Search results for: predictive distribution

Authors: Akiyoshi Wakatani

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This paper describes the Message Passing Interface (MPI) implementation of ADETRAN language, and its evaluation on SX-ACE supercomputers. ADETRAN language includes pdo statement that specifies the data distribution and parallel computations and pass statement that specifies the redistribution of arrays. Two methods for implementation of pass statement are discussed and the performance evaluation using Splitting-Up CG method is presented. The effectiveness of the parallelization is evaluated and the advantage of one dimensional distribution is empirically confirmed by using the results of experiments.

Keywords: iterative methods, array redistribution, translator, distributed memory

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Authors: Olga Tkacheva, Pavel Arkhipov, Alexey Rudenko, Yurii Zaikov

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The aluminum electrolysis process in the conventional cryolite-alumina electrolyte with cryolite ratio of 2.7 was carried out at an initial temperature of 970 °C and the anode current density of 0.5 A/cm² in a 15A lab-scale cell in order to study the formation of the side ledge during electrolysis and the alumina distribution between electrolyte and side ledge. The alumina contained 35.97% α-phase and 64.03% γ-phase with the particles size in the range of 10-120 μm. The cryolite ratio and the alumina concentration were determined in molten electrolyte during electrolysis and in frozen bath after electrolysis. The side ledge in the electrolysis cell was formed only by the 13^th hour of electrolysis. With a slight temperature decrease a significant increase in the side ledge thickness was observed. The basic components of the side ledge obtained by the XRD phase analysis were Na₃AlF₆, Na₅Al₃F₁₄, Al₂O₃, and NaF^.5CaF₂^.AlF₃. As in the industrial cell, the increased alumina concentration in the side ledge formed on the cell walls and at the ledge-electrolyte-aluminum three-phase boundary during aluminum electrolysis in the lab cell was found (FTP No 05.604.21.0239, IN RFMEFI60419X0239).

Keywords: alumina distribution, aluminum electrolyzer, cryolie-alumina electrolyte, side ledge

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Authors: Javier Sandoval Bustamante, Pardis Sheikhzadeh, Vijayanarasimha Hindupur Pakka

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In the pursuit of achieving net-zero carbon emissions, the integration of low carbon technologies into electricity distribution networks is paramount. This paper delves into the critical assessment of how the integration of low carbon technologies, such as heat pumps, electric vehicle chargers, and photovoltaic systems, impacts the infrastructure and operation of electricity distribution networks. The study employs rigorous methodologies, including power flow analysis and headroom analysis, to evaluate the feasibility and implications of integrating these technologies into existing distribution systems. Furthermore, the research utilizes Local Area Energy Planning (LAEP) methodologies to guide local authorities and distribution network operators in formulating effective plans to meet regional and national decarbonization objectives. Geospatial analysis techniques, coupled with building physics and electric energy systems modeling, are employed to develop geographic datasets aimed at informing the deployment of low carbon technologies at the local level. Drawing upon insights from the Local Energy Net Zero Accelerator (LENZA) project, a comprehensive case study illustrates the practical application of these methodologies in assessing the rollout potential of LCTs. The findings not only shed light on the technical feasibility of integrating low carbon technologies but also provide valuable insights into the broader transition towards a sustainable and electrified energy future. This paper contributes to the advancement of knowledge in power electrical engineering by providing empirical evidence and methodologies to support the integration of low carbon technologies into electricity distribution networks. The insights gained are instrumental for policymakers, utility companies, and stakeholders involved in navigating the complex challenges of energy transition and achieving long-term sustainability goals.

Keywords: energy planning, energy systems, digital twins, power flow analysis, headroom analysis

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Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

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Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

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Authors: Timothy Kayode Samson, Adedoyin Isola Lawal

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The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution.

