Search results for: distributed algorithms

Authors: Stephanie Chen, Mitja Echim, Christof Büskens

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To describe and propagate the behavior of a system mathematical models are formulated. Parameter identification is used to adapt the coefficients of the underlying laws of science. For complex systems this approach can be incomplete and hence imprecise and moreover too slow to be computed efficiently. Therefore, these models might be not applicable for the numerical optimization of real systems, since these techniques require numerous evaluations of the models. Moreover not all quantities necessary for the identification might be available and hence the system must be adapted manually. Therefore, an approach is described that generates models that overcome the before mentioned limitations by not focusing on physical laws, but on measured (sensor) data of real systems. The approach is more general since it generates models for every system detached from the scientific background. Additionally, this approach can be used in a more general sense, since it is able to automatically identify correlations in the data. The method can be classified as a multivariate data regression analysis. In contrast to many other data regression methods this variant is also able to identify correlations of products of variables and not only of single variables. This enables a far more precise and better representation of causal correlations. The basis and the explanation of this method come from an analytical background: the series expansion. Another advantage of this technique is the possibility of real-time adaptation of the generated models during operation. Herewith system changes due to aging, wear or perturbations from the environment can be taken into account, which is indispensable for realistic scenarios. Since these data driven models can be evaluated very efficiently and with high precision, they can be used in mathematical optimization algorithms that minimize a cost function, e.g. time, energy consumption, operational costs or a mixture of them, subject to additional constraints. The proposed method has successfully been tested in several complex applications and with strong industrial requirements. The generated models were able to simulate the given systems with an error in precision less than one percent. Moreover the automatic identification of the correlations was able to discover so far unknown relationships. To summarize the above mentioned approach is able to efficiently compute high precise and real-time-adaptive data-based models in different fields of industry. Combined with an effective mathematical optimization algorithm like WORHP (We Optimize Really Huge Problems) several complex systems can now be represented by a high precision model to be optimized within the user wishes. The proposed methods will be illustrated with different examples.

Keywords: adaptive modeling, automatic identification of correlations, data based modeling, optimization

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Jaehyung An, Sungjoo Lee

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Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology.

Keywords: NLP, patent analysis, SAO, semantic-analysis

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservices are a software development methodology in which applications are built by composing a set of independently deploy-able, small, modular services. Each service runs a unique process and it gets instantiated and deployed in one or more machines (we assume that different microservices are deployed into different machines). Microservices are becoming the de facto standard for developing distributed cloud applications due to their reduced release cycles. In principle, the responsibility of a microservice can be as simple as implementing a single function, which can lead to the following issues: - Resource fragmentation due to the virtual machine boundary. - Poor communication performance between microservices. Two composition techniques can be used to optimize resource fragmentation and communication performance: aggregation and embedding of microservices. Aggregation allows the deployment of a set of microservices on the same machine using a proxy server. Aggregation helps to reduce resource fragmentation, and is particularly useful when the aggregated services have a similar scalability behavior. Embedding deals with communication performance by deploying on the same virtual machine those microservices that require a communication channel (localhost bandwidth is reported to be about 40 times faster than cloud vendor local networks and it offers better reliability). Embedding can also reduce dependencies on load balancer services since the communication takes place on a single virtual machine. For example, assume that microservice A has two instances, a1 and a2, and it communicates with microservice B, which also has two instances, b1 and b2. One embedding can deploy a1 and b1 on machine m1, and a2 and b2 are deployed on a different machine m2. This deployment configuration allows each pair (a1-b1), (a2-b2) to communicate using the localhost interface without the need of a load balancer between microservices A and B. Aggregation and embedding techniques are complex since different microservices might have incompatible runtime dependencies which forbid them from being installed on the same machine. There is also a security concern since the attack surface between microservices can be larger. Luckily, container technology allows to run several processes on the same machine in an isolated manner, solving the incompatibility of running dependencies and the previous security concern, thus greatly simplifying aggregation/embedding implementations by just deploying a microservice container on the same machine as the aggregated/embedded microservice container. Therefore, a wide variety of deployment configurations can be described by combining aggregation and embedding to create an efficient and robust microservice architecture. This paper presents a formal method that receives a declarative definition of a microservice architecture and proposes different optimized deployment configurations by aggregating/embedding microservices. The first prototype is based on i2kit, a deployment tool also submitted to ICWS 2018. The proposed prototype optimizes the following parameters: network/system performance, resource usage, resource costs and failure tolerance.

