Search results for: deep gaining knowledge of

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Kevin Wongleedee, Atcharapun Daiporn

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This research paper was aimed to examine the level of satisfaction of the faculty and staff members who participated in the ASEAN camp. The population of this study included all the faculty and staff members who participated in the activities of the ASEAN camp during January 2014. Based on 106 faculty and staff members who answered the questionnaire, the data were complied by using SPSS. Mean and standard deviation were utilized in analyzing the data. The findings revealed that the average mean of satisfaction was 4.16, and standard deviation was 0.6634. Moreover, the mean average can be used to rank the level of satisfaction from each of the following factors: useful knowledge, technique of explaining knowledge, understanding materials, appropriateness of knowledge, document available, time of activities, service from staff, and public relation.

Keywords: ASEAN camp, customer, satisfaction, faculty and staff members

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Authors: Majid Bagherinia

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Lime and cement are frequently used as binders in the Deep Mixing Method (DMM) to improve soft clay soils. The most significant disadvantages of these materials are carbon dioxide emissions and the consumption of natural resources. In this study, three different biopolymers, guar gum, locust bean gum, and sodium alginate, were investigated for the improvement of soft clay using DMM. In the experimental study, the effects of the additive ratio and curing time on the Unconfined Compressive Strength (UCS) of stabilized specimens were investigated. According to the results, the UCS values of the specimens increased as the additive ratio and curing time increased. The most effective additive was sodium alginate, and the highest strength was obtained after 28 days.

Keywords: deep mixing method, soft clays, ground improvement, biopolymers, unconfined compressive strength

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Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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Authors: A. A. AlRasheed, A. Atkins, R. Campion

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This paper focuses on using knowledge management and visualisation concepts to improve the patients and hospitals employee’s workflow. Hospitals workflow is a complex and complicated process and poor patient flow can put both patients and a hospital’s reputation at risk, and can threaten the facility’s financial sustainability. Healthcare leaders are under increased pressure to reduce costs while maintaining or increasing patient care standards. In this paper, a framework is proposed to help improving patient experience, staff satisfaction, and operational efficiency across hospitals by using knowledge management based visualisation concepts. This framework is using real-time visibility to track and monitor location and status of patients, staff, rooms, and medical equipment.

Keywords: knowledge management, improvements, visualisation, workflow

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

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Authors: Rosalina Penaloza, Joanna Navarro, Pauline Jolly, Anna Junkins, Carlos Seas, Larissa Otero

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Background: Tuberculosis (TB) case detection in Peru relies on passive case finding. This strategy relies on the assumption that the community is aware that a persistent cough is a possible symptom of TB and that formal health care needs to be sought. Despite its importance, health knowledge specific to TB is underexplored in Peru. This study aimed to assess health literacy and level of TB knowledge among outpatients attending a referral hospital in Lima, Peru. The goal was to ascertain knowledge gaps in key areas relating to TB, to identify and prioritize subgroups for intervention, and to provide insight for policy and community interventions considering health literacy. Methods: An observational cross-sectional study was conducted using a survey to measure sociodemographic factors, tuberculosis knowledge, and health literacy. Bivariate and Multivariate logistic regression was performed to study the associations between variables and to account for potential confounders. The study was conducted at Hospital Cayetano Heredia in Lima, Peru from June – August 2017. Results: 272 participants were included in the analysis. 57.7% knew someone who had had TB before, 9% had had TB in the past. Two weeks a cough was correctly identified as a symptom that could be TB by 69.1%. High TB knowledge was found among 149 (54.8%) participants. High health literacy was found among 193 (71.0%) participants. Health literacy and TB knowledge were not significantly associated (OR 0.9 (95%CI 0.5-1.5)). After controlling for sex, age, district, education, health insurance, frequency of hospital visits and previous TB diagnosis: High TB knowledge was associated with knowing someone with TB (aOR 2.7 (95%CI 1.6-4.7)) and being a public transport driver, (aOR 0.2 (95%CI 0.05-0.9)). Not being poor was the single factor associated with high health literacy (aOR 3.8 (95%CI 1.6-8.9)). Conclusions: TB knowledge was fair, though 30% did not know the most important symptom of TB. Tailoring educational strategies to risk groups may enhance passive case detection especially amongst transport workers in Lima, Peru.

