the Box and Blocks Test. Results showed reduced spasticity in elbow and wrist flexors only after tDCS combination weeks (+1 to 0). The patient demonstrated clinically meaningful improvements in gross motor and fine motor control over the duration of the study; however, components of the ARAT that require fine motor control improved the greatest during the experimental block. Average time improvement compared to baseline was26.29 s for tDCS combination weeks, 18.48 s for sham tDCS, and 6.83 for PT standard of care weeks. Combining dual hemispheric tDCS with the standard of care PT demonstrated improvements in hand dexterity greater than PT alone in this patient case.

Keywords: tDCS, stroke, case study, physical therapy

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Saradha Priya

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Developing virtual human is very important to meet the challenges occurred in many applications where human find difficult or risky to perform the task. A robot is a machine that can perform a task automatically or with guidance. Robotics is generally a combination of artificial intelligence and physical machines (motors). Computational intelligence involves the programmed instructions. This project proposes a robotic vehicle that has a camera, PIR sensor and text command based movement. It is specially designed to perform surveillance and other few tasks in the most efficient way. Serial communication has been occurred between a remote Base Station, GUI Application, and PC.

Keywords: Zigbee, camera, pirsensor, wireless transmission, DC motor

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Authors: Mohamed Amhal, Jose Sayritupac

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Photovoltaic (PV) systems have risen as one of the modern renewable energy sources that are used in wide ranges to produce electricity and deliver it to the electrical grid. In parallel, monitoring systems have been deployed as a key element to track the energy production and to forecast the total production for the next days. The reliability of the PV energy production has become a crucial point in the analysis of PV systems. A deeper understanding of each phenomenon that causes a gain or a loss of energy is needed to better design, operate and maintain the PV systems. This work analyzes the current losses distribution in PV systems starting from the available solar energy, going through the DC side and AC side, to the delivery point. Most of the phenomena linked to energy losses and gains are considered and modeled, based on real time monitoring data and datasheets of the PV system components. An analysis of the order of magnitude of each loss is compared to the current literature and commercial software. To date, the analysis of PV systems performance based on a breakdown structure of energy losses and gains is not covered enough in the literature, except in some software where the concept is very common. The cutting-edge of the current analysis is the implementation of software tools for energy losses estimation in PV systems based on several energy losses definitions and estimation technics. The developed tools have been validated and tested on some PV plants in France, which are operating for years. Among the major findings of the current study: First, PV plants in France show very low rates of soiling and aging. Second, the distribution of other losses is comparable to the literature. Third, all losses reported are correlated to operational and environmental conditions. For future work, an extended analysis on further PV plants in France and abroad will be performed.

Keywords: energy gains, energy losses, losses distribution, monitoring, photovoltaic, photovoltaic systems

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Authors: Mehrnoosh Askarizadeh

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The increasing variations of the business world correspond with a high diversity of theoretical perspectives used in project management research. This diversity is reflected by a variety of influencing factors, which have been the subject of empirical studies. This article aims to systemize the different streams of research on the basis of a literature review and at developing a research framework influencing factors. We will identify fundamental elements of a project management theory. The framework consists of three dimensions: design, context, and goal. Its purpose is to support the combination of different perspectives and the development of strategies for further research.

Keywords: project, goal, project management, influencing factors

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Authors: Mohamad Amin Amini, Mehdi Poursha

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Nonlinear response history analysis (NL-RHA) is a robust analytical tool for estimating the seismic demands of structures responding in the inelastic range. However, because of its conceptual and numerical complications, the nonlinear static procedure (NSP) is being increasingly used as a suitable tool for seismic performance evaluation of structures. The conventional pushover analysis methods presented in various codes (FEMA 356; Eurocode-8; ATC-40), are limited to the first-mode-dominated structures, and cannot take higher modes effect into consideration. Therefore, since more than a decade ago, researchers developed enhanced pushover analysis procedures to take higher modes effect into account. The main objective of this study is to propose an enhanced invariant lateral displacement distribution to take higher modes effect into consideration in performing a displacement-based pushover analysis, whereby a set of laterally applied displacements, rather than forces, is monotonically applied to the structure. For this purpose, the effect of different parameters such as the spectral displacement of ground motion, the modal participation factor, and the effective modal participating mass ratio on the lateral displacement distribution is investigated to find the best distribution. The major simplification of this procedure is that the effect of higher modes is concentrated into a single invariant lateral load distribution. Therefore, only one pushover analysis is sufficient without any need to utilize a modal combination rule for combining the responses. The invariant lateral displacement distribution for pushover analysis is then calculated by combining the modal story displacements using the modal combination rules. The seismic demands resulting from the different procedures are compared to those from the more accurate nonlinear response history analysis (NL-RHA) as a benchmark solution. Two structures of different heights including 10 and 20-story special steel moment resisting frames (MRFs) were selected and evaluated. Twenty ground motion records were used to conduct the NL-RHA. The results show that more accurate responses can be obtained in comparison with the conventional lateral loads when the enhanced modal lateral displacement distributions are used.