Keywords: skewed generalized error distribution, skewed normal distribution, skewed student t- distribution, APARCH, EGARCH, sGARCH, GJR-GARCH

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Authors: Bernd Engel, Hassan Raheem Hassan

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Rotary draw bending is a method used for tube forming. During the tube bending process, the neutral axis moves towards the inner arc and the wall thickness changes in the cross section of the tube. Wall thinning of the tube takes place at the extrados, whereas wall thickening of the tube occurs at the intrados. This paper investigates the tube bending with rotary draw bending process using thick-walled tubes and different material properties (16Mo3 and 10CrMo9-10). The experimental tests and finite element simulations are used to calculate the variable characteristics (wall thickness distribution, neutral axis shifting and longitudinal strain distribution). These results are compared with results of a plasto-mechanical model. Moreover, the cross section distortion is investigated in this study. This study helped to get bends with smaller wall factor for different material properties.

Keywords: rotary draw bending, thick wall tube, material properties, material influence

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Authors: Demet Ozgur-Unluakin, Busenur Turkali, Ayse Karacaorenli

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Maintenance strategies can be classified into two types, which are reactive and proactive, with respect to the time of the failure and maintenance. If the maintenance activity is done after a breakdown, it is called reactive maintenance. On the other hand, proactive maintenance, which is further divided as preventive and predictive, focuses on maintaining components before a failure occurs to prevent expensive halts. Recently, the number of interacting components in a system has increased rapidly and therefore, the structure of the systems have become more complex. This situation has made it difficult to provide the right maintenance decisions. Herewith, determining effective decisions has played a significant role. In multi-component systems, many methodologies and strategies can be applied when a component or a system has already broken down or when it is desired to identify and avoid proactively defects that could lead to future failure. This study focuses on the comparison of various maintenance strategies on a multi-component dynamic system. Components in the system are hidden, although there exists partial observability to the decision maker and they deteriorate in time. Several predefined policies under corrective, preventive and predictive maintenance strategies are considered to minimize the total maintenance cost in a planning horizon. The policies are simulated via Dynamic Bayesian Networks on a multi-component system with different policy parameters and cost scenarios, and their performances are evaluated. Results show that when the difference between the corrective and proactive maintenance cost is low, none of the proactive maintenance policies is significantly better than the corrective maintenance. However, when the difference is increased, at least one policy parameter for each proactive maintenance strategy gives significantly lower cost than the corrective maintenance.

Keywords: decision making, dynamic Bayesian networks, maintenance, multi-component systems, reliability

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: Bernardo C. P. Albuquerque, Darym J. F. Campos

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Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management.

Keywords: statistical slope stability analysis, skew distributions, probability of failure, functions of random variables

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Amir Reza Radmanesh, Sina Farajzadeh Khosroshahi, Hani Sadr

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The transient catalytic converter performance is governed by complex interactions between exhaust gas flow and the monolithic structure of the catalytic converter. Stringent emission regulations around the world necessitate the use of highly-efficient catalytic converters in vehicle exhaust systems. Computational fluid dynamics (CFD) is a powerful tool for calculating the flow field inside the catalytic converter. Radial velocity profiles, obtained by a commercial CFD code, present very good agreement with respective experimental results published in the literature. However the applicability of CFD for transient simulations is limited by the high CPU demands. In the present work, Geometric modeling ceramic monolith substrate is done with square shaped channel type of Catalytic converter and it is coated platinum and palladium. This example illustrates the effect of flow distribution on thermal response of a catalytic converter and different gas flow velocities, during the critical phase of catalytic converter warm up.

Keywords: catalytic converter, computational fluid dynamic, porous media, velocity distribution

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Authors: M. M. Bashandy, A. R. Ahmed, M. El-Gaffary, Sahar S. Abd El-Rahman

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This study evaluated the acute toxicity and tissue distribution of intravenously administered gold nanoparticles (AuNPs) in male rabbits. Rabbits were exposed to single dose of AuNPs (300 µg/ kg). Toxic effects were assessed via general behavior, hematological parameters, serum biochemical parameters and histopathological examination of various rabbits’ organs. Tissue distribution of AuNPs was evaluated at a dose of 300 µg/ kg in male rabbit. Inductively coupled plasma–mass spectrometry (ICP-MS) was used to determine gold concentrations in tissue samples collected at predetermined time intervals. After one week, AuNPs exerted no obvious acute toxicity in rabbits. However, inflammatory reactions in lung and liver cells were induced in rabbits treated at the300 µg/ kg dose level. The highest gold levels were found in the spleen, followed by liver, lungs and kidneys. These results indicated that AuNPs could be distributed extensively to various tissues in the body, but primarily in the spleen and liver.

