Search results for: time series data mining

Authors: Zavkiddin Mirtoshev, Mirabbos Mirkamalov

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We have analyzed the time series of full disk integrated soft X-ray (SXR) and hard X-ray (HXR) emission from the solar corona during 2004 January 1 to 2009 December 31, covering the descending phase of solar cycle 23. We employed the daily X-ray index (DXI) derived from X-ray observations from the Solar X-ray Spectrometer (SOXS) mission in four different energy bands: 4-5.5; 5.5-7.5 keV (SXR) and 15-20; 20-25 keV (HXR). The application of Lomb-Scargle periodogram technique to the DXI time series observed by the Silicium detector in the energy bands reveals several short and intermediate periodicities of the X-ray corona. The DXI explicitly show the periods of 13.6 days, 26.7 days, 128.5 days, 151 days, 180 days, 220 days, 270 days, 1.24 year and 1.54 year periods in SXR as well as in HXR energy bands. Although all periods are above 70% confidence level in all energy bands, they show strong power in HXR emission in comparison to SXR emission. These periods are distinctly clear in three bands but somehow not unambiguously clear in 5.5-7.5 keV band. This might be due to the presence of Ferrum and Ferrum/Niccolum line features, which frequently vary with small scale flares like micro-flares. The regular 27-day rotation and 13.5 day period of sunspots from the invisible side of the Sun are found stronger in HXR band relative to SXR band. However, flare activity Rieger periods (150 and 180 days) and near Rieger period 220 days are very strong in HXR emission which is very much expected. On the other hand, our current study reveals strong 270 day periodicity in SXR emission which may be connected with tachocline, similar to a fundamental rotation period of the Sun. The 1.24 year and 1.54 year periodicities, represented from the present research work, are well observable in both SXR as well as in HXR channels. These long-term periodicities must also have connection with tachocline and should be regarded as a consequence of variation in rotational modulation over long time scales. The 1.24 year and 1.54 year periods are also found great importance and significance in the life formation and it evolution on the Earth, and therefore they also have great astro-biological importance. We gratefully acknowledge support by the Indian Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP, the Centre is affiliated to the United Nations), Physical Research Laboratory (PRL) at Ahmedabad, India. This work has done under the supervision of Prof. Rajmal Jain and paper consist materials of pilot project and research part of the M. Tech program which was made during Space and Atmospheric Science Course.

Keywords: corona, flares, solar activity, X-ray emission

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: M. Y. Lee, D. H. Shin, S. H. Park, W.C. Ham, S.K. Ko, C. G. Song

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Photoacoustic Tomography (PAT) is a promising medical imaging modality that combines optical imaging contrast with the spatial resolution of ultrasound imaging. It can also distinguish the changes in biological features. But, real-time PAT system should be confirmed due to photoacoustic effect for tissue. Thus, we have developed a real-time PAT system using a custom-developed data acquisition board and ultrasound linear probe. To evaluate performance of our system, phantom test was performed. As a result of those experiments, the system showed satisfactory performance and its usefulness has been confirmed. We monitored the degradation of inflammation which induced on the rat’s kidney using real-time PAT.

Keywords: photoacoustic tomography, inflammation detection, rat, kidney, contrast agent, ultrasound

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Authors: Orhun Vural, Oguz Bayat, Rustu Akay, Osman N. Ucan

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This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms.

Keywords: parallel k-means algorithm, parallel clustering, clustering algorithms, clustering on flowing data

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Authors: Tomasz Smolinski, Irena Herdzik-Koniecko, Marta Pyszynska, M. Rogowski

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Old electronic components can be easily found nowadays. Significant quantities of valuable metals such as gold, silver or copper are used for the production of advanced electronic devices. Old useless electronic device slowly became a new source of precious metals, very often more efficient than natural. For example, it is possible to recover more gold from 1-ton personal computers than seventeen tons of gold ore. It makes urban mining industry very profitable and necessary for sustainable development. For the recovery of metals from waste of electronic equipment, various treatment options based on conventional physical, hydrometallurgical and pyrometallurgical processes are available. In this group hydrometallurgy processes with their relatively low capital cost, low environmental impact, potential for high metal recoveries and suitability for small scale applications, are very promising options. Institute of Nuclear Chemistry and Technology has great experience in hydrometallurgy processes especially focused on recovery metals from industrial and agricultural wastes. At the moment, urban mining project is carried out. The method of effective recovery of valuable metals from central processing units (CPU) components has been developed. The principal processes such as acidic leaching and solvent extraction were used for precious metals recovery from old processors and graphic cards. Electronic components were treated by acidic solution at various conditions. Optimal acid concentration, time of the process and temperature were selected. Precious metals have been extracted to the aqueous phase. At the next step, metals were selectively extracted by organic solvents such as oximes or tributyl phosphate (TBP) etc. Multistage mixer-settler equipment was used. The process was optimized.

