Search results for: data encoding

Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Leila Noori, Rosario Barone, Francesca Rappa, Antonella Marino Gammazza, Alessandra Maria Vitale, Giuseppe Donato Mangano, Giusy Sentiero, Filippo Macaluso, Kathryn H. Myburgh, Francesco Cappello, Federica Scalia

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The neuromuscular system controls, directs, and allows movement of the body through the action of neural circuits, which include motor neurons, sensory neurons, and skeletal muscle fibers. Protein homeostasis of the involved cytotypes appears crucial to maintain the correct and prolonged functions of the neuromuscular system, and both neuronal cells and skeletal muscle fibers express significant quantities of protein chaperones, the molecular machinery responsible to maintain the protein turnover. Genetic mutations or defective post-translational modifications of molecular chaperones (i.e., genetic or acquired chaperonopathies) may lead to neuromuscular disorders called as neurochaperonopathies. The limited knowledge of the effects of the defective chaperones on skeletal muscle fibers and neurons impedes the progression of therapeutic approaches. A distinct genetic variation of CCT5 gene encoding for the subunit 5 of the chaperonin CCT (Chaperonin Containing TCP1; also known as TRiC, TCP1 Ring Complex) was recently described associated with severe distal motor neuropathy by our team. In this study, we investigated the histopathological abnormalities of the skeletal muscle biopsy of the pediatric patient affected by the mutation Leu224Val in the CCT5 subunit. We provide molecular and structural features of the diseased skeletal muscle tissue that we believe may be useful to identify undiagnosed cases of this rare genetic disorder. We investigated the histological abnormalities of the affected tissue via hematoxylin and eosin staining. Then we used immunofluorescence and qPCR techniques to explore the expression and distribution of CCT5 in diseased and healthy skeletal muscle tissue. Immunofluorescence and immunohistochemistry assays were performed to study the sarcomeric and structural proteins of skeletal muscle, including actin, myosin, tubulin, troponin-T, telethonin, and titin. We performed Western blot to examine the protein expression of CCT5 and some heat shock proteins, Hsp90, Hsp60, Hsp27, and α-B crystallin, along with the main client proteins of the CCT5, actin, and tubulin. Our findings revealed muscular atrophy, abnormal morphology, and different sizes of muscle fibers in affected tissue. The swollen nuclei and wide interfiber spaces were seen. Expression of CCT5 had been decreased and showed a different distribution pattern in the affected tissue. Altered expression, distribution, and bandage pattern were detected by confocal microscopy for the interested muscular proteins in tissue from the patient compared to the healthy control. Protein levels of the studied Hsps normally located at the Z-disk were reduced. Western blot results showed increased levels of the actin and tubulin proteins in the diseased skeletal muscle biopsy compared to healthy tissue. Chaperones must be expressed at high levels in skeletal muscle to counteract various stressors such as mechanical, oxidative, and thermal crises; therefore, it seems relevant that defects of molecular chaperones may result in damaged skeletal muscle fibers. So far, several chaperones or cochaperones involved in neuromuscular disorders have been defined. Our study shows that alteration of the CCT5 subunit is associated with the damaged structure of skeletal muscle fibers and alterations of chaperone system components and paves the way to explore possible alternative substrates of chaperonin CCT. However, further studies are underway to investigate the CCT mechanisms of action to design applicable therapeutic strategies.

Keywords: molecular chaperones, neurochaperonopathy, neuromuscular system, protein homeostasis

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Ali A. Abdollahi

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During the last several decades, scientists have consistently applied Multiple Criteria Decision-Making methods in making decisions about multi-faceted, complicated subjects. While making such decisions and in order to achieve more accurate evaluations, they have regularly used a variety of criteria instead of applying just one Optimum Evaluation Criterion. The method presented here utilizes both ‘quantity’ and ‘quality’ to assess the function of the Multiple-Criteria method. Applying Data envelopment analysis (DEA), weighted aggregated sum product assessment (WASPAS), Weighted Sum Approach (WSA), Analytic Network Process (ANP), and Charnes, Cooper, Rhodes (CCR) methods, we have analyzed thirteen parks in Kerman city. It further indicates that the functions of WASPAS and WSA are compatible with each other, but also that their deviation from DEA is extensive. Finally, the results for the CCR technique do not match the results of the DEA technique. Our study indicates that the ANP method, with the average rate of 1/51, ranks closest to the DEA method, which has an average rate of 1/49.