Keywords: displacement-based pushover, enhanced lateral load distribution, higher modes effect, nonlinear response history analysis (NL-RHA)

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Authors: Aliakbar Alijarahi, Ali Khaleghi, Azadehe Afrasiyabi

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One of the emerging technologies in the field of education that can be effectively profitable, called augmented reality, where the combination of real world and virtual images in real time produces new concepts that can facilitate learning. The paper, providing an introduction to the general concept of augmented reality, aims at surveying its capabitities in different areas, with an emphasis on Education, It seems quite necessary to have comparative study on virtual/e-learning and augmented reality and conclude their differences in education methods. As an review article, the paper is composed, instead of producing new concepts, to sum-up and analayze accomplished works related to the subject.

Keywords: augmented reality, education, virtual learning, e-learning

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Authors: Ikram Elsiddig, Yacouba Djamila, Amna Hamad

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Abstract—The worldwide increasing of using herbs in form of medicine with or without prescription medications potentiates the interactions between herbal products and conventional medicines; due to more research for herb-drug interactions are needed. for a long time hyperglycemia had been treated with several medicinal plants. A. sativum, belonging to the Liliaceae family is well known for its medicinal uses in African traditional medicine, it used for treating of many human diseases mainly diabetes, high cholesterol and high blood pressure. The purpose of this study is to determine the interaction effect between A. sativum bulb extracts and metformin drug used in diabetes treatment. The in vitro and in vivo evaluation were conducted by glucose reuptake using isolated rats hemidiaphgrams tissue and by estimate glucose tolerance in glucose-loaded wistar albino rats. The results showed that, petroleum ether, chloroform and ethyl acetate extracts were found to have activity of glucose uptake in isolated rats hemidiaphgrams of 24.11 mg/g, 19.07 mg/g and 15.66 mg/g compared to metformin drug of 17 mg/g. These activity were reducded to 17.8 mg/g, 13.59 mg/g and 14.46 mg/g after combination with metformin, metformin itself reduced to 13.59 mg/g, 14.46 mg/g and 12.71 mg/g in comination with chloroform and ethyl acetate. These decrease in activity could be due to herbal–drug interaction between the extracts of A. sativum bulb and metformin drug. The interaction between A. sativum extract and metformin was also shown by in vivo study on the induced hyperglycemic rats. The glucose level after administered of 200 mg/kg was found to be increase with 47.2 % and 17.7% at first and second hour compared to the increase of blood glucose in the control group of 82.6% and76.7%.. At fourth hour the glucose level was became less than normal with 3.4% compared to control which continue to increase with 68.2%. Dose of 400 mg/kg at first hour showed increase in blood glucose of 31.5 %, at second and fourth hours the glucose level was became less than normal with decrease of 3.2 % and 30.4%. After combination the activity was found to be less than that of extract at both high and low dose, whereas, at first and second hour, the glucose level was found to be increase with 50.4% and 21.2%, at fourth hour the glucose level was became less than normal with 14%. Therefore A. sativum could be a potential source for anti-diabetic when it used alone, and it is significant important to use the garlic extract alone instead of combined with Metformin drug in diabetes- treatment.

Keywords: Antagonistic, Garlic, Metformin, Synergistic

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Authors: Ladda Her, Juntip Kanjanasilp, Ratree Sawangjit, Nathorn Chaiyakunapruk

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Cough is the common symptom of the respiratory tract infections or non-infections; the duration of cough indicates a classification and severity of disease. Herbal medicines can be used as the alternative to drugs for relief of cough symptoms from acute and chronic disease. Eucalyptus was used for reducing cough with evidences suggesting it has an active role in reduction of airway inflammation. The present study aims to evaluate efficacy and safety of eucalyptus for relief of cough symptom in respiratory disease. Method: The Cochrane Library, MEDLINE (PubMed), Scopus, CINAHL, Springer, Science direct, ProQuest, and THAILIS databases. From its inception until 01/02/2019 for randomized control trials. We follow for the efficacy and safety of eucalyptus for reducing cough. Methodological quality was evaluated by using the Cochrane risk of bias tool; two reviewers in our team screened eligibility and extracted data. Result: Six studies were included for the review and five studies were included in the meta-analysis, there were 1.911 persons including children (n: 1) and adult (n: 5) studies; for study in children and adult were between 1 and 80 years old, respectively. Eucalyptus was used as mono herb (n: 2) and in combination with other herbs form (n: 4). All of the studies with eucalyptus were compared for efficacy and safety with placebo or standard treatment, Eucalyptus dosage form in studies included capsules, spray, and syrup. Heterogeneity was 32.44 used random effect model (I² = 1.2%, χ² = 1.01; P-value = 0.314). The efficacy of eucalyptus was showed a reduced cough symptom statistically significant (n = 402, RR: 1.40, 95%CI [1.19, 1.65], P-value < 0.0001) when compared with placebo. Adverse events (AEs) were reported mild to moderate intensity with mostly gastrointestinal symptom. The methodological quality of the included trials was overall poor. Conclusion: Eucalyptus appears to be beneficial and safe for relieving in respiratory diseases focus on cough frequency. The evidence was inconclusive due to limited quality trial. Well-designed trials for evaluating the effectiveness in humans, the effectiveness for reducing cough symptom in human is needed. Eucalyptus had safety as monotherapy or in combination with other herbs.