Keywords: aggregation, deployment, embedding, resource allocation

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Yun-Hsiang Lee

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Cancer is the main cause of death worldwide. With advances in medical technology, immunotherapy, which is a newly developed advanced treatment, is currently a crucial cancer treatment option. For better quality cancer care, the ability to communicate and critical thinking plays a central role in clinical oncology settings. However, few studies have explored the impact of communication skills on immunotherapy-related issues and their related factors. This study was to (i) explore the current status of communication skill ability for immunotherapy-related issues, self-efficacy for immunotherapy-related care, and critical thinking ability; and (ii) identify factors related to communication skill ability. This is a cross-sectional study. Oncology nurses were recruited from the Taiwan Oncology Nursing Society, in which nurses came from different hospitals distributed across four major geographic regions (North, Center, South, East) of Taiwan. A total of 123 oncology nurses participated in this study. A set of questionnaires were used for collecting data. Communication skill ability for immunotherapy issues, self-efficacy for immunotherapy-related care, critical thinking ability, and background information were assessed in this survey. Independent T-test and one-way ANOVA were used to examine different levels of communication skill ability based on nurses having done oncology courses (yes vs. no) and education years (< 1 year, 1-3 years, and > 3 years), respectively. Spearman correlation was conducted to understand the relationships between communication skill ability and other variables. Among the 123 oncology nurses in the current study, the majority of them were female (98.4%), and most of them were employed at a hospital in the North (46.8%) of Taiwan. Most of them possessed a university degree (78.9%) and had at least 3 years of prior work experience (71.7%). Forty-three of the oncology nurses indicated in the survey that they had not received oncology nurses-related training. Those oncology nurses reported moderate to high levels of communication skill ability for immunotherapy issues (mean=4.24, SD=0.7, range 1-5). Nurses reported moderate levels of self-efficacy for immunotherapy-related care (mean=5.20, SD=1.98, range 0-10) and also had high levels of critical thinking ability (mean=4.76, SD=0.60, range 1-6). Oncology nurses who had received oncology training courses had significantly better communication skill ability than those who had not received oncology training. Oncology nurses who had higher work experience (1-3 years, or > 3 years) had significantly higher levels of communication skill ability for immunotherapy-related issues than those with lower work experience (<1 year). When those nurses reported better communication skill ability, they also had significantly better self-efficacy (r=.42, p<.01) and better critical thinking ability (r=.47, p<.01). Taken altogether, courses designed to improve communication skill ability for immunotherapy-related issues can make a significant impact in clinical settings. Communication skill ability for oncology nurses is the major factor associated with self-efficacy and critical thinking, especially for those with lower work experience (< 1 year).

Keywords: communication skills, critical thinking, immunotherapy, oncology nurses, self-efficacy

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Hamid Abdulroof Saleh

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Background: Dioxins are one of the most widely distributed environmental pollutants. Dioxins consist of feedstock during the preparation of some industries, such as the paper industry as they can be produced in the atmosphere during the process of burning garbage and waste, especially medical waste. Dioxins can be found in the adipose tissues of animals in the food chain as well as in human breast milk. 2,3,7,8-Tetrachlorodibenzo-pdioxin (TCDD) is the most toxic component of a large group of dioxins. Humans are exposed to TCDD through contaminated food items like meat, fish, milk products, eggs etc. Recently, natural formulations relating to reducing or eliminating TCDD toxicity have been in focus. Ginger rhizome (Zingiber officinale R., family: Zingiberaceae), is used worldwide as a spice. Both antioxidative and androgenic activity of Z. officinale was reported in animal models. Researchers showed that ginger oil has dominative protective effect on DNA damage and might act as a scavenger of oxygen radical and might be used as an antioxidant. Aim of the work: The present study was undertaken to evaluate the toxic effect of TCDD on the structure and histoarchitecture of the testis and the protective role of co-administration of ginger root extract to prevent this toxicity. Materials & Methods: Male adult rats of Sprague-Dawley strain were assigned to four groups, eight rats in each; control group, dioxin treated group (given TCDD at the dose of 100 ng/kg Bwt/day by gavage), ginger treated group (given 50 mg/kg Bwt/day of ginger root extract by gavage), dioxin and ginger treated group (given TCDD at the dose of 100 ng/kg Bwt/day and 50 mg/kg Bwt/day of ginger root extract by gavages). After three weeks, rats were weighed and sacrificed where testis were removed and weighted. The testes were processed for routine paraffin embedding and staining. Tissue sections were examined for different morphometric and histopathological changes. Results: Dioxin administration showed a harmful effects in the body, testis weight and other morphometric parameters of the testis. In addition, it produced varying degrees of damage to the seminiferous tubules, which were shrunken and devoid of mature spermatids. The basement membrane was disorganized with vacuolization and loss of germinal cells. The co-administration of ginger root extract showed obvious improvement in the above changes and showed reversible morphometric and histopathological changes of the seminiferous tubules. Conclusion: Ginger root extract treatment in this study was successful in reversing all morphometric and histological changes of dioxin testicular damage. Therefore, it showed a protective effect on testis against dioxin toxicity.