Keywords: electronic waste, leaching, hydrometallurgy, metal recovery, solvent extraction

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Authors: Saeed Bagherifam, Trevor Brown, Chris Fellows, Ravi Naidu

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Over the last century, rapid urbanization, industrial emissions, and mining activities have resulted in widespread contamination of the environment by heavy metal(loid)s. Arsenic (As) is a toxic metalloid belonging to group 15 of the periodic table, which occurs naturally at low concentrations in soils and the earth’s crust, although concentrations can be significantly elevated in natural systems as a result of dispersion from anthropogenic sources, e.g., mining activities. Bioavailability is the fraction of a contaminant in soils that is available for uptake by plants, food chains, and humans and therefore presents the greatest risk to terrestrial ecosystems. Numerous attempts have been made to establish in situ and ex-situ technologies of remedial action for remediation of arsenic-contaminated soils. In situ stabilization techniques are based on deactivation or chemical immobilization of metalloid(s) in soil by means of soil amendments, which consequently reduce the bioavailability (for biota) and bioaccessibility (for humans) of metalloids due to the formation of low-solubility products or precipitates. This study investigated the effectiveness of two different types of synthetic manganese and iron oxides (birnessite and goethite) for stabilization of As in a soil spiked with 1000 mg kg⁻¹ of As and treated with 10% dosages of soil amendments. Birnessite was made using HCl and KMnO₄, and goethite was synthesized by the dropwise addition of KOH into Fe(NO₃) solution. The resulting contaminated soils were subjected to a series of chemical extraction studies including sequential extraction (BCR method), single-step extraction with distilled (DI) water, 2M HNO₃ and simplified bioaccessibility extraction tests (SBET) for estimation of bioaccessible fractions of As in two different soil fractions ( < 250 µm and < 2 mm). Concentrations of As in samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that soil with birnessite reduced bioaccessibility of As by up to 92% in both soil fractions. Furthermore, the results of single-step extractions revealed that the application of both birnessite and Goethite reduced DI water and HNO₃ extractable amounts of arsenic by 75, 75, 91, and 57%, respectively. Moreover, the results of the sequential extraction studies showed that both birnessite and goethite dramatically reduced the exchangeable fraction of As in soils. However, the amounts of recalcitrant fractions were higher in birnessite, and Goethite amended soils. The results revealed that the application of both birnessite and goethite significantly reduced bioavailability and the exchangeable fraction of As in contaminated soils, and therefore birnessite and Goethite amendments might be considered as promising adsorbents for stabilization and remediation of As contaminated soils.

Keywords: arsenic, bioavailability, in situ stabilisation, metalloid(s) contaminated soils

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Authors: Onyinyechi Joy Kingdom

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Mergers and Acquisitions (M&A) have been proposed as a mechanism through which, problems associated with inefficiency or poor performance in financial institution could be addressed. The aim of this study is to examine the proposition that recapitalization of banks, which encouraged Mergers and Acquisitions in Nigeria banking system, would strengthen the domestic banks, improve financial deepening and the confidence of depositors. Hence, this study examines the impact of the 2005 M&A in the Nigerian-banking sector on financial deepening using mixed method (quantitative and qualitative approach). The quantitative process of this study utilised annual time series for financial deepening indicator for the period of 1997 to 2012. While, the qualitative aspect adopted semi-structured interview to collate data from three merged banks and three stand-alone banks to explore, understand and complement the quantitative results. Furthermore, a framework thematic analysis is employed to analyse the themes developed using NVivo 11 software. Using the quantitative approach, findings from the equality of mean test (EMT) used suggests that M&A have significant impact on financial deepening. However, this method is not robust enough given its weak validity as it does not control for other potential factors that may determine financial deepening. Thus, to control for other factors that may affect the level of financial deepening, a Multiple Regression Model (MRM) and Interrupted Times Series Analysis (ITSA) were applied. The coefficient for M&A dummy turned negative and insignificant using MRM. In addition, the estimated linear trend of the post intervention when ITSA was applied suggests that after M&A, the level of financial deepening decreased annually; however, this was statistically insignificant. Similarly, using the qualitative approach, the results from the interview supported the quantitative results from ITSA and MRM. The result suggests that interest rate should fall when capital base is increased to improve financial deepening. Hence, this study contributes to the existing literature the importance of other factors that may affect financial deepening and the economy when policies that will enhance bank performance and the economy are made. In addition, this study will enable the use of valuable policy instruments relevant to monetary authorities when formulating policies that will strengthen the Nigerian banking sector and the economy.