Keywords: multiple criteria decision making, Data envelopment analysis (DEA), Charnes Cooper Rhodes (CCR), Weighted Sum Approach (WSA)

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Authors: Mayada Attia Ibrahim

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Evaluating and developing the electroplating production process is a key challenge in this type of process. The process is influenced by several factors such as process parameters, process costs, and production environments. Analyzing and optimizing all these factors together requires extensive analytical techniques that are not available in real-case industrial entities. This paper presents a practice-based framework for the evaluation and optimization of some of the crucial factors that affect the costs and production times associated with this type of process, energy costs, material costs, and product flow times. The proposed approach uses Design of Experiments, Discrete-Event Simulation, and Theory of Constraints were respectively used to identify the most significant factors affecting the production process and simulate a real production line to recognize the effect of these factors and assign possible bottlenecks. Several scenarios are generated as corrective strategies for improving the production line. Following that, data envelopment analysis CCR input-oriented DEA model is used to evaluate and optimize the suggested scenarios.

Keywords: electroplating process, simulation, design of experiment, performance optimization, theory of constraints, data envelopment analysis

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Nesrine Gaaliche

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This study provides a finite element dynamic model for analyzing rolling bearing system vibration response. The vibration responses of polypropylene bearings with and without defects are studied using FE analysis and compared to experimental data. The viscoelastic behavior of thermoplastic is investigated in this work to evaluate the influence of material flexibility and damping viscosity. The vibrations are detected using 3D dynamic analysis. Peak vibrations are more noticeable in an inner ring defect than in an outer ring defect, according to test data. The performance of thermoplastic bearings is compared to that of metal parts using vibration signals. Both the test and numerical results show that Polypropylene bearings exhibit less vibration than steel counterparts. Unlike bearings made from metal, polypropylene bearings absorb vibrations and handle shaft misalignments. Following validation of the overall vibration spectrum data, Von Mises stresses inside the rings are assessed under high loads. Stress is significantly high under the balls, according to the simulation findings. For the test cases, the computational findings correspond closely to the experimental results.

Keywords: viscoelastic, FE analysis, polypropylene, bearings

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Authors: Sarah Ryu

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Agricultural methods and ecological approaches employed by the pre-colonial Mayans may provide valuable insights into forest management and viable alternatives for resource sustainability in the face of major deforestation across Central and South America.Using a combination of observation data collected from the modern indigenous inhabitants near Mixco in Guatemala and historical data, this study was able to create a holistic picture of how the Maya maintained their ecosystems. Surveys and observations were conducted in the field, over a period of twelve weeks across two years. Geographic and archaeological data for this area was provided by Guatemalan organizations such as the Universidad de San Carlos de Guatemala. Observations of current indigenous populations around Mixco showed that they adhered to traditional Mayan methods of agriculture, such as terrace construction and arboriculture. Rather than planting one cash crop as was done by the Spanish, indigenous peoples practice agroforestry, cultivating forests that would provide trees for construction material, wild plant foods, habitat for game, and medicinal herbs. The emphasis on biodiversity prevented deforestation and created a sustainable balance between human consumption and forest regrowth. Historical data provided by MayaSim showed that the Mayans successfully maintained their ecosystems from about 800BCE to 700CE. When the Mayans practiced natural resource conservation and cultivated a harmonious relationship with the forest around them, they were able to thrive and prosper alongside nature. Having lasted over a thousand years, the Mayan empire provides a valuable lesson in sustainability and human attitudes towards the environment.

Keywords: biodiversity, forestry, mayan, sustainability

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: M. A. Wanzala, L. A. Ogallo, F. J. Opijah, J. N. Mutemi

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The usefulness and limitations in climate information are due to uncertainty inherent in the climate system. For any given region to have sustainable development it is important to apply climate information into its socio-economic strategic plans. The overall objective of the study was to assess the performance of the Coupled Model Inter-comparison Project (CMIP5) over the Lake Victoria Basin. The datasets used included the observed point station data, gridded rainfall data from Climate Research Unit (CRU) and hindcast data from eight CMIP5. The methodology included trend analysis, spatial analysis, correlation analysis, Principal Component Analysis (PCA) regression analysis, and categorical statistical skill score. Analysis of the trends in the observed rainfall records indicated an increase in rainfall variability both in space and time for all the seasons. The spatial patterns of the individual models output from the models of MPI, MIROC, EC-EARTH and CNRM were closest to the observed rainfall patterns.