Keywords: dioxin, ginger, rat, testis

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Authors: Susan Mwai, Mary Muchane, Peter Wachira, Sheila Okoth, Muchai Muchane, Halima Saado

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Tropical forests harbor a wide range of biodiversity and rich macro-fungi diversity compared to the temperate regions in the World. However, biodiversity is facing the threat of extinction following the rate of forest loss taking place before proper study and documentation of macrofungi is achieved. The present study was undertaken to determine the effect of converting indigenous habitat to plantation forest on macrofungi diversity. To achieve the objective of this study, an inventory focusing on macro-fungi diversity was conducted within Kereita block in Kikuyu Escarpment forest which is on the southern side of Aberdare mountain range. The macrofungi diversity was conducted in the indigenous forest and in more than 15 year old Patula plantation forest , during the wet (long rain season, December 2014) and dry (Short rain season, May, 2015). In each forest type, 15 permanent (20m x 20m) sampling plots distributed across three (3) forest blocks were used. Both field and laboratory methods involved recording abundance of fruiting bodies, taxonomic identity of species and analysis of diversity indices and measures in terms of species richness, density and diversity. R statistical program was used to analyze for species diversity and Canoco 4.5 software for species composition. A total number of 76 genera in 28 families and 224 species were encountered in both forest types. The most represented taxa belonged to the Agaricaceae (16%), Polyporaceae (12%), Marasmiaceae, Mycenaceae (7%) families respectively. Most of the recorded macro-fungi were saprophytic, mostly colonizing the litter 38% and wood 34% based substrates, which was followed by soil organic dwelling species (17%). Ecto-mycorrhiza fungi (5%) and parasitic fungi (2%) were the least encountered. The data established that indigenous forests (native ecosystems) hosts a wide range of macrofungi assemblage in terms of density (2.6 individual fruit bodies / m2), species richness (8.3 species / plot) and species diversity (1.49/ plot level) compared to the plantation forest. The Conversion of native forest to plantation forest also interfered with species composition though did not alter species diversity. Seasonality was also shown to significantly affect the diversity of macro-fungi and 61% of the total species being present during the wet season. Based on the present findings, forested ecosystems in Kenya hold diverse macro-fungi community which warrants conservation measures.

Keywords: diversity, Indigenous forest, macro-fungi, plantation forest, season

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Authors: Elizabeth Patricia Beck

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Stepping into various English as foreign language classrooms, one will see some fundamental similarities. There will likely be groups of students working collaboratively, possibly sitting at tables together. They will be using a set coursebook or photocopies of materials developed by publishers or the teacher. The teacher will be carefully monitoring students’ behaviour and progress. The teacher will also likely be insisting that the students only speak English together, possibly having implemented a complex penalty and award systems to encourage this. This is communicative language teaching and it is commonly how foreign languages are taught around the world. Recently, there has been much interest in the codeswitching behaviour of learners in foreign or second language classrooms. It is a significant topic as it relates to second language acquisition theory, language teaching training and policy, and student expectations and classroom practice. Generally in an English as a foreign language context, an ‘English Only’ policy is the norm. This is based on historical factors, socio-political influence and theories surrounding language learning. The trend, however, is shifting and, based on these same factors, a re-examination of language use in the foreign language classroom is taking place. This paper reports the findings of an examination into the codeswitching behaviour of learners with a shared native language in an English classroom. Specifically, it addresses the question of classroom code-switching by adult learners in the EFL classroom during student-to-student, spoken interaction. Three generic categories of code switching are proposed based on published research and classroom practice. Italian adult learners at three levels were observed and patterns of language use were identified, recorded and analysed using the proposed categories. After observations were completed, a questionnaire was distributed to the students focussing on attitudes and opinions around language choice in the EFL classroom, specifically, the usefulness of L1 for specific functions in the classroom. The paper then investigates the relationship between learners’ foreign language proficiency and the frequency and type of code-switching that they engaged in, and the relationship between learners’ attitudes to classroom code-switching and their behaviour. Results show that code switching patterns underwent changes as the students’ level of English language proficiency improved, and that students’ attitudes towards code-switching generally correlated with their behaviour with some exceptions, however. Finally, the discussion focusses on the details of the language produced in observation, possible influencing factors that may affect the frequency and type of code switching that took place, and additional influencing factors that may affect students’ attitudes towards code switching in the foreign language classroom. An evaluation of the limitations of this study is offered and some suggestions are made for future research in this field of study.