Keywords: health literacy, Peru, tuberculosis, tuberculosis knowledge

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Authors: A. M. Omoare, E. O. Fakoya

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This study sought to determine the use of indigenous knowledge for selected arable crops production in Ondo Sate. A multistage sampling method was used and 112 arable crops farmers were systematically selected. Data were analyzed using both descriptive and inferential statistics. The results showed that majority of the sampled farmers were male (75.90%). About 75% were married with children. Large proportion of them (62.61%) were within the ages of 30-49 years. Most of them have spent about 10 years in farming (58.92%). The highest raw scores of use of indigenous knowledge were found in planting on mound in yam production, use of native medicine and scare crow method in controlling birds in rice production, timely planting of locally developed resistant varieties in cassava production and soaking of maize seeds in water to determine their viability with raw scores of 313, 310, 305, 303, and 300 respectively, while the lowest raw scores was obtained in use of bell method in controlling birds in rice production with raw scores of 210. The findings established that proverbs (59.8%) and taboos (55.36%) were the most commonly used media in transmitting indigenous knowledge by arable crop farmers. The multiple regression analysis result revealed that age of the farmers and farming experience had a significant relationship with the use of indigenous knowledge of the farmers which gave R2 = 0.83 for semi log function form of equation which is the land equation. The policy implication is that indigenous knowledge should provide basis for designing modern technologies to enhance sustainable agricultural development.

Keywords: crop production, extent of use, indigenous knowledge, arable crops

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Authors: Mohammad R. Jafari, Francois G. Bernardeau

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With its arid climate, Qatar experiences low annual rainfall, intense storms, and high evaporation rates. However, the fast-paced rate of infrastructure development in the capital city of Doha has led to recurring instances of surface water flooding as well as rising groundwater levels. Public Work Authority (PWA/ASHGHAL) has implemented an approach to collect and discharge the flood water into a) positive gravity systems; b) Emergency Flooding Area (EFA) – Evaporation, Infiltration or Storage off-site using tankers; and c) Discharge to deep injection wells. As part of the flood prevention scheme, 21 deep injection wells have been constructed to discharge the collected surface and groundwater table in Doha city. These injection wells function as an alternative in localities that do not possess either positive gravity systems or downstream networks that can accommodate additional loads. These injection wells are 400-m deep and are constructed in a complex karstic subsurface condition with large cavities. The injection well system will discharge collected groundwater and storm surface runoff into the permeable Umm Er Radhuma Formation, which is an aquifer present throughout the Persian Gulf Region. The Umm Er Radhuma formation contains saline water that is not being used for water supply. The injection zone is separated by an impervious gypsum formation which acts as a barrier between upper and lower aquifer. State of the art drilling, grouting, and geophysical techniques have been implemented in construction of the wells to assure that the shallow aquifer would not be contaminated and impacted by injected water. Injection and pumping tests were performed to evaluate injection well functionality (injectability). The results of these tests indicated that majority of the wells can accept injection rate of 200 to 300 m³ /h (56 to 83 l/s) under gravity with average value of 250 m³ /h (70 l/s) compared to design value of 50 l/s. This paper presents design and construction process and issues associated with these injection wells, performing injection/pumping tests to determine capacity and effectiveness of the injection wells, the detailed design of collection system and conveying system into the injection wells, and the operation and maintenance process. This system is completed now and is under operation, and therefore, construction of injection wells is an effective option for flood control.

Keywords: deep injection well, flood prevention scheme, geophysical tests, pumping and injection tests, wellhead assembly

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Authors: A. Bouzabata, A. Boukhari

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Curcuma longa L. (Zingiberaceae), commonly known as turmeric, has a long history of traditional uses for culinary purposes as a spice and a food colorant. The present study aimed to document the ethnobotanical knowledge about Curcuma longa and to assess the variation in the herbalists’ experience in Northeastern Algeria. Data were collected by semi-structured questionnaires and direct interviews with 30 herbalists. Ethnobotanical indices, including the fidelity level (FL%), the relative frequency citation (RFC) and use value (UV) were determined by quantitative methods. Diversity in the knowledge was analyzed using univariate, non-parametric and multivariate statistical methods. Three main categories of uses were recorded for C. longa: for food, for medicine and for cosmetic purposes. As a medicine, turmeric was used for the treatment of gastrointestinal, dermatological and hepatic diseases. Medicinal and food uses were correlated with both forms of use (rhizome and powder). The age group did not influence the use. Multivariate analyses showed a significant variation in traditional knowledge, associated with the use value, origin, quality and efficacy of the drug. These findings suggested that the geographical origin of C. longa affected the use in Algeria.

