Search results for: gamma distributed degradation model

Authors: Katarzyna Rzeszut, Ilona Szewczak

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The review of selected methods of strengthening of steel structures with carbon fiber reinforced polymer (CFRP) tapes and the analysis of influence of composite materials on the steel thin-walled elements are performed in this paper. The study is also focused to the problem of applying fast and effective strengthening methods of the steel structures made of thin-walled profiles. It is worth noting that the issue of strengthening the thin-walled structures is a very complex, due to inability to perform welded joints in this type of elements and the limited ability to applying mechanical fasteners. Moreover, structures made of thin-walled cross-section demonstrate a high sensitivity to imperfections and tendency to interactive buckling, which may substantially contribute to the reduction of critical load capacity. Due to the lack of commonly used and recognized modern methods of strengthening of thin-walled steel structures, authors performed the experimental studies of thin-walled sigma profiles strengthened with CFRP tapes. The paper presents the experimental stand and the preliminary results of laboratory test concerning the analysis of the effectiveness of the strengthening steel beams made of thin-walled sigma profiles with CFRP tapes. The study includes six beams made of the cold-rolled sigma profiles with height of 140 mm, wall thickness of 2.5 mm, and a length of 3 m, subjected to the uniformly distributed load. Four beams have been strengthened with carbon fiber tape Sika CarboDur S, while the other two were tested without strengthening to obtain reference results. Based on the obtained results, the evaluation of the accuracy of applied composite materials for strengthening of thin-walled structures was performed.

Keywords: CFRP tapes, sigma profiles, steel thin-walled structures, strengthening

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Authors: Zafar Mahmood, Surayya Naz, Nazir Shah Khattak

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The degree of freedom relates to our capability to observe or model the energy paths within the system. Higher the number of energy paths being modeled leaves to us a higher degree of freedom, but increasing the time and modeling complexity rendering it useless for today’s world’s need for minimum time to market. Since the number of residuals that can be uniquely isolated are dependent on the number of independent outputs of the system, increasing the number of sensors required. The examples of discrete position sensors that may be used to form an array include limit switches, Hall effect sensors, optical sensors, magnetic sensors, etc. Their mechanical design can usually be tailored to fit in the transitional path of an STME in a variety of mechanical configurations. The case studies into multi-sensor system were carried out and actual data from sensors is used to test this generic framework. It is being investigated, how the proper modeling of limit switches as timing sensors, could lead to unified and neutral residual space while keeping the implementation cost reasonably low.

Keywords: low-cost limit sensors, fault diagnostics, Single Throw Mechanical Equipment (STME), parameter estimation, parity-space

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Authors: Nouha Ben Salah, Mohamed Ali Omri

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This paper treats the association between disclosure level and the cost of equity capital in Tunisian’slisted firms. This relation is tested by using two models. The first is used for testing this relation directly by regressing firm specific estimates of cost of equity capital on market beta, firm size and a measure of disclosure level. The second model is used for testing this relation by introducing information asymmetry as mediator variable. This model is suggested by Baron and Kenny (1986) to demonstrate the role of mediator variable in general. Based on a sample of 21 non-financial Tunisian’s listed firms over a period from 2000 to 2004, the results prove that greater disclosure is associated with a lower cost of equity capital. However, the results of indirect relationship indicate a significant positive association between the level of voluntary disclosure and information asymmetry and a significant negative association between information asymmetry and cost of equity capital in contradiction with our previsions. Perhaps this result is due to the biases of measure of information asymmetry.

Keywords: cost of equity capital, voluntary disclosure, information asymmetry, and Tunisian’s listed non-financial firms

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Authors: M. Kosandi, V. Susanti, N. I. Subono, E. Kartini

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This paper discusses the risk of glorification trap in combating human trafficking, as it is shown in the case of Indonesia. Based on a research on Indonesian combat against trafficking in 2017-2018, this paper shows the tendency of misinterpretation and misapplication of the Indonesian anti-trafficking law into misusing the law for glorification, to create an image of certain extent of achievement in combating human trafficking. The objective of this paper is to explain the persistent occurrence of human trafficking crimes despite the significant progress of anti-trafficking efforts of Indonesian government. The research was conducted in 2017-2018 by qualitative approach through observation, depth interviews, discourse analysis, and document study, applying the three-dimensional model for analyzing human trafficking in the source country. This paper argues that the drive for glorification of achievement in the combat against trafficking has trapped Indonesian government in the loop of misinterpretation, misapplication, and misuse of the anti-trafficking law. In return, the so-called crime against humanity remains high and tends to increase in Indonesia.