Keywords: categorical statistics, coupled model inter-comparison project, principal component analysis, statistical downscaling

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Authors: Kylie Leonard

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Investment in artificial intelligence (AI) can be an attractive opportunity for business leaders as there are many easy-to-see benefits. These benefits include task completion rates, overall cost, and better forecasting. Business leaders are often unaware of the challenges that can accompany AI, such as data center costs, access to data, employee acceptance, and privacy concerns. In addition to the benefits and challenges of AI, it is important to practice AI ethics to ensure the safe creation of AI. AI ethics include aspects of algorithm bias, limits in transparency, and surveillance. To be a good business leader, it is critical to address all the considerations involving the challenges of AI and AI ethics.

Keywords: artificial intelligence, artificial intelligence ethics, business leaders, business concerns

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Authors: Amy Gooden

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Various modes of consent have been utilized in health research, but open consent has not been explored in South Africa’s AI research context. Open consent entails the sharing of data without assurances of privacy and may be seen as an attempt to marry open science with informed consent. Because all potential uses of data are unknown, it has been questioned whether consent can be informed. Instead of trying to adapt existing modes of consent, why not adopt a new perspective? This is what open consent proposes and what this research will explore in AI health research in South Africa.

Keywords: artificial intelligence, consent, health, law, research, South Africa

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Authors: Odai Ibrahim Mohammed Al Balasmeh, Tapas Karmaker, Richa Babbar

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In this study, the reservoir capacity and sediment deposition were estimated using remote sensing data. The satellite images were synchronized with water level and storage capacity to find out the change in sediment deposition due to soil erosion and transport by streamflow. The water bodies spread area was estimated using vegetation indices, e.g., normalize differences vegetation index (NDVI) and normalize differences water index (NDWI). The 3D reservoir bathymetry was modeled by integrated water level, storage capacity, and area. From the models of different time span, the change in reservoir storage capacity was estimated. Another reservoir with known water level, storage capacity, area, and sediment deposition was used to validate the estimation technique. The t-test was used to assess the results between observed and estimated reservoir capacity and sediment deposition.

Keywords: satellite data, normalize differences vegetation index, NDVI, normalize differences water index, NDWI, reservoir capacity, sedimentation, t-test hypothesis

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Authors: Artur Siqueira Nóbrega de Freitas

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The Autonomous Underwater Vehicles (AUV), as well the Remotely Operated Vehicles (ROV), are unmanned technologies used in oceanographic investigations, offshore oil extraction, military applications, among others. Differently from AUVs, ROVs uses a physical connection with the surface for energy supply e data traffic. The AUVs use batteries and embedded data acquisition systems. These technologies have progressed, supported by studies in the areas of robotics, embedded systems, naval engineering, etc. This work presents a methodology for external pressure vessel design, responsible for contain and keep the internal components of the vehicle, such as on-board electronics and sensors, isolated from contact with water, creating a pressure differential between the inner and external regions.

Keywords: vessel, external pressure, AUV, buckling

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Authors: Dagnew Dessie Mengie

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The purpose of this study was to investigate German tourists' demand for Ethiopian tourism destinations. The author has made every effort to identify the differences in the preferences of German visitors’ demand in Ethiopia comparing with Egypt, Kenya, Tanzania, and South African tourism sectors if they are invited to visit at the same time. However, the demand for international tourism for Ethiopia currently lags behind these African countries. Therefore, to offer demand-driven tourism products, the Ethiopian government and tour and travel operators need to understand the important factors that affect international tourists’ decision to visit Ethiopian tourist destinations. The aim of this study was to analyze German Tourists’ Demand for Ethiopian destinations. The researcher aimed to identify the demand for German tourists’ preference for Ethiopian tourist destinations compared to the above-mentioned African countries. For collecting and analysing data for this study, both quantitative and qualitative methods of research are being used in this study. The most significant data are collected by using the primary data collection method i.e. survey and interviews which are the most and large number of potential responses and feedback from nine German active tourists,12 Ethiopian tourism officials, four African embassies, and four well functioning private tour companies and secondary data collected from books, journals, previous research and electronic websites. Based on the data analysis of the information gathered from interviews and questionnaires, the study disclosed that the majority of German tourists do have not that high demand for Ethiopian Tourist destinations due to the following reasons: (1) Many Germans are fascinated by adventures and safari and simply want to lie on the beach and relax. These interests have leaded them to look for other African countries which have these accesses. (2) Uncomfortable infrastructure and transport problems are attributed to the decreasing number of German tourists in the country. (3) Inadequate marketing operation of the Ethiopian Tourism Authority and its delegates in advertising and clarifying the above irregularities which are raised by the tourists.