Keywords: gold nanoparticles, toxicity, pathology, hematology, liver function, kidney function

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Authors: Cecile Edel, Sandrine Giscard D'Estaing, Elsa Labrune, Jacqueline Lornage, Mehdi Benchaib

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Introduction: The use of incubator equipped with time-lapse technology in Artificial Reproductive Technology (ART) allows a continuous surveillance. With morphocinetic parameters, algorithms are available to predict the potential outcome of an embryo. However, the different proposed time-lapse algorithms do not take account the missing data, and then some embryos could not be classified. The aim of this work is to construct a predictive model even in the case of missing data. Materials and methods: Patients: A retrospective study was performed, in biology laboratory of reproduction at the hospital ‘Femme Mère Enfant’ (Lyon, France) between 1 May 2013 and 30 April 2015. Embryos (n= 557) obtained from couples (n=108) were cultured in a time-lapse incubator (Embryoscope®, Vitrolife, Goteborg, Sweden). Time-lapse incubator: The morphocinetic parameters obtained during the three first days of embryo life were used to build the predictive model. Predictive model: A quadratic regression was performed between the number of cells and time. N = a. T² + b. T + c. N: number of cells at T time (T in hours). The regression coefficients were calculated with Excel software (Microsoft, Redmond, WA, USA), a program with Visual Basic for Application (VBA) (Microsoft) was written for this purpose. The quadratic equation was used to find a value that allows to predict the blastocyst formation: the synthetize value. The area under the curve (AUC) obtained from the ROC curve was used to appreciate the performance of the regression coefficients and the synthetize value. A cut-off value has been calculated for each regression coefficient and for the synthetize value to obtain two groups where the difference of blastocyst formation rate according to the cut-off values was maximal. The data were analyzed with SPSS (IBM, Il, Chicago, USA). Results: Among the 557 embryos, 79.7% had reached the blastocyst stage. The synthetize value corresponds to the value calculated with time value equal to 99, the highest AUC was then obtained. The AUC for regression coefficient ‘a’ was 0.648 (p < 0.001), 0.363 (p < 0.001) for the regression coefficient ‘b’, 0.633 (p < 0.001) for the regression coefficient ‘c’, and 0.659 (p < 0.001) for the synthetize value. The results are presented as follow: blastocyst formation rate under cut-off value versus blastocyst rate formation above cut-off value. For the regression coefficient ‘a’ the optimum cut-off value was -1.14.10-3 (61.3% versus 84.3%, p < 0.001), 0.26 for the regression coefficient ‘b’ (83.9% versus 63.1%, p < 0.001), -4.4 for the regression coefficient ‘c’ (62.2% versus 83.1%, p < 0.001) and 8.89 for the synthetize value (58.6% versus 85.0%, p < 0.001). Conclusion: This quadratic regression allows to predict the outcome of an embryo even in case of missing data. Three regression coefficients and a synthetize value could represent the identity card of an embryo. ‘a’ regression coefficient represents the acceleration of cells division, ‘b’ regression coefficient represents the speed of cell division. We could hypothesize that ‘c’ regression coefficient could represent the intrinsic potential of an embryo. This intrinsic potential could be dependent from oocyte originating the embryo. These hypotheses should be confirmed by studies analyzing relationship between regression coefficients and ART parameters.

Keywords: ART procedure, blastocyst formation, time-lapse incubator, quadratic model

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Authors: M. G. M. Khan, V. D. Prasad, D. K. Rao

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In this manuscript, we discuss the problem of determining the optimum stratification of a study (or main) variable based on the auxiliary variable that follows a uniform distribution. If the stratification of survey variable is made using the auxiliary variable it may lead to substantial gains in precision of the estimates. This problem is formulated as a Nonlinear Programming Problem (NLPP), which turn out to multistage decision problem and is solved using dynamic programming technique.