Keywords: mergers and acquisitions, recapitalization, financial deepening, efficiency, financial crisis

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Authors: Anjana Aggarwal, Sheilja Walia

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Prevalence of obesity and overweight is increasing due to sedentary lifestyles and busy schedules of people that spend less time on physical exercise. To reduce weight, people are finding easier and more convenient ways. The easiest solution is the use of dietary supplements and fat burners. These are products that decrease body weight by increasing the basal metabolic rate. Various reports have been published on the consumption of fat burners leading to heart palpitations, seizures, anxiety, depression, psychosis, bradycardia, insomnia, muscle contractions, hepatotoxicity, and even liver failure. Case reports and series are reporting that the ingredients present in the fat burners caused acute liver failure (ALF) and hepatic toxicity in many cases. Another contributing factor is the absence of regulations from the Food and Drug Administration on these products, leading to increased consumption and a higher risk of liver diseases among the population. This systematic review aims to attain a better understanding of the dietary supplements used globally to reduce weight and document the case reports/series of acute liver failure caused by the consumption of fat burners. Electronic databases like PubMed, Cochrane, Google Scholar, etc., will be systematically searched for relevant articles. Various websites of dietary products and brands that sell such supplements, Journals of Hepatology, National and international projects launched for ALF, and their reports, along with the review of grey literature, will also be done to get a better understanding of the topic. After discussing with the co-author, the selection and screening of the articles will be performed by the author. The studies will be selected based on the predefined inclusion and exclusion criteria. The case reports and case series that will be included in the final list of the studies will be assessed for methodological quality using the CARE guidelines. The results from this study will provide insights and a better understanding of fat burners. Since the supplements are easily available in the market without any restrictions on their sale, people are unaware of their adverse effects. The consumption of these supplements causes acute liver failure. Thus, this review will provide a platform for future larger studies to be conducted.

Keywords: acute liver failure, dietary supplements, fat burners, weight loss supplements

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: M. H. Aldahrob, A. Kokce, S. Altindal, H. E. Lapa

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In order to obtain the detailed information about the effect of (Zn-doped PVA) interfacial layer, surface states (Nss) and series resistance (Rs) on electrical characteristics, both Au/n- type 4H-SiC (MS) with and without (Zn doped PVA) interfacial layer were fabricated to compare. The main electrical parameters of them were investigated using forward and reverse bias current-voltage (I-V), capacitance-voltage (C-V) and conductance –voltage (G/W –V) measurements were performed at room temperature. Experimental results show that the value of ideality factor (n), zero –bias barrier height (ΦBo), Rs, rectifier rate (RR=IF/IR) and the density of Nss are strong functions interfacial layer and applied bias voltage. The energy distribution profile of Nss was obtained from forward bias I-V data by taking into account voltage dependent effective BH (ΦBo) and ideality factor (n(V)). Voltage dependent profile of Rs was also obtained both by using Ohm’s law and Nicollian and Brew methods. The other main diode parameters such as the concentration of doping donor atom (ND), Fermi energy level (EF).BH (ΦBo), depletion layer with (WD) were obtained by using the intercept and slope of the reverse bias C-2 vs V plots. It was found that (Zn-doped PVA) interfacial layer lead to a quite decrease in the values Nss, Rs and leakage current and increase in shunt resistance (Rsh) and RR. Therefore, we can say that the use of thin (Zn-doped PVA) interfacial layer can quite improved the performance of MS structure.