Keywords: environmental benefits of tourism, social benefits of tourism, economic benefits of tourism, political factors on tourism

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Authors: Naima Fatehi

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The paper reports on 107 medicinal plants, traditionally used in the South-West of Algeria (Bechar region). The information has been documented by interviewing traditional herbalists, various elderly men and women following different ethnobotanical methods. Ethnobotanical data was arranged alphabetically by botanical name, followed by family name, vernacular name, and part used. The present paper represents significant ethnobotanical information on medical plants used extensively in Bechar region for treating various diseases and provides baseline data for future pharmacological and phytochemical studies.

Keywords: medicinal plants, ethnobotanical survey, South-West Algeria, Bechar region

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Authors: Panasbodee Thachaopas, Duangdorm Gamnerdsap, Waraporn Inthip, Arissara Pungpa

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LDD’s IM Farm or Land Development Department’s Integrated Management Farm is the agricultural simulation application developed by Land Development Department relies on actual data in simulation game to grow 12 cash crops which are rice, corn, cassava, sugarcane, soybean, rubber tree, oil palm, pineapple, longan, rambutan, durian, and mangosteen. Launching in simulation game, players could select preferable areas for cropping from base map or Orthophoto map scale 1:4,000. Farm management is simulated from field preparation to harvesting. The system uses soil group, and present land use database to facilitate player to know whether what kind of crop is suitable to grow in each soil groups and integrate LDD’s data with other agencies which are soil types, soil properties, soil problems, climate, cultivation cost, fertilizer use, fertilizer price, socio-economic data, plant diseases, weed, pest, interest rate for taking on loan from Bank for Agriculture and Agricultural Cooperatives (BAAC), labor cost, market prices. These mentioned data affect the cost and yield differently to each crop. After completing, the player will know the yield, income and expense, profit/loss. The player could change to other crops that are more suitable to soil groups for optimal yields and profits.

Keywords: agricultural simulation, smart farmer, web application, factors of agricultural production

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Authors: Ahmed R. Abdelaziz, Zaki M. I. Osha

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Solar radiation data are of great significance for solar energy system design. This study aims at developing and calibrating new empirical models for estimating monthly mean daily global solar radiation on a horizontal surface in Alexandria, Egypt. Day length hours, sun height, day number, and declination angle calculated data are used for this purpose. A comparison between measured and calculated values of solar radiation is carried out. It is shown that all the proposed correlations are able to predict the global solar radiation with excellent accuracy in Alexandria.

Keywords: solar energy, global solar radiation, model, regression coefficient

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Authors: Mangala M. Pai, B. V. Murlimanju, Latha V. Prabhu, P. J. Jiji , Vandana Blossom

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Background:To study the morphological parameters of the axis vertebra in anatomical specimens. Methods: The present study was designed to obtain the morphometric data of axis vertebra. The superior and inferior articular facets of the axis were macroscopically observed for their shapes and the different parameters were measured using the digital Vernier caliper. It included 20 dried axis bones, which were obtained from the anatomy laboratory. Results: The morphometric data obtained in the present study are represented in the tables. The side wise comparison of the length and width of the articular facets of the axis vertebra were done. The present study observed that, there is no statistically significant difference observed among the parameters of right and left side articular facets (p>0.05). The superior and inferior articular facets were observed to have variable shapes. The frequencies of different shapes of superior and inferior articular facets are represented in figures. All the shapes of the inferior and superior articular facets were symmetrical over the right and left sides. Among the superior articular facets, the constrictions were absent in 13 cases (65%), 2 (10%) exhibited a single constriction, 3 (15%) had 2 constrictions and 2 (10%) were having 3 constrictions. The constrictions were absent in 11 (55%) of the inferior articular facets, 3 (15%) of them had 1 constriction, 3 (15%) were having 2 constrictions, 2 (10%) exhibited 3 constrictions and 1 (5%) of them had 4 constrictions. The constrictions of the inferior and superior articular facets were symmetrical over the right and left sides. Conclusion: We believe that the present study has provided additional information on the morphometric data of the axis vertebra. The data are important to the neurosurgeons, orthopedic surgeons and radiologists. The preoperative assessment of the axis vertebra may prevent dangerous complications like spinal cord and nerve root compression during the surgical intervention.