Keywords: auxiliary variable, dynamic programming technique, nonlinear programming problem, optimum stratification, uniform distribution

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

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Authors: Dimpi A. Patel, Pankaj Raina, Ramesh Chinnasamy, Sunetro Ghosal

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The herpetological fauna of Ladakh has been surveyed few times till 1999. In 2019, a rapid survey to document current herpetofaunal composition was undertaken in which a total of 6 species belonging to 2 orders and five families along with their altitudinal ranges were recorded. We present a revised checklist of reptiles found in Ladakh trans Himalayas based on historical records and recent field surveys. Records for erroneously reported species in literature are discussed and recommended for removal from the list from this region. For several species, new elevation range records have been recorded. This paper contributes to the present status of the richness of reptiles and amphibians in the region by documenting the composition and ecological distribution of the herpetofauna of unstudied sites. Species-specific temperature and humidity regimes were also recorded during the survey periods. Our study creates baseline information for future ecological and behavioral studies on the herpetofauna of the region by providing habitat preferences and distribution in detail.

Keywords: amphibians, distribution, diversity, reptiles, trans-Himalaya

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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: Mohsen Mazidi, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Mohamamd Rastegar

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To tackle the air pollution issues, Plug-in Hybrid Electric Vehicles (PHEVs) are proposed as an appropriate solution. Charging a large amount of PHEV batteries, if not controlled, would have negative impacts on the distribution system. The control process of charging of these vehicles can be centralized in parking lots that may provide a chance for better coordination than the individual charging in houses. In this paper, an optimization-based approach is proposed to determine the optimum PHEV parking capacities in candidate nodes of the distribution system. In so doing, a profile for charging and discharging of PHEVs is developed in order to flatten the network load profile. Then, this profile is used in solving an optimization problem to minimize the distribution system losses. The outputs of the proposed method are the proper place for PHEV parking lots and optimum capacity for each parking. The application of the proposed method on the IEEE-34 node test feeder verifies the effectiveness of the method.

Keywords: loss, plug-in hybrid electric vehicle (PHEV), PHEV parking lot, V2G

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Authors: Andrew Kanzunguze, Lusayo Mwabumba, Jason K. Gilbertson, Dominic B. Gondwe, George Z. Nxumayo

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Knowledge about the spatio-temporal distribution of invasive plants in protected areas provides a base from which hypotheses explaining proliferation of plant invasions can be made alongside development of relevant invasive plant monitoring programs. The aim of this study was to investigate the spatio-temporal distribution of bracken fern on the grassland plateau of Nyika National Park over the past 30 years (1986-2016) as well as to determine the current extent of the invasion. Remote sensing, machine learning, and statistical modelling techniques (object-based image analysis, image classification and linear regression analysis) in geographical information systems were used to determine both the spatial and temporal distribution of bracken fern in the study area. Results have revealed that bracken fern has been increasing coverage on the Nyika plateau at an estimated annual rate of 87.3 hectares since 1986. This translates to an estimated net increase of 2,573.1 hectares, which was recorded from 1,788.1 hectares (1986) to 4,361.9 hectares (2016). As of 2017 bracken fern covered 20,940.7 hectares, approximately 14.3% of the entire grassland plateau. Additionally, it was observed that the fern was distributed most densely around Chelinda camp (on the central plateau) as well as in forest verges and roadsides across the plateau. Based on these results it is recommended that Ecological Niche Modelling approaches be employed to (i) isolate the most important factors influencing bracken fern proliferation as well as (ii) identify and prioritize areas requiring immediate control interventions so as to minimize bracken fern proliferation in Nyika National Park.

Keywords: bracken fern, image classification, Landsat-8, Nyika National Park, spatio-temporal distribution

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Authors: Ling Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

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The installation of photovoltaic based distributed generation (PVDG) in active distribution system can lead to voltage fluctuation due to the intermittent and unpredictable PVDG output power. This paper presented a method in mitigating the voltage rise by optimally locating and sizing the battery energy storage system (BESS) in PVDG integrated distribution network. The improved firefly algorithm is used to perform optimal placement and sizing. Three objective functions are presented considering the voltage deviation and BESS off-time with state of charge as the constraint. The performance of the proposed method is compared with another optimization method such as the original firefly algorithm and gravitational search algorithm. Simulation results show that the proposed optimum BESS location and size improve the voltage stability.