Keywords: human trafficking, anti-trafficking policy, transnational crime, source country, glorification trap

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Authors: Saroj Panthi, Sher Singh Thagunna

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Reliable and sufficient information regarding status, distribution and habitat preference of red panda (Ailurus fulgens fulgens) is lacking in Nepal. The research activities on red panda in the mid-western Nepal are very limited, so the status of red panda in the region is quite unknown. The study conducted during May, 2013 in three Village Development Committees (VDCs) namely Godhemahadev, Malikathata and Tamti of Jumla district was an important step for providing vital information including distribution and habitat preference of this species. The study included the reconnaissance, key informants survey, interviews, and consultation for the most potential area identification, opportunistic survey comprising the direct observation and indirect sign count method for the presence and distribution, habitat assessment consisting vegetation sampling and ocular estimation. The study revealed the presence of red panda in three forests namely Bahirepatan, Imilchadamar and Tyakot of Godhemahadev, Tamti and Malikathata VDCs respectively. The species was found distributed between 2880 and 3244 m with an average dropping encounter rate of 1.04 per hour of searching effort and 12 pellets per dropping. Red panda mostly preferred the habitat in the elevation range of 2900 - 3000 m with southwest facing steep slopes (36˚ - 45˚), associated with water sources at the distance of ≤100 m. Trees such as Acer spp., Betula utilis and Quercus semecarpifolia, shrub species of Elaeagnus parvifolia, Drepanostachyum spp. and Jasminum humile, and the herbs like Polygonatum cirrhifolium, Fragaria nubicola and Galium asperifolium were found to be the most preferred species by red panda. The red panda preferred the habitat with dense crown coverage ( >20% - 100%) and 31% - 50% ground cover. Fallen logs (39%) were the most preferred substrate used for defecation.

Keywords: distribution, habitat preference, jumla, red panda

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Authors: Shereif. H. Mahmoud

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Egypt has limited water resources, and it will be under water stress by the year 2030. Therefore, Egypt should consider natural and non-conventional water resources to overcome such a problem. Rain harvesting is one solution. This Paper presents a geographic information system (GIS) methodology - based on decision support system (DSS) that uses remote sensing data, filed survey, and GIS to identify potential RWH areas. The input into the DSS includes a map of rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, soil texture. In addition, the outputs are map showing potential sites for RWH. Identifying suitable RWH sites implemented in the ArcGIS model environment using the model builder of ArcGIS 10.1. Based on Analytical hierarchy process (AHP) analysis taking into account five layers, the spatial extents of RWH suitability areas identified using Multi-Criteria Evaluation (MCE). The suitability model generated a suitability map for RWH with four suitability classes, i.e. Excellent, Moderate, Poor, and unsuitable. The spatial distribution of the suitability map showed that the excellent suitable areas for RWH concentrated in the northern part of Egypt. According to their averages, 3.24% of the total area have excellent and good suitability for RWH, while 45.04 % and 51.48 % of the total area are moderate and unsuitable suitability, respectively. The majority of the areas with excellent suitability have slopes between 2 and 8% and with an intensively cultivated area. The major soil type in the excellent suitable area is loam and the rainfall range from 100 up to 200 mm. Validation of the used technique depends on comparing existing RWH structures locations with the generated suitability map using proximity analysis tool of ArcGIS 10.1. The result shows that most of exiting RWH structures categorized as successful.

Keywords: rainwater harvesting (RWH), geographic information system (GIS), analytical hierarchy process (AHP), multi-criteria evaluation (MCE), decision support system (DSS)

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Authors: Desiree Vandenberghe, Johan Albrecht