Keywords: axis, articular facet, morphology, morphometry

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Authors: Archit Yajnik

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Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: hidden markov model, natural language processing, POS tagging, viterbi algorithm

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Authors: Chantal Hendriks, Harry Gibson, Anna Trett, Penny Hancock, Catherine Moyes

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The use of modern inputs for crop protection, such as insecticides, is strongly underestimated in Sub-Saharan Africa. Several studies measured toxic concentrations of insecticides in fruits, vegetables and fish that were cultivated in Sub-Saharan Africa. The use of agricultural insecticides has impact on human and environmental health, but it also has the potential to impact on insecticide resistance in malaria transmitting mosquitos. To analyse associations between historic use of agricultural insecticides and the distribution of insecticide resistance through space and time, the use and environmental fate of agricultural insecticides needs to be mapped through the same time period. However, data on the use and environmental fate of agricultural insecticides in Africa are limited and therefore risk maps on the spatiotemporal occurrence of agricultural insecticides are created using environmental data. Environmental data on crop density and crop type were used to select the areas that most likely receive insecticides. These areas were verified by a literature review and expert knowledge. Pesticide fate models were compared to select most dominant processes that are involved in the environmental fate of insecticides and that can be mapped at a continental scale. The selected processes include: surface runoff, erosion, infiltration, volatilization and the storing and filtering capacity of soils. The processes indicate the risk for insecticide accumulation in soil, water, sediment and air. A compilation of all available data for traces of insecticides in the environment was used to validate the maps. The risk maps can result in space and time specific measures that reduce the risk of insecticide exposure to non-target organisms.

Keywords: crop protection, pesticide fate, tropics, insecticide resistance

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Authors: Rajkumar Ghosh

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The study presents an overview of the many uses and approaches for estimating out-of-sequence thrust movement in earthquake mitigation. The study investigates how knowing and forecasting thrust movement during seismic occurrences might assist to effective earthquake mitigation measures. The review begins by discussing out-of-sequence thrust movement and its importance in earthquake mitigation strategies. It explores how typical techniques of estimating thrust movement may not capture the full complexity of seismic occurrences and emphasizes the benefits of include out-of-sequence data in the analysis. A thorough review of existing research and studies on out-of-sequence thrust movement estimates for earthquake mitigation. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources such as GPS measurements, satellite imagery, and seismic recordings. The study also examines the use of out-of-sequence thrust movement estimates in earthquake mitigation measures. It investigates how precise calculation of thrust movement may help improve structural design, analyse infrastructure risk, and develop early warning systems. The potential advantages of using out-of-sequence data in these applications to improve the efficiency of earthquake mitigation techniques. The difficulties and limits of estimating out-of-sequence thrust movement for earthquake mitigation. It addresses data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and increase the accuracy and reliability of out-of-sequence thrust movement estimates, the authors recommend topics for additional study and improvement. The study is a helpful resource for seismic monitoring and earthquake risk assessment researchers, engineers, and policymakers, supporting innovations in earthquake mitigation measures based on a better knowledge of thrust movement dynamics.