Keywords: code-switching, EFL, second language aquisition, adult learners

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Mariamawit T. Belete

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Cereal production and marketing are the means of livelihood for millions of households in Ethiopia. However, cereal production is constrained by technical and socio-economic factors. Among the technical factors, cereal crop diseases are the major contributing factors to the low yield. The aim of this research is to develop an integration of data mining and knowledge based system for sorghum anthracnose disease diagnosis that assists agriculture experts and development agents to make timely decisions. Anthracnose diagnosing systems gather information from Melkassa agricultural research center and attempt to score anthracnose severity scale. Empirical research is designed for data exploration, modeling, and confirmatory procedures for testing hypothesis and prediction to draw a sound conclusion. WEKA (Waikato Environment for Knowledge Analysis) was employed for the modeling. Knowledge based system has come across a variety of approaches based on the knowledge representation method; case-based reasoning (CBR) is one of the popular approaches used in knowledge-based system. CBR is a problem solving strategy that uses previous cases to solve new problems. The system utilizes hidden knowledge extracted by employing clustering algorithms, specifically K-means clustering from sampled anthracnose dataset. Clustered cases with centroid value are mapped to jCOLIBRI, and then the integrator application is created using NetBeans with JDK 8.0.2. The important part of a case based reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution, and retain to store the confirmed solution to the case base for future use. Evaluation of the system was done for both system performance and user acceptance. For testing the prototype, seven test cases were used. Experimental result shows that the system achieves an average precision and recall values of 70% and 83%, respectively. User acceptance testing also performed by involving five domain experts, and an average of 83% acceptance is achieved. Although the result of this study is promising, however, further study should be done an investigation on hybrid approach such as rule based reasoning, and pictorial retrieval process are recommended.

Keywords: sorghum anthracnose, data mining, case based reasoning, integration

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Authors: Abiodun Olayinka, Daniel Dzidzienyo, Pangirayi Tongoona, Samuel Offei, Edwige Gaby Nkouaya Mbanjo, Chiedozie Egesi, Ismail Yusuf Rabbi

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Cassava (Manihot esculenta Crantz) is a major source of starch for various industrial applications. However, the traditional cultivation and harvesting methods of cassava are labour-intensive and inefficient, limiting the supply of fresh cassava roots for industrial starch production. To achieve improved productivity and quality of fresh cassava roots through mechanized cultivation, cassava cultivars with compact plant architecture and moderate plant height are needed. Plant architecture-related traits, such as plant height, harvest index, stem diameter, branching angle, and lodging tolerance, are critical for crop productivity and suitability for mechanized cultivation. However, the genetics of cassava plant architecture remain poorly understood. This study aimed to identify the genetic bases of the relationships between plant architecture traits and productivity-related traits, particularly starch content. A panel of 453 clones developed at the International Institute of Tropical Agriculture, Nigeria, was genotyped and phenotyped for 18 plant architecture and productivity-related traits at four locations in Nigeria. A genome-wide association study (GWAS) was conducted using the phenotypic data from a panel of 453 clones and 61,238 high-quality Diversity Arrays Technology sequencing (DArTseq) derived Single Nucleotide Polymorphism (SNP) markers that are evenly distributed across the cassava genome. Five significant associations between ten SNPs and three plant architecture component traits were identified through GWAS. We found five SNPs on chromosomes 6 and 16 that were significantly associated with shoot weight, harvest index, and total yield through genome-wide association mapping. We also discovered an essential candidate gene that is co-located with peak SNPs linked to these traits in M. esculenta. A review of the cassava reference genome v7.1 revealed that the SNP on chromosome 6 is in proximity to Manes.06G101600.1, a gene that regulates endodermal differentiation and root development in plants. The findings of this study provide insights into the genetic basis of plant architecture and yield in cassava. Cassava breeders could leverage this knowledge to optimize plant architecture and yield in cassava through marker-assisted selection and targeted manipulation of the candidate gene.

Keywords: Manihot esculenta Crantz, plant architecture, DArtseq, SNP markers, genome-wide association study

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Authors: Elidrissi Laila, El Rhaoussi Fatima-Zahra, Haddad Fouad, Tahiri Mohamed, Hliwa Wafaa, Bellabah Ahmed, Badre Wafaa