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Non-communicable diseases (NCDs) are responsible for the vast majority of deaths in the European Union (EU) and represent a large share of total health care spending. A future increase in this health and financial burden is likely to be driven by population ageing, lifestyle changes and technological advances in medicine. Without adequate prevention measures, this burden can severely threaten population health and economic development. To tackle this challenge, a correct assessment of the current burden of NCDs is required, as well as a projection of potential increases of this burden. The contribution of this paper is to offer perspective on the evolution of the NCD burden towards the future and to give an indication of the potential of prevention policy. A Non-Homogenous, Semi-Markov model for the EU was constructed, which allowed for a projection of the cost burden for the four main NCDs (cancer, cardiovascular disease, chronic respiratory disease and diabetes mellitus) towards 2030 and 2050. This simulation is done based on multiple baseline scenarios that vary in demand and supply factors such as health status, population structure, and technological advances. Finally, in order to assess the potential of preventive measures to curb the cost explosion of NCDs, a simulation is executed which includes increased efforts for preventive health care measures. According to the Markov model, by 2030 and 2050, total costs (direct and indirect costs) in the EU could increase by 30.1% and 44.1% respectively, compared to 2015 levels. An ambitious prevention policy framework for NCDs will be required if the EU wants to meet this challenge of rising costs. To conclude, significant cost increases due to Non-Communicable Diseases are likely to occur due to demographic and lifestyle changes. Nevertheless, an ambitious prevention program throughout the EU can aid in making this cost burden manageable for future generations.

Keywords: non-communicable diseases, preventive health care, health policy, Markov model, scenario analysis

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Sipho Mbatha, Anne Mastament-Mason

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South African manufacturing global competitiveness was ranked 22nd (out of 38 countries), dropped to 24th in 2013 and is expected to drop further to 25th by 2018. These impacts negatively on the industrialisation project of South Africa. For industrialization to be achieved through labour intensive industries like the Apparel Manufacturing Industries of South Africa (AMISA), South Africa needs to identify and respond to factors negatively impacting on the development of competitive advantage This paper applied factor conditions from Porter’s Diamond Model (1990) to understand the various challenges facing the AMISA. Factor conditions highlighted in Porter’s model are grouped into two groups namely, basic and advance factors. Two AMISA associations representing over 10 000 employees were interviewed. The largest Clothing, Textiles and Leather (CTL) apparel retail group was also interviewed with a government department implementing the industrialisation policy were interviewed The paper points out that while AMISA have basic factor conditions necessary for competitive advantage in the clothing and textiles industries, Advance factor coordination has proven to be a challenging task for the AMISA, Higher Education Institutions (HEIs) and government. Poor infrastructural maintenance has contributed to high manufacturing costs and poor quick response as a result of lack of advanced technologies. The use of Porter’s Factor Conditions as a tool to analyse the sector’s competitive advantage challenges and opportunities has increased knowledge regarding factors that limit the AMISA’s competitiveness. It is therefore argued that other studies on Porter’s Diamond model factors like Demand conditions, Firm strategy, structure and rivalry and Related and supporting industries can be used to analyse the situation of the AMISA for the purposes of improving competitive advantage.

Keywords: compliance rule, apparel manufacturing industry, factor conditions, advance skills and South African industrial policy

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Saeed Ullah Jan, Shaukat Ullah

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Purpose of the study: The main theme of this study is to explore the information literacy skills of the law practitioners in Khyber Pakhtunkhwa-Pakistan under the heading "Information Literacy Skills of Legal Practitioners in Khyber Pakhtunkhwa-Pakistan: An Empirical Study." Research Method and Procedure: To conduct this quantitative study, the simple random sample approach is used. An adapted questionnaire is distributed among 254 lawyers of Dera Ismail Khan through personal visits and electronic means. The data collected is analyzed through SPSS (Statistical Package for Social Sciences) software. Delimitations of the study: The study is delimited to the southern district of Khyber Pakhtunkhwa: Dera Ismael Khan. Key Findings: Most of the lawyers of District Dera Ismail Khan of Khyber Pakhtunkhwa can recognize and understand the needed information. A large number of lawyers are capable of presenting information in both written and electronic forms. They are not comfortable with different legal databases and using various searching and keyword techniques. They have less knowledge of Boolean operators for locating online information. Conclusion and Recommendations: Efforts should be made to arrange refresher courses and training workshops on the utilization of different legal databases and different search techniques for retrieval of information sources. This practice will enhance the information literacy skills of lawyers, which will ultimately result in a better legal system in Pakistan. Practical implication(s): The findings of the study will motivate the policymakers and authorities of legal forums to restructure the information literacy programs to fulfill the lawyers' information needs. Contribution to the knowledge: No significant work has been done on the lawyers' information literacy skills in Khyber Pakhtunkhwa-Pakistan. It will bring a clear picture of the information literacy skills of law practitioners and address the problems faced by them during the seeking process.