Keywords: earthquake mitigation, out-of-sequence thrust, satellite imagery, seismic recordings, GPS measurements

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Authors: H. Chassaigne, S. Gioria, J. Lobo Vicente, D. Carpi, P. Barboro, G. Tomasi, A. Kinsner-Ovaskainen, F. Rossi

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Emerging approaches in the area of exposure to nanomaterials and assessment of human health effects combine the use of in vitro systems and analytical techniques to study the perturbation of the proteome and/or the metabolome. We investigated the modification in the cytoplasmic compartment of the Balb/3T3 cell line exposed to gold nanoparticles. On one hand, the proteomic approach is quite standardized even if it requires precautions when dealing with in vitro systems. On the other hand, metabolomic analysis is challenging due to the chemical diversity of cellular metabolites that complicate data elaboration and interpretation. Differentially expressed proteins were found to cover a range of functions including stress response, cell metabolism, cell growth and cytoskeleton organization. In addition, de-regulated metabolites were annotated using the HMDB database. The "omics" fields hold huge promises in the interaction of nanoparticles with biological systems. The combination of proteomics and metabolomics data is possible however challenging.

Keywords: data processing, gold nanoparticles, in vitro systems, metabolomics, proteomics

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Authors: Vinicius R. Domingues, Luan C. S. M. Ozelim

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The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.

Keywords: Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-Von-Mises test, nonparametric adherence tests

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Authors: Sameh Shahin, Nannang Arrys

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Optimizing pit slope stability and reducing strip ratio of a mining operation are two key tasks in geotechnical engineering. With a growing demand for minerals and an increasing cost associated with extraction, companies are constantly re-evaluating the viability of mineral deposits and challenging their geological understanding. Within Rio Tinto Iron Ore, the Structural Geology (SG) team investigate and collect critical data, such as point based orientations, mapping and geological inferences from adjacent pits to re-model deposits where previous interpretations have failed to account for structurally controlled slope failures. Utilizing innovative data collection methods and data-driven investigation, SG aims to address the root causes of slope instability. Committing to a resource grid drill campaign as the primary source of data collection will often bias data collection to a specific orientation and significantly reduce the capability to identify and qualify complexity. Consequently, these limitations make it difficult to construct a realistic and coherent structural model that identifies adverse structural domains. Without the consideration of complexity and the capability of capturing these structural domains, mining operations run the risk of inadequately designed slopes that may fail and potentially harm people. Regional structural trends have been considered in conjunction with surface and in-pit mapping data to model multi-batter fold structures that were absent from previous iterations of the structural model. The risk is evident in newly identified dip-slope and rock-mass controlled sectors of the geotechnical design rather than a ubiquitous dip-slope sector across the pit. The reward is two-fold: 1) providing sectors of rock-mass controlled design in previously interpreted structurally controlled domains and 2) the opportunity to optimize the slope angle for mineral recovery and reduced strip ratio. Furthermore, a resulting high confidence model with structures and geometries that can account for historic slope instabilities in structurally controlled domains where design assumptions failed.

Keywords: structural geology, geotechnical design, optimization, slope stability, risk mitigation

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Authors: Sujan Mahamud

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Purpose: A major challenge in radiotherapy treatment is to deliver precise dose of radiation to the tumor with minimum dose to the healthy normal tissues. Recently, gel dosimetry has emerged as a powerful tool to measure three-dimensional (3D) dose distribution for complex delivery verification and quality assurance. These dosimeters act both as a phantom and detector, thus confirming the versatility of dosimetry technique. The aim of the study is to know the application of Gel Dosimeters in Radiotherapy and find out the comparison with 1D and 2D dimensional dosimeters. Methods and Materials: The study is carried out from Gel Dosimeter literatures. Secondary data and images have been collected from different sources such as different guidelines, books, and internet, etc. Result: Analyzing, verifying, and comparing data from treatment planning system (TPS) is determined that gel dosimeter is a very excellent powerful tool to measure three-dimensional (3D) dose distribution. The TPS calculated data were in very good agreement with the dose distribution measured by the ferrous gel. The overall uncertainty in the ferrous-gel dose determination was considerably reduced using an optimized MRI acquisition protocol and a new MRI scanner. The method developed for comparing measuring gel data with calculated treatment plans, the gel dosimetry method, was proven to be a useful for radiation treatment planning verification. In 1D and 2D Film, the depth dose and lateral for RMSD are 1.8% and 2%, and max (Di-Dj) are 2.5% and 8%. Other side 2D+ ( 3D) Film Gel and Plan Gel for RMSDstruct and RMSDstoch are 2.3% & 3.6% and 1% & 1% and system deviation are -0.6% and 2.5%. The study is investigated that the result fined 2D+ (3D) Film Dosimeter is better than the 1D and 2D Dosimeter. Discussion: Gel Dosimeters is quality control and quality assurance tool which will used the future clinical application.