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Introduction: Metabolic Dysfunction Associated with Non-Alcoholic Fatty Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is the leading cause of chronic liver disease. The cardiometabolic risk factors associated with MASLD represent common health issues and significant public health challenges. Medical students, being active participants in the healthcare system and a young demographic, are particularly relevant for understanding this entity to prevent its occurrence on a personal and collective level. The objective of our study is to assess the level of knowledge among medical students regarding MASLD, its risk factors, and its long-term consequences. Materials and Methods: We conducted a descriptive cross-sectional study using an anonymous questionnaire distributed through social media over a period of 2 weeks. Medical students from various faculties in Morocco answered 22 questions about MASLD, its etiological factors, diagnosis, complications, and principles of treatment. All responses were analyzed using the Jamovi software. Results: A total of 124 students voluntarily provided complete responses. 59% of our participants were in their 3rd year, with a median age of 21 years. Among the respondents, 27% were overweight, obese, or diabetic. 83% correctly answered more than half of the questions, and 77% believed they knew about MASLD. However, 84% of students were unaware that MASLD is the leading cause of chronic liver disease, and 12% even considered it a rare condition. Regarding etiological factors, overweight and obesity were mentioned in 93% of responses, and type 2 diabetes in 84%. 62% of participants believed that type 1 diabetes could not be implicated in MASLD. For 83 students, MASLD was considered a diagnosis of exclusion, while 41 students believed that a biopsy was mandatory for diagnosis. 12% believed that MASLD did not lead to long-term complications, and 44% were unaware that MASLD could progress to hepatocellular carcinoma. Regarding treatment, 85% included weight loss, and 19% did not consider diabetes management as a therapeutic approach for MASLD. At the end of the questionnaire, 89% of the students expressed a desire to learn more about MASLD and were invited to access an informative sheet through a hyperlink. Conclusion: MASLD represents a significant public health concern due to the prevalence of its risk factors, notably the obesity pandemic, which is widespread among the young population. There is a need for awareness about the seriousness of this emerging and long-underestimated condition among young future physicians.

Keywords: MASLD, medical students, obesity, diabetes

Procedia PDF Downloads 49

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

Procedia PDF Downloads 58

Authors: J. D. García Espinel, Paula Pérez Sánchez, Carlos Egea Ruiz, Carlos Lardín Mifsut, Andrés López-Aranguren Oliver

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This paper shortly describes three main actuations over a Waste Water Treatment Plant (WWTP) for reducing its energy consumption: Optimization of the biological reactor in the aeration stage by including new control algorithms and introducing new efficient equipment, the installation of an innovative hybrid system with zero Grid injection (formed by 100kW of PV energy and 5 kW of mini-wind energy generation) and an intelligent management system for load consumption and energy generation control in the most optimum way. This project called RENEWAT, involved in the European Commission call LIFE 2013, has the main objective of reducing the energy consumptions through different actions on the processes which take place in a WWTP and introducing renewable energies on these treatment plants, with the purpose of promoting the usage of treated waste water for irrigation and decreasing the C02 gas emissions. WWTP is always required before waste water can be reused for irrigation or discharged in water bodies. However, the energetic demand of the treatment process is high enough for making the price of treated water to exceed the one for drinkable water. This makes any policy very difficult to encourage the re-use of treated water, with a great impact on the water cycle, particularly in those areas suffering hydric stress or deficiency. The cost of treating waste water involves another climate-change related burden: the energy necessary for the process is obtained mainly from the electric network, which is, in most of the cases in Europe, energy obtained from the burning of fossil fuels. The innovative part of this project is based on the implementation, adaptation and integration of solutions for this problem, together with a new concept of the integration of energy input and operative energy demand. Moreover, there is an important qualitative jump between the technologies used and the alleged technologies to use in the project which give it an innovative character, due to the fact that there are no similar previous experiences of a WWTP including an intelligent discrimination of energy sources, integrating renewable ones (PV and Wind) and the grid.

Keywords: aeration system, biological reactor, CO2 emissions, energy efficiency, hybrid systems, LIFE 2013 call, process optimization, renewable energy sources, wasted water treatment plants

Procedia PDF Downloads 334

Authors: Sharon Garyn-Tal, Shoshana Shahrabani

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Military service in general, and in combat units in particular, can be physically and psychologically stressful. Therefore, type of service may have significant implications for soldiers during and after their military service including emotions, judgments and risk perceptions. Previous studies have focused on risk propensity and risky behavior among soldiers, however there is still lack of knowledge on the impact of type of army service on risk perceptions. The current study examines the effect of type of army service (combat versus non-combat service) and negative incidental emotions on risk perceptions. In 2014 a survey was conducted among 153 combat and non-combat Israeli soldiers. The survey was distributed in train stations and central bus stations in various places in Israel among soldiers waiting for the train/bus. Participants answered questions related to the levels of incidental negative emotions they felt, to their risk perceptions (chances to be hurt by terror attack, by violent crime and by car accident), and personal details including type of army service. The data in this research is unique because military service in Israel is compulsory, so that the Israeli population serving in the army is wide and diversified. The results indicate that currently serving combat participants were more pessimistic in their risk perceptions (for all type of risks) compared to the currently serving non-combat participants. Since combat participants probably experienced severe and distressing situations during their service, they became more pessimistic regarding their probabilities of being hurt in different situations in life. This result supports the availability heuristic theory and the findings of previous studies indicating that those who directly experience distressing events tend to overestimate danger. The findings also indicate that soldiers who feel higher levels of incidental fear and anger have pessimistic risk perceptions. In addition, respondents who experienced combat army service also have pessimistic risk perceptions if they feel higher levels of fear. In addition, the findings suggest that higher levels of the incidental emotions of fear and anger are related to more pessimistic risk perceptions. These results can be explained by the compulsory army service in Israel that constitutes a focused threat to soldiers' safety during their period of service. Thus, in this stressful environment, negative incidental emotions even during routine times correlate with higher risk perceptions. In conclusion, the current study results suggest that combat army service shapes risk perceptions and the way young people control their negative incidental emotions in everyday life. Recognizing the factors affecting risk perceptions among soldiers is important for better understanding the impact of army service on young people.