Keywords: curcuma, indices, knowledge, variation

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Authors: Luojie Tang

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Riverside space is an important public space and ecological corridor in urban areas, but mountainous urban rivers are often overlooked due to their deep valleys and poor accessibility. This article takes the Qing Shui Xi River in Chongqing as an example, and through long-term field inspections, measurements, interviews, and online surveys, summarizes the problems of poor accessibility, limited space for renovation, lack of waterfront facilities, excessive artificial intervention, low average runoff, severe river water pollution, and difficulty in integrated watershed management in riverside space. Based on the current situation and drawing on relevant experiences, this article summarizes the design methods for riverside space in deep valley rivers in mountainous urban areas. Regarding spatial design techniques, the article emphasizes the importance of integrating waterfront spaces into the urban public space system and vertical linkages. Furthermore, the article suggests different design methods and improvement strategies for the already developed areas and new development areas. Specifically, the article proposes a planning and design strategy of "protection" and "empowerment" for new development areas and an updating and transformation strategy of "improvement" and "revitalization" for already developed areas. In terms of ecological restoration methods, the article suggests three focus points: increasing the runoff of urban rivers, raising the landscape water level during dry seasons, and restoring vegetation and wetlands in the riverbank buffer zone while protecting the overall pattern of the watershed. Additionally, the article presents specific design details of the Qingshuixi River to illustrate the proposed design and restoration techniques.

Keywords: deep-cut river, design method, mountainous city, Qingshuixi river in Chongqing, waterfront space design

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Authors: Gilbert Makanda, Roelf Sypkens

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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.

Keywords: differential equations, knowledge acquisition, least squares nonlinear, dynamical systems

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

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Authors: Ahmed Negm, George Aggidis, Xiandong Ma

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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.

Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management

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Authors: Hamnah Nisar

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As food is something which is particularly important for survival, in fact, it improves the quality of life and promotes health. Quality food is a key to a healthy life. Consumption of food depends on the knowledge we have regarding the nutrients it contains. Moreover, the awareness and knowledge about something is an initial stage for its improvement. We cannot work on anything unless we have knowledge about it. The pros and cons, effects, causes, dos, and don'ts, especially for an important things like food, are a necessity to learn. That is why my research would be all about analyzing what difference awareness makes on people and how making people more aware about a certain thing can help them improve their lifestyles and bring a positive change for them. The research would be done through questionnaires and interviews among two classes, one would be the upper class and the other would-be lower class. Because the upper class can easily access learning facilities and can know about the new things than the lower class. The questions would be related to what kind of food do they consume, what health issues they face, or what health issues are common among their regions. The results of the research would be helpful to know firstly the effects of awareness and education regarding food on health, how a basic thing like knowledge can have a significant effect on health and can be the cause of several diseases.

Keywords: nutrition, awareness, quality food, knowledge

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Authors: Muhammad Sollehhuddin A. Jalil, Mohd Ibrahim Shapiai, Rubiyah Yusof

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Nowadays, ontology is common in many areas like artificial intelligence, bioinformatics, e-commerce, education and many more. Ontology is one of the focus areas in the field of Information Retrieval. The purpose of an ontology is to describe a conceptual representation of concepts and their relationships within a particular domain. In other words, ontology provides a common vocabulary for anyone who needs to share information in the domain. There are several ontology domains in various fields including engineering and non-engineering knowledge. However, there are only a few available ontology for engineering knowledge. Fuzzy logic as engineering knowledge is still not available as ontology domain. In general, fuzzy logic requires step-by-step guidelines and instructions of lab experiments. In this study, we presented domain ontology for Fuzzy Logic Control (FLC) knowledge. We give Table of Content (ToC) with middle strategy based on the Uschold and King method to develop FLC ontology. The proposed framework is developed using Protégé as the ontology tool. The Protégé’s ontology reasoner, known as the Pellet reasoner is then used to validate the presented framework. The presented framework offers better performance based on consistency and classification parameter index. In general, this ontology can provide a platform to anyone who needs to understand FLC knowledge.