Keywords: gel dosimeters, phantom, rmsd, QC, detector

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Authors: Sittampalam Manoharan, Gary Chai, Sanaul Chowdhury, Andrew Golding

Abstract:

The primary objective of this paper is to present a simplified approach to develop the structural deterioration model using traffic speed deflectometer data for flexible pavements. Maintaining assets to meet functional performance is not economical or sustainable in the long terms, and it would end up needing much more investments for road agencies and extra costs for road users. Performance models have to be included for structural and functional predicting capabilities, in order to assess the needs, and the time frame of those needs. As such structural modelling plays a vital role in the prediction of pavement performance. A structural condition is important for the prediction of remaining life and overall health of a road network and also major influence on the valuation of road pavement. Therefore, the structural deterioration model is a critical input into pavement management system for predicting pavement rehabilitation needs accurately. The Traffic Speed Deflectometer (TSD) is a vehicle-mounted Doppler laser system that is capable of continuously measuring the structural bearing capacity of a pavement whilst moving at traffic speeds. The device’s high accuracy, high speed, and continuous deflection profiles are useful for network-level applications such as predicting road rehabilitations needs and remaining structural service life. The methodology adopted in this model by utilizing time series TSD maximum deflection (D0) data in conjunction with rutting, rutting progression, pavement age, subgrade strength and equivalent standard axle (ESA) data. Then, regression analyses were undertaken to establish a correlation equation of structural deterioration as a function of rutting, pavement age, seal age and equivalent standard axle (ESA). This study developed a simple structural deterioration model which will enable to incorporate available TSD structural data in pavement management system for developing network-level pavement investment strategies. Therefore, the available funding can be used effectively to minimize the whole –of- life cost of the road asset and also improve pavement performance. This study will contribute to narrowing the knowledge gap in structural data usage in network level investment analysis and provide a simple methodology to use structural data effectively in investment decision-making process for road agencies to manage aging road assets.

Keywords: adjusted structural number (SNP), maximum deflection (D0), equant standard axle (ESA), traffic speed deflectometer (TSD)

Procedia PDF Downloads 148

Authors: A. Hidalgo, A. Desportes, E. Bonin, A. Kadaoui, T. Bouaricha

Abstract:

Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation.

Keywords: Black-Litterman, Markowitz, market data, portfolio manager opinion

Procedia PDF Downloads 259

Authors: Dana Craciun, Daniela Dascalu, Adriana Isvoran

Abstract:

Phthalates are a class of plastic additives that are used to enhance the physical properties of plastics and as solvents in paintings and some of them proved to be of particular concern for the human health. There are insufficient data concerning the health risks of phthalates and further research on evaluating their effects in humans is needed. As humans are not volunteers for such experiments, computational analysis may be used to predict the biological effects of phthalates in humans. Within this study we have used some computational approaches (SwissADME, admetSAR, FAFDrugs) for predicting the absorption, distribution, metabolization, excretion and toxicity (ADME-Tox) profiles and pharmacokinetics for the most common used phthalates. These computational tools are based on quantitative structure-activity relationship modeling approach. The predictions are further compared to the known effects of each considered phthalate in humans and correlations between computational results and experimental data are discussed. Our data revealed that phthalates are a class of compounds reflecting high toxicity both when ingested and when inhaled, but by inhalation their toxicity is even greater. The predicted harmful effects of phthalates are: toxicity and irritations of the respiratory and gastrointestinal tracts, dyspnea, skin and eye irritations and disruption of the functions of liver and of the reproductive system. Many of investigated phthalates are predicted to be able to inhibit some of the cytochromes involved in the metabolism of numerous drugs and consequently to affect the efficiency of administrated treatments for many diseases and to intensify the adverse drugs reactions. The obtained predictions are in good agreement with clinical data concerning the observed effects of some phthalates in cases of acute exposures. Our study emphasizes the possible health effects of numerous phthalates and underlines the applicability of computational methods for predicting the biological effects of xenobiotics.

Keywords: phthalates, ADME-Tox, pharmacokinetics, biological effects

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Authors: Belayneh Matebie, Michael Melese

Abstract:

The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 51