Keywords: interfacial polymer layer, thickness dependence, electric and dielectric properties, series resistance, interface state

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Authors: Smita Sonker, Uaday Singh

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Various investigators have determined the degree of approximation of functions belonging to the classes W(L r , ξ(t)), Lip(ξ(t), r), Lip(α, r), and Lipα using different summability methods with monotonocity conditions. Recently, Lal has determined the degree of approximation of the functions belonging to Lipα and W(L r , ξ(t)) classes by using Ces`aro-N¨orlund (C 1 .Np)- summability with non-increasing weights {pn}. In this paper, we shall determine the degree of approximation of 2π - periodic functions f belonging to the function classes Lipα and W(L r , ξ(t)) by C 1 .T - means of Fourier series of f. Our theorems generalize the results of Lal and we also improve these results in the light off. From our results, we also derive some corollaries.

Keywords: Lipschitz classes, product matrix operator, signals, trigonometric Fourier approximation

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Authors: Ratul Das

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The present experimental study insights the decontamination of instantaneous velocity fluctuations captured by Acoustic Doppler Velocimeter (ADV) in gravel-bed streams to ascertain near-bed turbulence for low Reynolds number. The interference between incidental and reflected pulses produce spikes in the ADV data especially in the near-bed flow zone and therefore filtering the data are very essential. Nortek’s Vectrino four-receiver ADV probe was used to capture the instantaneous three-dimensional velocity fluctuations over a non-cohesive bed. A spike removal algorithm based on the acceleration threshold method was applied to note the bed roughness and its influence on velocity fluctuations and velocity power spectra in the carrier fluid. The velocity power spectra of despiked signals with a best combination of velocity threshold (VT) and acceleration threshold (AT) are proposed which ascertained velocity power spectra a satisfactory fit with the Kolmogorov “–5/3 scaling-law” in the inertial sub-range. Also, velocity distributions below the roughness crest level fairly follows a third-degree polynomial series.

Keywords: acoustic doppler velocimeter, gravel-bed, spike removal, reynolds shear stress, near-bed turbulence, velocity power spectra

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Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

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This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

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Authors: K. Santacruz-Gomez, E. Silva-Campa, S. Álvarez-García, V. Mata-Haro, D. Soto-Puebla, M. Pedroza-Montero

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Blood gamma irradiation is the only available method to prevent transfusion-associated graft versus host disease (TA-GVHD). However, when blood is irradiated, determine blood shelf time is crucial. Non-irradiated blood has a self-time from 21 to 35 days when is preserved with an anticoagulated solution and stored at 4°C. During their storage, red blood cells (RBC) undergo a series of biochemical, biomechanical and molecular changes involving what is known as storage lesion (SL). SL include loss of structural integrity of RBC, a decrease of 2,3-diphosphatidylglyceric acid levels, and an increase of both ion potassium concentration and hemoglobin (Hb). On the other hand, Atomic force Microscopy (AFM) represents a versatile tool for a nano-scale high-resolution topographic analysis in biological systems. In order to evaluate SL in irradiated and non-irradiated blood, RBC topography and morphometric parameters were obtained from an AFM XE-BIO system. Cell viability was followed using flow cytometry. Our results showed that early markers as nanoscale roughness, allow us to evaluate blood quality since another perspective.

Keywords: AFM, blood γ-irradiation, roughness, storage lesion

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Authors: Jamal Wiwoho, Pujiyono, Triyanto

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Terrorism is a real enemy for all countries, including Indonesia. Bomb attacks in some parts of Indonesia are proof that Indonesia has serious problems with terrorism. Perpetrators of terror are arrested and imprisoned, and some of them were executed. However, this method did not succeed in stopping the terrorist attacks. Former terrorists continue to carry out bomb attacks. Therefore, this paper proposes a program towards deradicalization efforts of former terrorists through entrepreneurship. This is necessary because it is impossible to change their radical ideology. The program is also motivated by understanding that terrorists generally come from poor families. This program aims to occupy their time with business activities so there is no time to plan and carry out bomb attacks. This research is an empirical law study. Data were collected by literature study, observation, and in-depth interviews. Data were analyzed with the Miles and Huberman interactive model. The results show that the entrepreneurship program is effective to prevent terrorist attack. Former terrorists are busy with their business. Therefore, they have no time to carry out bomb attacks.