Keywords: army service, combat soldiers, incidental emotions, risk perceptions

Procedia PDF Downloads 217

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Background: Avian influenza, more commonly known as Bird flu, is a viral zoonotic respiratory disease stemming from various species of poultry, including pets and migratory birds. Researchers have purported that the accessibility of health information online, in addition to the low-cost data collection methods the internet provides, has revolutionized the methods in which epidemiological and disease surveillance data is utilized. This paper examines the feasibility of using internet data sources, such as Twitter and livestock forums, for the early detection of the avian flu outbreak, through the use of text mining algorithms and social network analysis. Methods: Social media mining was conducted on Twitter between the period of 01/01/2021 to 31/12/2021 via the Twitter API in Python. The results were filtered firstly by hashtags (#avianflu, #birdflu), word occurrences (avian flu, bird flu, H5N1), and then refined further by location to include only those results from within the UK. Analysis was conducted on this text in a time-series manner to determine keyword frequencies and topic modeling to uncover insights in the text prior to a confirmed outbreak. Further analysis was performed by examining clinical signs (e.g., swollen head, blue comb, dullness) within the time series prior to the confirmed avian flu outbreak by the Animal and Plant Health Agency (APHA). Results: The increased search results in Google and avian flu-related tweets showed a correlation in time with the confirmed cases. Topic modeling uncovered clusters of word occurrences relating to livestock biosecurity, disposal of dead birds, and prevention measures. Conclusions: Text mining social media data can prove to be useful in relation to analysing discussed topics for epidemiological surveillance purposes, especially given the lack of applied research in the veterinary domain. The small sample size of tweets for certain weekly time periods makes it difficult to provide statistically plausible results, in addition to a great amount of textual noise in the data.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, avian influenza, social media

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Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Zohar Manber, Ran Elkon

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Accumulation of somatic mutations (SMs) in the genome is a major driving force of cancer development. Most SMs in the tumor's genome are functionally neutral; however, some cause damage to critical processes and provide the tumor with a selective growth advantage (termed cancer driver mutations). Current research on functional significance of SMs is mainly focused on finding alterations in protein coding sequences. However, the exome comprises only 3% of the human genome, and thus, SMs in the noncoding genome significantly outnumber those that map to protein-coding regions. Although our understanding of noncoding driver SMs is very rudimentary, it is likely that disruption of regulatory elements in the genome is an important, yet largely underexplored mechanism by which somatic mutations contribute to cancer development. The expression of most human genes is controlled by multiple enhancers, and therefore, it is conceivable that regulatory SMs are distributed across different enhancers of the same target gene. Yet, to date, most statistical searches for regulatory SMs have considered each regulatory element individually, which may reduce statistical power. The first challenge in considering the cumulative activity of all the enhancers of a gene as a single unit is to map enhancers to their target promoters. Such mapping defines for each gene its set of regulating enhancers (termed "set of regulatory elements" (SRE)). Considering multiple enhancers of each gene as one unit holds great promise for enhancing the identification of driver regulatory SMs. However, the success of this approach is greatly dependent on the availability of comprehensive and accurate enhancer-promoter (E-P) maps. To date, the discovery of driver regulatory SMs has been hindered by insufficient sample sizes and statistical analyses that often considered each regulatory element separately. In this study, we analyzed more than 2,500 whole-genome sequence (WGS) samples provided by The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) in order to identify such driver regulatory SMs. Our analyses took into account the combinatorial aspect of gene regulation by considering all the enhancers that control the same target gene as one unit, based on E-P maps from three genomics resources. The identification of candidate driver noncoding SMs is based on their recurrence. We searched for SREs of genes that are "hotspots" for SMs (that is, they accumulate SMs at a significantly elevated rate). To test the statistical significance of recurrence of SMs within a gene's SRE, we used both global and local background mutation rates. Using this approach, we detected - in seven different cancer types - numerous "hotspots" for SMs. To support the functional significance of these recurrent noncoding SMs, we further examined their association with the expression level of their target gene (using gene expression data provided by the ICGC and TCGA for samples that were also analyzed by WGS).