Keywords: engineering knowledge, fuzzy logic control ontology, ontology development, table of content

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Authors: Sisay Tamrat Ayalew

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This paper aims to explore the two contradictory debates about the educational philosophy of universities in the context of Ethiopia. It also explores how the understanding of the philosophy of the university is shaped by environmental factors and exacerbates the marginalization of youth. On the one hand, universities are perceived as places where knowledge can solve a certain practical problem. On the other hand, universities are places where knowledge is produced and disseminated irrespective of its immediate use value. This study found that understanding the philosophy of education varies across time and place. In the Ethiopian context, there is neither a disinterested pursuit of knowledge nor an instrumentalist epistemology nor a synthesis of the two. It disregards the value of knowledge altogether and overly simplifies the philosophy of instrumentalist epistemology to the extent of buying and selling certificates, even in the absence of formal training.

Keywords: philosophy of universities, marginalized youth, diploma mill, instrumentalist epistemology, disinterested pursuit

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Authors: Amira Khelil, Habib Affes

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Organization resource planning implementation success is important. Today`s competitors in business, in enterprise resource planning and in managing are becoming one of the main tools of achieving competitiveness in business. Resource technologies are considered as an infrastructure to create and maintain business to improve front and back-office efficiency and effectiveness. This study is significant to bring new ideas in determining the key antecedents which are technological resource planning implementation based on knowledge capacity perspectives and help to understand the key success factor in the Tunisian banks. Based on a survey of 150 front office Tunisian agents working in Tunisian banks with mixed capital, using Groupware system, only 51 respondents had given feedback to this survey. By using Warp PLS 3.0, through several tests the relationship between knowledge capability and Groupware implementation success having beta coefficient 0.37 and P-Value <0.01. This result highlights that knowledge capability of bank agent can influence the success of the Groupware implementation.

Keywords: groupware implementation, knowledge capacity, partial least squares method, Tunisian banks

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Authors: R. Alias, A. Kasa, M. R. Taha

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The effect of particle size on shear strength of granular materials are investigated using direct shear tests. Small direct shear test (60 mm by 60 mm by 24 mm deep) were conducted for particles passing the sieves with opening size of 2.36 mm. Meanwhile, particles passing the standard 20 mm sieves were tested using large direct shear test (300 mm by 300 mm by 200 mm deep). The large direct shear tests and the small direct shear tests carried out using the same shearing rate of 0.09 mm/min and similar normal stresses of 100, 200, and 300 kPa. The results show that the peak and residual shear strength decreases as particle size increases.

Keywords: particle size, shear strength, granular material, direct shear test

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Maria Granados

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Information technology (IT) facilitates the management of knowledge in organisations through the effective leverage of collective experience and knowledge of employees. This supports information processing needs, as well as enables and facilitates sense-making activities of knowledge workers. The study of IT support for knowledge management (KM) has been carried out mainly in larger organisations where resources and competitive conditions can trigger the use of KM. However, there is still a lack of understanding on how IT can support the management of knowledge under different organisational settings influenced by: constant tensions between social and economic objectives, more focus on sustainability than competiveness, limited resources, and high levels of democratic participation and intrinsic motivations among employees. All these conditions are presented in Social Enterprises (SEs), which are normally micro and small businesses that trade to tackle social problems, improve communities, people’s life chances, and the environment. Thus, their importance to society and economies is increasing. However, there is still a need for more understanding of how these organisations operate, perform, innovate and scale-up. This knowledge is crucial to design and provide accurate strategies to enhance the sector and increase its impact and coverage. To obtain a conceptual and empirical understanding of how IT can facilitate KM in the particular organisational conditions of SEs, a quantitative study was conducted with 432 owners and senior members of SEs in UK, underpinned by 21 interviews. The findings demonstrated how IT was supporting more the recovery and storage of necessary information in SEs, and less the collaborative work and communication among enterprise members. However, it was established that SEs were using cloud solutions, web 2.0 tools, Skype and centralised shared servers to manage informally their knowledge. The possible impediments for SEs to support themselves more on IT solutions can be linked mainly to economic and human constraints. These findings elucidate new perspectives that can contribute not only to SEs and SE supporters, but also to other businesses.