Keywords: BESS, firefly algorithm, PVDG, voltage fluctuation

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Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

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Authors: Pholosho M. Kgopa, Phatu W. Mashela

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Due to high incidents of marginal land in Limpopo Province, South Africa, and increasing demand for arable land, small-holder farmers tend to continuously cultivate the same fields and at the same time, applying fertilisers to improve yields for meeting local food security. These practices might have an impact on the distribution of trace and essential elements. Therefore, the objective of this investigation was to assess the distribution of essential elements and trace metals in a continuously cultivated and fertilised field, at the University of Limpopo Experimental Farm. Three fields, 3 ha each were identified as continuously cultivated (CC), moderately cultivated (MC) and virgin fields (VF). Each field was divided into 12 equal grids of 50 m × 50 m for sampling. A soil profile was opened in each grid, where soil samples were collected from 0-20; 20-40 and 40-60; 60-80 and 80-100 cm depths for analysis. Samples were analysed for soil texture, pH, electrical conductivity, organic matter content, selected essential elements (Ca, P and Mg), Na and trace elements (Cu, Fe, Ni, and Zn). Results suggested that most of the variables were vertically different, with high concentrations of the test elements except for magnesium. Soil pH in depth 0-20 cm was high (6.44) in CC when compared to that in VF (5.29), but lower than that of MC (7.84). There were no distinctive vertical trends of the variables, except for Mg, Na, and K which displayed a declining trend at 40-60 cm depth when compared to the 0-20 cm depth. Concentrations of Fe, Cu, Zn, and Ni were generally low which might be due to their indirect relationship with soil pH. Continuous cultivation and fertilisation altered soil chemical properties; which could explain the unproductivity of such fields.

Keywords: over-cultivation, soil chemical properties, vertical distribution, spatial distribution

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Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

Abstract:

Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

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Authors: Jamal Hussain Al-Smail

Abstract:

Hydrogen fuel cells (HFCs) are environmentally friendly, energy converter devices that convert the chemical energy of the reactants (oxygen and hydrogen) to electricity through electrochemical reactions. The level of the electricity production of HFCs mainly increases depending on the oxygen distribution in the HFC’s cathode gas diffusion layer (GDL). With a constant porosity of the GDL, the electrochemical reaction can have a great variation that reduces the cell’s productivity and stability. Our findings bring a methodology in finding porosity designs of the diffusion layer to improve the oxygen distribution such that it results in a stable oxygen-hydrogen reaction. We first introduce a mathematical model involving the mass and momentum transport equations, in which a porosity function of the GDL is incorporated as a control for the fluid flow. We then derive numerical methods for solving the mathematical model. In conclusion, we present our numerical results to show how to design the GDL porosity to result in a uniform oxygen distribution.

Keywords: fuel cells, material porosity design, mathematical modeling, porous media

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Authors: Anthony Nyamekeh-Armah Adjei, Toshiaki Aoki

Abstract:

The unreliable and poor efficiency of electricity distribution leading to frequent power outages and high losses are the major challenge facing the power distribution sector in Ghana. Distribution system routes electricity from the power generating station at a higher voltage through the transmission grid and steps it down through the low voltage lines to end users. Approximately all electricity problems and disturbances that have increased the call for renewable and sustainable energy in recent years have their roots in the distribution system. Therefore, sustaining electricity distribution efficiency can potentially contribute to the reserve of natural energy resources use in power generation, reducing greenhouse gas emission (GHG), decreasing tariffs for consumers and effective human security. Human Security is a people-centered approach where individual human being is the principal object of concern, focuses on protecting the vital core of all human lives in ways for meeting basic needs that enhance the safety and protection of individuals and communities. The vulnerability is the diminished capacity of an individual or group to anticipate, resist and recover from the effect of natural, human-induced disaster. The research objectives are to explore the causes of frequent power outages to consumers, high losses in the distribution network and the effect of poor electricity distribution efficiency on the vulnerable (poor and ordinary) people that mostly depend on electricity for their daily activities or life to survive. The importance of the study is that in a developing country like Ghana where raising a capital for new infrastructure project is difficult, it would be beneficial to enhance the efficiency that will significantly minimize the high energy losses, reduce power outage, to ensure safe and reliable delivery of electric power to consumers to secure the security of people’s livelihood. The methodology used in this study is both interview and questionnaire survey to analyze the response from the respondents on causes of power outages and high losses facing the electricity company of Ghana (ECG) and its effect on the livelihood on the vulnerable people. Among the outcome of both administered questionnaire and the interview survey from the field were; poor maintenance of existing sub-stations, use of aging equipment, use of poor distribution infrastructure and poor metering and billing system. The main observation of this paper is that the poor network efficiency (high losses and power outages) affects the livelihood of the vulnerable people. Therefore, the paper recommends that policymakers should insist on all regulation guiding electricity distribution to improve system efficiency. In conclusion, there should be decentralization of off-grid solar PV technologies to provide a sustainable and cost-effective, which can increase daily productivity and improve the quality of life of the vulnerable people in the rural communities.