Keywords: cancer genomics, enhancers, noncoding genome, regulatory elements

Procedia PDF Downloads 90

Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

Procedia PDF Downloads 86

Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Razan Salem

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Gender plays a vital role in the stock markets because men and women differ in their behavior when investing in stocks. Accordingly, the role of gender differences in investment behavior is an increasingly important strand in the field of behavioral finance research. The investment behaviors of women relative to men have been examined in the behavioral finance literature, mainly for comparison purposes. Women's roles in the stock market have not been examined in the behavioral finance literature, however, particularly with respect to the Arab region. This study aims to contribute towards a better understanding of the investment behavior of Arab women (in regards to their risk tolerance, investment confidence, and investment literacy levels) relative to Arab men; using a sample from Arab women and men investors living in Saudi Arabia and Jordan. In order to achieve the study's main aim, the researcher used non-parametric tests, as Mann-Whitney U test, along with frequency distribution analysis to analyze the study’s primary data. The researcher distributed close-ended online questionnaires to a sample of 550 Arab male and female individuals investing in stocks in both Saudi Arabia and Jordan. The results confirm that the sample Arab women invest less in stocks compared to Arab men due to their risk-averse behaviors and limited confidence levels. The results also reveal that due to Arab women’s very low investment literacy levels, they fear from taking the risk and invest often in stocks relative to Arab men. Overall, the study’s main variables (risk tolerance, investment confidence, and investment literacy levels) have a combined effect on the investment behavior of Arab women and their limited participation in the stock market. Hence, this study is one of the very first studies that indicate the combined effect of the three main variables (which are usually studied separately in the existing literature) on the investment behavior of women, particularly Arab women. This study makes three important contributions to the growing literature on gender differences in investment behavior. First, while the behavioral finance literature documents evidence on gender differences in investment behaviors in many developed countries, there are very limited studies that investigate such differences in Arab countries. Arab women investors, generally, are ignored from the behavioral finance literature due probably to cultural barriers and data collection difficulties. Thus, this study extends the literature to include Arab women and their investment behaviors when trading stock relative to Arab men. Moreover, the study associates women investment literacy and confidence levels with their financial risk behaviors and participation in the stock market. This study provides direct evidence on Arab women's investment behaviors when trading stocks. Overall, studying Arab women investors is important to investigate whether the investment behavior identified for Western women investors are also found in Arab women investors.

Keywords: Arab women, gender differences, investment behavior, stock markets

Procedia PDF Downloads 162

Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

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Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

Procedia PDF Downloads 137

Authors: Denisa Mackova, Andrea Pokorna

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The management of postoperative pain is a meaningful part of quality care. The experience and knowledge of registered nurses in postoperative pain management can be influenced by local know-how. Therefore, the research helps to understand the cultural differences between two countries with the aim of evaluating the management of postoperative pain management among the nurses from the Czech Republic and the Kingdom of Saudi Arabia. Both countries have different procedures on managing postoperative pain and the research will provide an understanding of both the advantages and disadvantages of the procedures and also highlight the knowledge and experience of registered nurses in both countries. Between the Czech Republic and the Kingdom of Saudi Arabia, the expectation is for differing results in the usage of opioid analgesia for the patients postoperatively and in the experience of registered nurses with Patient Controlled Analgesia. The aim is to evaluate the knowledge and awareness of registered nurses and to merge the data with the postoperative pain management in the early postoperative period in the Czech Republic and the Kingdom of Saudi Arabia. Also, the aim is to assess the knowledge and experience of registered nurses by using Patient Controlled Analgesia and epidural analgesia treatment in the early postoperative period. The criteria for those providing input into the study, are registered nurses, working in surgical settings (standard departments, post-anesthesia care unit, day care surgery or ICU’s) caring for patients in the postoperative period. Method: Research is being conducted by questionnaires. It is a quantitative research, a comparative study of registered nurses in the Czech Republic and the Kingdom of Saudi Arabia. Questionnaire surveys were distributed through an electronic Bristol online survey. Results: The collection of the data in the Kingdom of Saudi Arabia has been completed successfully, with 550 respondents, 77 were excluded and 473 respondents were included for statistical data analysis. The outcome of the research is expected to highlight the differences in treatment through Patient Controlled Analgesia, with more frequent use in the Kingdom of Saudi Arabia. A similar assumption is expected for treatment conducted by analgesia. We predict that opioids will be used more regularly in the Kingdom of Saudi Arabia, whilst therapy through NSAID’s being the most common approach in the Czech Republic. Discussion/Conclusion: The majority of respondents from the Kingdom of Saudi Arabia were female registered nurses from a multitude of nations. We are expecting a similar split in gender between the Czech Republic respondents; however, there will be a smaller number of nationalities. Relevance for research and practice: Output from the research will assess the knowledge, experience and practice of patient controlled analgesia and epidural analgesia treatment. Acknowledgement: This research was accepted and affiliated to the project: Postoperative pain management, knowledge and experience registered nurses (Czech Republic and Kingdom of Saudi Arabia) – SGS05/2019-2020.