Keywords: information literacy-Pakistan, infromation literacy-lawyers, information literacy-lawyers-KP, law practitioners-Pakistan

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Protima Chakraborty

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The sustainability of conventional agriculture in Bangladesh is under threat from the continuous degradation of land and water resources, and from declining yields due to indiscriminate use of agrochemicals. NASL (Northern Agro Services Limited) is pursuing efforts to promote ecological agriculture with emphasis on better use of organic fertilizer resources and the reduction of external inputs. This paper examines the sustainability of two production systems in terms of their environmental soundness, economic viability and social acceptability based on empirical data collected through making demonstration land cultivation, a household survey, soil sample analysis, observations and discussions with key informants. Twelve indicators were selected to evaluate sustainability. Significant differences were found between the two systems in crop diversification, soil fertility management, pests and diseases management, and use of agrochemicals & Organic Compost. However, significant variations were found in other indicators such as land-use pattern, crop yield and stability, risk and uncertainties, and food security. Although crop yield and financial return were found to be slightly higher in the ecological system, the economic return and value addition per unit of land show the positive difference of using compost rather than chemical fertilizer. The findings suggest that ecological agriculture has a tendency towards becoming ecologically, economically and socially more sound than conventional agriculture, as it requires considerably fewer agro-chemicals, adds more organic matter to the soil, provides balanced food, and requires higher local inputs without markedly compromising output and financial benefits. Broad-policy measures, including the creation of mass awareness of adverse health effects of agrochemical-based products, are outlined for the promotion of ecological agriculture.

Keywords: Bangladesh, compost, conventional agriculture, organic fertilizer, environmental sustainability, economic viability, social acceptability

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Authors: Nicolas Lopez-Villalobos, Dorian J. Garrick, Hugh T. Blair

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Many dairy goat farmers of New Zealand milk herds of mixed breed does. Simultaneous evaluation of sires and does across breed is required to select the best animals for breeding on a common basis. Across-breed estimated breeding values (EBV) and estimated producing values for 208-day lactation yields of milk (MY), fat (FY), protein (PY) and somatic cell score (SCS; LOG2(SCC) of Saanen, Nubian, Alpine, Toggenburg and crossbred dairy goats from 75 herds were estimated using a test day model. Evaluations were based on 248,734 herd-test records representing 125,374 lactations from 65,514 does sired by 930 sires over 9 generations. Averages of MY, FY and PY were 642 kg, 21.6 kg and 19.8 kg, respectively. Average SCC and SCS were 936,518 cells/ml milk and 9.12. Pure-bred Saanen does out-produced other breeds in MY, FY and PY. Average EBV for MY, FY and PY compared to a Saanen base were Nubian -98 kg, 0.1 kg and -1.2 kg; Alpine -64 kg, -1.0 kg and -1.7 kg; and Toggenburg -42 kg, -1.0 kg and -0.5 kg. First-cross heterosis estimates were 29 kg MY, 1.1 kg FY and 1.2 kg PY. Average EBV for SCS compared to a Saanen base were Nubian 0.041, Alpine -0.083 and Toggenburg 0.094. Heterosis for SCS was 0.03. Breeding values are combined with respective economic values to calculate an economic index used for ranking sires and does to reflect farm profit.

Keywords: breed effects, dairy goats, milk traits, test-day model

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Authors: Olga Kononova, Sergey Lyapin

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Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

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Authors: Odete Paiva, Cláudia Seabra, José Luís Abrantes, Fernanda Cravidão

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There is a clear ‘cult of authenticity’, at least in modern Western society. So, there is a need to analyze the tourist perception of authenticity, bearing in mind the destination, its attractions, motivations, cultural distance, and contact with other tourists. Our study seeks to investigate the relationship among cultural values, image, sense of place, perception of authenticity and behavior intentions at World Heritage Historic Centers. From a theoretical perspective, few researches focus on the impact of cultural values, image and sense of place on authenticity and intentions behavior in tourists. The intention of this study is to help close this gap. A survey was applied to collect data from tourists visiting two World Heritage Historic Centers – Guimarães in Portugal and Cordoba in Spain. Data was analyzed in order to establish a structural equation model (SEM). Discussion centers on the implications of model to theory and managerial development of tourism strategies. Recommendations for destinations managers and promoters and tourist organizations administrators are addressed.