Keywords: deradicalization, terrorism, terrorists, entrepreneurship

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Authors: Abdulkadir Magaji

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This study analyzes and compares the actual measured and simulated performance of a 3.2 kwP grid-connected photovoltaic system. The system is located at the Outdoor Facility of Government Day secondary School Katsina State, which lies approximately between coordinate of 12°15′N 7°30′E. The system consists of 14 Mono crystalline silicon modules connected in two strings of 7 series-connected modules, each facing north at a fixed tilt of 340. The data presented in this study were measured in the year 2015, where the system supplied a total of 4628 kWh to the local electric utility grid. The performance of the system was simulated using PVsyst software using measured and Meteonorm derived climate data sets (solar radiation, ambient temperature and wind speed). The comparison between measured and simulated energy yield are discussed. Although, both simulation results were similar, better comparison between measured and predicted monthly energy yield is observed with simulation performed using measured weather data at the site. The measured performance ratio in the present study shows 58.4% is higher than those reported elsewhere as compared in the study.

Keywords: performance, evaluation, grid connection, photovoltaic system

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Authors: Ishmael Tingbani

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This paper adopts a contingency theory approach to investigate the relationship between working capital management and profitability using data of 225 listed British firms on the London Stock Exchange for the period 2001-2011. The paper employs a panel data analysis on a series of interactive models to estimate this relationship. The findings of the study confirm the relevance of the contingency theory. Evidence from the study suggests that the impact of working capital management on profitability varies and is constrained by organizational contingencies (environment, resources, and management factors) of the firm. These findings have implications for a more balanced and nuanced view of working capital management policy for policy-makers.

Keywords: working capital management, profitability, contingency theory approach, interactive models

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Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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Authors: J. Ranjbarn, A. Alibeigloo

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In this paper, we present an analytical method for analysis of nano-scale spherical shell subjected to thermo-mechanical shocks based on nonlocal elasticity theory. Thermo-mechanical properties of nano shpere is assumed to be temperature dependent. Governing partial differential equation of motion is solved analytically by using Laplace transform for time domain and power series for spacial domain. The results in Laplace domain is transferred to time domain by employing the fast inverse Laplace transform (FLIT) method. Accuracy of present approach is assessed by comparing the the numerical results with the results of published work in literature. Furtheremore, the effects of non-local parameter and wall thickness on the dynamic characteristics of the nano-sphere are studied.

Keywords: nano-scale spherical shell, nonlocal elasticity theory, thermomechanical shock, dynamic response

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: Bernard Moeketsi Hlalele

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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.

Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Arka Roy, Chandra Prakash Dubey

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Inverse problem generally used for recovering hidden information from outside available data. Vertical component of gravity field we will be going to use for underneath density structure calculation. Ill-posing nature is main obstacle for any inverse problem. Linear regularization using Tikhonov formulation are used for appropriate choice of SVD and GSVD components. For real time data handle, signal to noise ratios should have to be less for reliable solution. In our study, 2D and 3D synthetic model with rectangular grid are used for gravity field calculation and its corresponding inversion for density reconstruction. Fine grid also we have considered to hold any irregular structure. Keeping in mind of algebraic ambiguity factor number of observation point should be more than that of number of data point. Picard plot is represented here for choosing appropriate or main controlling Eigenvalues for a regularized solution. Another important study is depth resolution plot (DRP). DRP are generally used for studying how the inversion is influenced by regularizing or discretizing. Our further study involves real time gravity data inversion of Vredeforte Dome South Africa. We apply our method to this data. The results include density structure is in good agreement with known formation in that region, which puts an additional support of our method.

Keywords: depth resolution plot, gravity inversion, Picard plot, SVD, Tikhonov formulation

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Authors: Leila Rashidian, Mina Rajabali

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Industrialization has made progress and comfort for human beings in many aspects. It is not only achievement for the global environment but also factor for destruction and disruption of the Earth's climate. In this study, we used LARS.WG model and down scaling of general circulation climate model HADCM-3 daily precipitation amounts, minimum and maximum temperature and daily sunshine hours. These data are provided by the meteorological organization for Caspian Sea coastal station such as Anzali, Manjil, Rasht, Lahijan and Astara since their establishment is from 1982 until 2010. According to the IPCC scenarios, including series A1b, A2, B1, we tried to simulate data from 2010 to 2040. The rainfall pattern has changed. So we have a rainfall distribution inappropriate in different months.

Keywords: climate change, Lars.WG, HADCM3, Gillan province, climatic parameters, A2 scenario

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Authors: Siddhartha Chauhan, Nitin Kumar Kotania

Abstract:

Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network. Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.

Keywords: buffer overflow problem, mobile sink, virtual grid, wireless sensor networks

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

Abstract:

In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.