Keywords: cough, eucalyptus, cineole, herbal medicine, systematic review, meta-analysis

Procedia PDF Downloads 152

Authors: Mohamed Sifan, Brabha Nagaratnam, Julian Thamboo, Keerthan Poologanathan

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Ultra high performance concrete (UHPC) is a type of cementitious material known for its exceptional strength, ductility, and durability. However, its production is often associated with high costs due to the significant amount of cementitious materials required and the use of fine powders to achieve the desired strength. The aim of this research is to explore the feasibility of developing cost-effective UHPC mixes using locally available materials. Specifically, the study aims to investigate the use of coarse limestone sand along with other sand types, namely, basalt sand, dolomite sand, and river sand for developing UHPC mixes and evaluating its performances. The study utilises the particle packing model to develop various UHPC mixes. The particle packing model involves optimising the combination of coarse limestone sand, basalt sand, dolomite sand, and river sand to achieve the desired properties of UHPC. The developed UHPC mixes are then evaluated based on their workability (measured through slump flow and mini slump value), compressive strength (at 7, 28, and 90 days), splitting tensile strength, and microstructural characteristics analysed through scanning electron microscope (SEM) analysis. The results of this study demonstrate that cost-effective UHPC mixes can be developed using locally available materials without the need for silica fume or fly ash. The UHPC mixes achieved impressive compressive strengths of up to 149 MPa at 28 days with a cement content of approximately 750 kg/m³. The mixes also exhibited varying levels of workability, with slump flow values ranging from 550 to 850 mm. Additionally, the inclusion of coarse limestone sand in the mixes effectively reduced the demand for superplasticizer and served as a filler material. By exploring the use of coarse limestone sand and other sand types, this study provides valuable insights into optimising the particle packing model for UHPC production. The findings highlight the potential to reduce costs associated with UHPC production without compromising its strength and durability. The study collected data on the workability, compressive strength, splitting tensile strength, and microstructural characteristics of the developed UHPC mixes. Workability was measured using slump flow and mini slump tests, while compressive strength and splitting tensile strength were assessed at different curing periods. Microstructural characteristics were analysed through SEM and energy dispersive X-ray spectroscopy (EDS) analysis. The collected data were then analysed and interpreted to evaluate the performance and properties of the UHPC mixes. The research successfully demonstrates the feasibility of developing cost-effective UHPC mixes using locally available materials. The inclusion of coarse limestone sand, in combination with other sand types, shows promising results in achieving high compressive strengths and satisfactory workability. The findings suggest that the use of the particle packing model can optimise the combination of materials and reduce the reliance on expensive additives such as silica fume and fly ash. This research provides valuable insights for researchers and construction practitioners aiming to develop cost-effective UHPC mixes using readily available materials and an optimised particle packing approach.

Keywords: cost-effective, limestone powder, particle packing model, ultra high performance concrete

Procedia PDF Downloads 110

Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

Procedia PDF Downloads 110

Authors: Fuad M. Alkoot

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The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy.

Keywords: fractal geometry, machine learning, classifier, fractal dimension

Procedia PDF Downloads 217

Authors: Muazzam Ali Khan Khattak

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Next Generation Network is combination of different networks having different technologies. Due to mobile nature of nodes the movement of nodes occurs from one network to another network. Multicasting in such networks is still a hot issue of research because the user in today's world wants reliable communication wherever it lies. Due to heterogeneity of NGN it is very difficult to handle reliable multicast communication. In this paper we proposed an improved scheme for reliable multicast communication in next generation networks. Because multicast communication is very important to deliver same data packets to multiple receivers and minimize the network traffic. This new scheme will make the multicast communication in NGN more reliable and efficient.

Keywords: next generation networks, route request, IPT, NACK, ARQ, DTN

Procedia PDF Downloads 503

Authors: David Lindström

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We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface.

Keywords: expensive function, infill sampling criterion, kriging, global optimization, response surface, Runge phenomenon

Procedia PDF Downloads 578

Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

Procedia PDF Downloads 510