Keywords: authenticity perception, behavior intentions, cultural tourism, cultural values, world heritage historic centers

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Negar Sartipzadeh

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Energy-conscious design, which coordinates with the Earth ecological systems during its life cycle, has the least negative impact on the environment with the least waste of resources. Due to the increasing in world population as well as the consumption of fossil fuels that cause the production of greenhouse gasses and environmental pollution, mankind is looking for renewable and also sustainable energies. The Iranian native construction is a clear evidence of energy-aware designing. Our predecessors were forced to rely on the natural resources and sustainable energies as well as environmental issues which have been being considered in the recent world. One of these endless energies is wind energy. Iranian traditional architecture foundations is a appropriate model in solving the environmental crisis and the contemporary energy. What will come in this paper is an effort to recognition and introduction of the unique characteristics of the Iranian architecture in the application of aerodynamic and hydraulic energies derived from the wind, which are the most common and major type of using sustainable energies in the traditional architecture of Iran. Therefore, the recent research attempts to offer a hybrid system suggestions for application in new constructions designing in a region such as Nashtifan, which has potential through reviewing windmills and how they deal with sustainable energy sources, as a model of Iranian native construction.

Keywords: renewable energy, sustainable building, windmill, Iranian architecture

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Julia Zimmerman, Gaurav Savant

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This study aims to evaluate the efficacy of the U.S. Army Corp of Engineers’ River Analysis System (HEC-RAS) application to modeling the hydraulics of estuaries. HEC-RAS has been broadly used for a variety of riverine applications. However, it has not been widely applied to the study of circulation in estuaries. This report details the model development and validation of a combined 1D/2D unsteady flow hydraulic model using HEC-RAS for estuaries and they are associated with tidally influenced rivers. Two estuaries, Galveston Bay and Delaware Bay, were used as case studies. Galveston Bay, a bar-built, vertically mixed estuary, was modeled for the 2005 calendar year. Delaware Bay, a drowned river valley estuary, was modeled from October 22, 2019, to November 5, 2019. Water surface elevation was used to validate both models by comparing simulation results to NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) gauge data. Simulations were run using the Diffusion Wave Equations (DW), the Shallow Water Equations, Eulerian-Lagrangian Method (SWE-ELM), and the Shallow Water Equations Eulerian Method (SWE-EM) and compared for both accuracy and computational resources required. In general, the Diffusion Wave Equations results were found to be comparable to the two Shallow Water equations sets while requiring less computational power. The 1D/2D combined approach was valid for study areas within the 2D flow area, with the 1D flow serving mainly as an inflow boundary condition. Within the Delaware Bay estuary, the HEC-RAS DW model ran in 22 minutes and had an average R² value of 0.94 within the 2-D mesh. The Galveston Bay HEC-RAS DW ran in 6 hours and 47 minutes and had an average R² value of 0.83 within the 2-D mesh. The longer run time and lower R² for Galveston Bay can be attributed to the increased length of the time frame modeled and the greater complexity of the estuarine system. The models did not accurately capture tidal effects within the 1D flow area.

Keywords: Delaware bay, estuarine hydraulics, Galveston bay, HEC-RAS, one-dimensional modeling, two-dimensional modeling

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Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

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The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Amira Abdelrasoul, Huu Doan, Ali Lohi

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An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling.

Keywords: membrane fouling, mathematical modeling, power consumption, attachments, ultrafiltration

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Authors: S. J. Higgs, E. Van Eck, K. Heynis, S. H. Broadberry