Keywords: electricity efficiency, high losses, human security, power outage

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Authors: Yao Song

Abstract:

Objectives: We aimed to explore the clinicodemographic characteristics and prognosis of grey zone squamous cell cancer (GZSCC) located in the overlapping or ambiguous area of the oral cavity and oropharynx and to identify valuable factors that would improve its differential diagnosis and prognosis. Methods: Information of GZSCC patients in the Surveillance, Epidemiology, and End Results (SEER) database was compared to patients with an oral cavity (OCSCC) and oropharyngeal (OPSCC) squamous cell carcinomas with corresponding HPV status, respectively. Kaplan-Meier method with log-rank test and multivariate Cox regression analysis were applied to assess associations between clinical characteristics and overall survival (OS). A predictive model integrating age, gender, marital status, HPV status, and staging variables was conducted to classify GZSCC patients into three risk groups and verified internally by 10-fold cross validation. Results: A total of 3318 GZSCC, 10792 OPSCC, and 6656 OCSCC patients were identified. HPV-positive GZSCC patients had the best 5-year OS as HPV-positive OPSCC (81% vs. 82%). However, the 5-year OS of HPV-negative/unknown GZSCC (43%/42%) was the worst among all groups, indicating that HPV status and the overlapping nature of tumors were valuable prognostic predictors in GZSCC patients. Compared with the strategy of dividing GZSCC into two groups by HPV status, the predictive model integrating more variables could additionally identify a unique high-risk GZSCC group with the lowest OS rate. Conclusions: GZSCC patients had distinct clinical characteristics and prognoses compared with OPSCC and OCSCC; integrating HPV status and other clinical factors could help distinguish GZSCC and predict their prognosis.

Keywords: GZSCC, OCSCC, OPSCC, HPV

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Authors: K. P. Gogoi

Abstract:

The current research study investigates the motivational pattern viz Power motivation among the tribal college students of Assam. The sample consisted of 240 college students (120 tribal and 120 non-tribal) ranging from 18-24 years, 60 males and 60 females for both tribal’s and non-tribal’s. Attempts were made to include all the prominent tribes of Assam viz. Thematic Apperception Test, Power motive Scale and a semi structured interview schedule were used to gather information about their family types, parental deprivation, parental relations, social and political belongingness. Mean, Standard Deviation, and t-test were the statistical measures adopted in this 2x2 factorial design study. In addition to this discriminant analysis has been worked out to strengthen the predictive validity of the obtained data. TAT scores reveal significant difference between the tribal’s and non-tribal on power motivation. However results obtained on gender difference indicates similar scores among both the cultures. Cross validation of the TAT results was done by using the power motive scale by T. S. Dapola which confirms the results on need for power through TAT scores. Power motivation has been studied in three directions i.e. coercion, inducement and restraint. An interesting finding is that on coercion tribal’s score high showing significant difference whereas in inducement or seduction the non-tribal’s scored high showing significant difference. On the other hand on restraint no difference exists between both cultures. Discriminant analysis has been worked out between the variables n-power, coercion, inducement and restraint. Results indicated that inducement or seduction (.502) is the dependent measure which has the most discriminating power between these two cultures.

Keywords: power motivation, tribal, social, political, predictive validity, cross validation, coercion, inducement, restraint

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

Abstract:

Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Bhavini Pandya

Abstract:

This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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