Keywords: acute postoperative pain, epidural analgesia, nursing care, patient controlled analgesia

Procedia PDF Downloads 163

Authors: Francesco Lubrano, Fabrizio Bertone, Federico Stirano

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Modern societies strongly depend on Critical Infrastructures (CI). Hospitals, power supplies, water supplies, telecommunications are just few examples of CIs that provide vital functions to societies. CIs like hospitals are very complex environments, characterized by a huge number of cyber and physical systems that are becoming increasingly integrated. Ensuring a high level of security within such critical infrastructure requires a deep knowledge of vulnerabilities, threats, and potential attacks that may occur, as well as defence and prevention or mitigation strategies. The possibility to remotely monitor and control almost everything is pushing the adoption of network-connected devices. This implicitly introduces new threats and potential vulnerabilities, posing a risk, especially to those devices connected to the Internet. Modern medical devices used in hospitals are not an exception and are more and more being connected to enhance their functionalities and easing the management. Moreover, hospitals are environments with high flows of people, that are difficult to monitor and can somehow easily have access to the same places used by the staff, potentially creating damages. It is therefore clear that physical and cyber threats should be considered, analysed, and treated together as cyber-physical threats. This means that an integrated approach is required. SAFECARE, an integrated cyber-physical security solution, tries to respond to the presented issues within healthcare infrastructures. The challenge is to bring together the most advanced technologies from the physical and cyber security spheres, to achieve a global optimum for systemic security and for the management of combined cyber and physical threats and incidents and their interconnections. Moreover, potential impacts and cascading effects are evaluated through impact propagation models that rely on modular ontologies and a rule-based engine. Indeed, SAFECARE architecture foresees i) a macroblock related to cyber security field, where innovative tools are deployed to monitor network traffic, systems and medical devices; ii) a physical security macroblock, where video management systems are coupled with access control management, building management systems and innovative AI algorithms to detect behavior anomalies; iii) an integration system that collects all the incoming incidents, simulating their potential cascading effects, providing alerts and updated information regarding assets availability.

Keywords: cyber security, defence strategies, impact propagation, integrated security, physical security

Procedia PDF Downloads 145

Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

Procedia PDF Downloads 42

Authors: Subhankar Kumar Sarkar

Abstract:

Scarab beetles composing the family Scarabaeidae is one of the largest families in the order Coleoptera. The family is comprised of 11 subfamilies. Of these, the subfamily Scarabaeinae includes 13 tribes globally. Indian species are however considered within 2 tribes Scarabaeini and Coprini. Scarab beetles under this subfamily also known as Coprophagus beetles play an indispensable role in forestry and agriculture. Both adults and larvae of these beetles do a remarkable job of carrying excrement into the soil thus enriching the soil to a great extent. Eastern and North Eastern states of India are heavily rich in diversity of organisms as this region exhibits the tropical rain forests of the eastern Himalayas, which exhibits one of the 18 biodiversity hotspots of the world and one of the three of India. Buxa Tiger Reserve located in Dooars between latitudes 26°30” to 26°55” North & longitudes 89°20” to 89°35” East is one such fine example of rain forests of the eastern Himalayas. Despite this, the subfamily is poorly known, particularly from this part of the globe and demands serious revisionary studies. It is with this background; the attempt is being made to assess the Scarabaeinae fauna of the forest. Both extensive and intensive surveys were conducted in different beats under different ranges of Buxa Tiger Reserve. For collection sweep nets, bush beating and collection in inverted umbrella, hand picking techniques were used. Several pit fall traps were laid in the collection localities of the Reserve to trap ground dwelling scarabs. Dung of various animals was also examined to make collections. In the evening hours UV light, trap was used to collect nocturnal beetles. The collected samples were studied under Stereozoom Binocular Microscopes Zeiss SV6, SV11 and Olympus SZ 30. The faunistic investigation of the forest revealed in the recognition of 19 species under 6 genera distributed over 2 tribes. Of these Heliocopris tyrannus Thomson, 1859 was recorded new from the Country, while Catharsius javanus Lansberge, 1886, Copris corpulentus Gillet, 1910, C. doriae Harold, 1877 and C. sarpedon Harold, 1868 from the state. 4 species are recorded as endemic to India. The forest is dominated by the members of the Genus Onthophagus, of which Onthophagus (Colobonthophagus) dama (Fabricius, 1798) is represented by highest number of individuals. Their seasonal distribution is most during Premonsoon followed by Monsoon and Postmonsoon. Zoogeographically all the recorded species are of oriental distribution.

Keywords: buxa tiger reserve, diversity, India, new records, scarabaeinae, scarabaeidae

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