Keywords: forecasting, generalized extreme value (GEV), meteorology, return level

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Authors: Miladi Yasmine, Bouhlel Salah, Garnit Hechmi

Abstract:

Hydrothermal vein-type deposits of the Hammam Jedidi F-Ba(Pb-Zn-Cu) are hosted in Lower Jurassic, Cretaceous and Tertiary series, and located near a very important structural lineament (NE-SW) corresponding to the Hammam Jedidi Fault in the Tunisian Dorsale. The circulation of the ore forming fluid is triggered by a regional tectonic compressive phase which occurred during the miocène time. Mineralization occurs as stratabound and vein-type orebodies adjacent to the Triassic salt diapirs and within fault in Jurassic limestone. Fluid inclusions data show that two distinct fluids were involved in the mineralisation deposition: a warmer saline fluid (180°C, 20 wt % NaCl equivalent) and cooler less saline fluid (126°C, 5wt%NaCl equivalent). The contrasting salinities and halogen ratios suggest that this two fluid derived from one of the brine originated after the dissolution of halite as suggested by its high salinity. The other end member, as indicated by the low Cl/Br ratios, acquired its low salinity by dilution of Br enriched evaporated seawater. These results are compatible with Mississippi-Valley- type mineralization.

Keywords: Jebel Oust, fluid inclusions, North Eastern Tunisia, mineralization

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

Abstract:

Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Young-Hyun Seo, Hyunwoo Park, Dong-Kyu Kim, Seung-Young Kho

Abstract:

The purpose of this study is providing an improved mode choice model considering parameters including age grouping of prime-aged and old age. In this study, 2010 Household Travel Survey data were used and improper samples were removed through the analysis. Chosen alternative, date of birth, mode, origin code, destination code, departure time, and arrival time are considered from Household Travel Survey. By preprocessing data, travel time, travel cost, mode, and ratio of people aged 45 to 55 years, 55 to 65 years and over 65 years were calculated. After the manipulation, the mode choice model was constructed using LIMDEP by maximum likelihood estimation. A significance test was conducted for nine parameters, three age groups for three modes. Then the test was conducted again for the mode choice model with significant parameters, travel cost variable and travel time variable. As a result of the model estimation, as the age increases, the preference for the car decreases and the preference for the bus increases. This study is meaningful in that the individual and households characteristics are applied to the aggregate model.

Keywords: age grouping, aging, mode choice model, multinomial logit model

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Authors: Anyimc, C. J. Aneke, J. O. Orji, O. Nworie, U. C. C. Egbule

Abstract:

The microbial examination and antimicrobial susceptibility profile of microorganism isolated from the salt mining site in Ebonyi state were evaluated in the present study using a standard microbiological technique. A total of 300 samples were randomly collected in three sample groups (A, B, and C) of 100 each. Isolation, Identification and characterization of organization present on the soil samples were determined by culturing, gram-staining and biochemical technique. The result showed the following organisms were isolated with their frequency as follow: Bacillus species (37.3%) and Staphylococcus species(23.5%) had the highest frequency in the whole Sample group A and B while Klebsiella specie (15.7%), Pseudomonas species(13.7%), and Erwinia species (9.8%) had the least. Rhizopus species (42.0%) and Aspergillus species (26.0%) were the highest fungi isolated, followed by Penicillum species (20.0%) while Mucor species (4.0%), and Fusarium species (8.0%) recorded the least. Sample group C showed high microbial population of all the microbial isolates when compared to sample group A and B. Disc diffusion method was used to determine the susceptibility of isolated bacteria to various antibiotics (oxfloxacin, pefloxacin, ciprorex, augumentin, gentamycin, ciproflox, septrin, ampicillin), while agar well diffusion method was used to determine the susceptibility of isolated fungi to some antifungal drugs (metronidazole, ketoconazole, itraconazole fluconazole). The antibacterial activity of the antibiotics used showed that ciproflux has the best inhibitory effect on all the test bacteria. Ketoconazole showed the highest inhibitory effect on the fungal isolates, followed by itraconazole, while metronidazole and fluconazole showed the least inhibitory effect on the entire test fungal isolates. Hence, the multiple drug resistance of most isolates to appropriate drugs of choice are of great public health concern and cells for periodic monitoring of antibiograms to detect possible changing patterns. Microbes isolated in the salt mining site can also be used as a source of gene(s) that can increase salt tolerance in different crop species through genetic engineering.

Keywords: microorganisms, antibacterial, antifungal, resistance, salt mining site, Ebonyi State

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