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Engagement with non-human animals is a rapidly-growing field of study within the animal science and social science sectors, with human-interactions occurring in many forms; interactions, encounters and animal-assisted therapy. To our knowledge, there has been a wide array of research published on domestic and livestock human-animal interactions, however, there appear to be fewer publications relating to zoo animals and the effect these interactions have on the animal, human and establishment. The aim of this study was to identify if there were any perceivable benefits from the human-animal interaction for the cheetah, the human and the establishment. Behaviour data were collected before, during and after the interaction on the behaviour of the cheetah and the human participants to highlight any trends with nine interactions conducted. All 35 participants were asked to fill in a questionnaire prior to the interaction and immediately after to ascertain if their perceptions changed following an interaction with the cheetah. An online questionnaire was also distributed for three months to gain an understanding of the perceptions of human-animal interactions from members of the public, gaining 229 responses. Both questionnaires contained qualitative and quantitative questions to allow for specific definitive answers to be analysed, but also expansion on the participants perceived perception of human-animal interactions. In conclusion, it was found that participants’ perceptions of human-animal interactions saw a positive change, with 64% of participants altering their opinion and viewing the interaction as beneficial for the cheetah (reduction in stress assumed behaviours) following participation in a 15-minute interaction. However, it was noted that many participants felt the interaction lacked educational values and therefore this is an area in which zoological establishments can work to further improve upon. The results highlighted many positive benefits for the human, animal and establishment, however, the study does indicate further areas for research in order to promote positive perceptions of human-animal interactions and to further increase the welfare of the animal during these interactions, with recommendations to create and regulate legislation.

Keywords: Acinonyx jubatus, encounters, human-animal interactions, perceptions, zoological establishments

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Authors: Nermeen Elbeltagy, Sara Hassan, Tamer Hosny, Mostafa Abdelaziz

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Introduction: Adequate postoperative analgesia after caesarean section (CS) is crucial as it impacts the distinct surgical recovery needs of the parturient. Over recent years, there has been increased interest in regional nerve block techniques with promising results on efficacy. These techniques reduce the need for additional analgesia, thereby lowering the incidence of drug-related side effects. As postoperative pain after cesarean is mainly due to abdominal incision, the transverses abdomenis plane ( TAP ) block is a relatively new abdominal nerve block with excellent efficacy after different abdominal surgeries, including cesarean section. Objective: The main objective is to compare ultrasound-guided TAP block provided by the anesthesiologist with TAP provided by the surgeon through a caesarean incision regarding the duration of postoperative analgesia, intensity of analgesia, timing of mobilization, and easiness of the procedure. Method: Ninety pregnant females at term who were scheduled for delivery by elective cesarean section were randomly distributed into two groups. The first group (45) received spinal anesthesia and postoperative ultrasound guided TAP block using 20ml on each side of 0.25% bupivacaine which was provided by the anesthesiologist. The second group (45) received spinal anesthesia plus a TAP block using 20ml on each side of 0.25% bupivacaine, which was provided by the surgeon through the cesarean incision. Visual Analogue Scale (VAS) was used for the comparison between the two groups. Results: VAS score after four hours was higher among the TAP block group provided by the surgeon through the surgical incision than the postoperative analgesic profile using ultrasound-guided TAP block provided by the anesthesiologist (P=0.011). On the contrary, there was no statistical difference in the patient’s dose of analgesia after four hours of the TAP block (P=0.228). Conclusion: TAP block provided through the surgical incision is safe and enhances early patient’s mobilization.

Keywords: TAP block, CS, VAS, analgesia

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Authors: Jeaha Yang, Ahmed Khan, Donna L. Rodela, Mohammed A. Qaudeer

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Traditional supply chain silos still exist today due to the difficulty of establishing trust between various partners and technological barriers across industries. Companies lose opportunities and revenue and inadvertently make poor business decisions resulting in further challenges. Blockchain technology can bring a new level of transparency through sharing information with a distributed ledger in a decentralized manner that creates a basis of trust for business. Blockchain is a loosely coupled, hub-style communication network in which trading partners can work indirectly with each other for simpler integration, but they work together through the orchestration of their supply chain operations under a coherent process that is developed jointly. A Blockchain increases efficiencies, lowers costs, and improves interoperability to strengthen and automate the supply chain management process while all partners share the risk. Blockchain ledger is built to track inventory lifecycle for supply chain transparency and keeps a journal of inventory movement for real-time reconciliation. State design patterns are used to capture the life cycle (behavior) of inventory management as a state machine for a common, transparent and coherent process which creates an opportunity for trading partners to become more responsive in terms of changes or improvements in process, reconcile discrepancies, and comply with internal governance and external regulations. It enables end-to-end, inter-company visibility at the unit level for more accurate demand planning with better insight into order fulfillment and replenishment.

Keywords: supply chain management, inventory trace-ability, perpetual inventory system, inventory lifecycle, blockchain, inventory consignment, supply chain transparency, digital thread, demand planning, hyper ledger fabric

Procedia PDF Downloads 88