Keywords: social enterprises, knowledge management, information technology, collaboration, small firms

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Authors: A. Capodieci, G. Del Fiore, L. Mainetti

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Recently, the use of web 2.0 tools has increased in companies and public administration organizations. This phenomenon, known as "Enterprise 2.0", has, de facto, modified common organizational and operative practices. This has led “knowledge workers” to change their working practices through the use of Web 2.0 communication tools. Unfortunately, these tools have not been integrated with existing enterprise information systems, a situation that could potentially lead to a loss of information. This is an important problem in an organizational context, because knowledge of information exchanged within the organization is needed to increase the efficiency and competitiveness of the organization. In this article we demonstrate that it is possible to capture this knowledge using collaboration processes, which are processes of abstraction created in accordance with design patterns and applied to new organizational operative practices.

Keywords: business practices, business process patterns, collaboration tools, enterprise 2.0, knowledge workers

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Authors: Solomon Ngoako Mabapa, Selekane Ananias Motadi, Nteseng Mailula, Hlekani Vanessa Mbhatsani, Lindelani Fhumudzani Mushaphi

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Background: South Africa has indeed made good progress towards IDD elimination, as far as implementation of salt iodization and coverage of iodized salt are concerned, the education and promotion aspects of the iodized salt intervention are seriously lacking. Objective: To determine the iodine nutritional knowledge of food handlers at primary schools under the National School Nutrition Programme in Capricorn and Waterberg district. Design: This study included 300 food handlers recruited from 95 primary schools in Capricorn district and 105 primary schools in Waterberg district, Limpopo Province, South Africa. Primary schools and study participants where conveniently selected. The data was collected by means of a structured questionnaire. Information obtained was on the socio-demographic characteristics of the participants, general knowledge on salt fortification and knowledge test. Results: The iodine knowledge for the food handlers in two districts was poor with the entire population’s iodine nutritional knowledge of 12% on the Lickert scale. The mean score on the Lickert scale for Capricorn and Waterberg districts was 17% and 8.6% respectively indicated poor iodine nutritional knowledge. Conclusion: The two districts had poor iodine nutritional knowledge. Giving nutrition education to the public on the importance of iodine and the consequences of iodine deficiency disorder (IDD) and continue advocacy on mass media on the iodine fortification as an intervention strategy to combat the escalating problem of micronutrient malnutrition control.

Keywords: food handlers, nutritional knowledge, iodine, National School Nutrition Programme

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Lali Khurtsia, Vano Tsertsvadze

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The purpose of the study was to find out incentives and expectations, methods and ways, which are influential to students during working with their thesis. Research findings shows that the use of plagiarism has cultural links deep in the history - on the one hand, the tradition of sharing knowledge in the oral manner, with its different interpretations, and on the other hand the lack of fair and honest methods in the academic process. Research results allow us to determine general ideas about preventive policy to reduce the use of plagiarism. We conducted surveys in three different groups – we interviewed so-called diploma writers, students on bachelors and masters level and the focus group of lecturers. We found that the problem with plagiarism in Georgia has cultural-mental character. We think that nearest years’ main task should be breaking of barriers existed between lecturers and students and acknowledgement of honest principals of study process among students and pupils.

Keywords: education, Georgia, plagiarism, study process, school, university

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Authors: Reggie Chandra

Abstract:

Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Sergio Mauricio Pérez López, Luis Rodrigo Valencia Pérez, Juan Manuel Peña Aguilar, Adelina Morita Alexander

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The culture of continuous improvement in organizations is very important as it represents a source of competitive advantage. This article discusses the transfer of knowledge between companies which formed cross-functional teams and used a dynamic model for knowledge creation as a framework. In addition, the article discusses the structure of cognitive assets in companies and the concept of "stickiness" (which is defined as an obstacle to the transfer of knowledge). The purpose of this analysis is to show that an improvement in the attitude of individual members of an organization creates opportunities, and that an exchange of information and knowledge leads to generating continuous improvements in the company as a whole. This article also discusses the importance of creating the proper conditions for sharing tacit knowledge. By narrowing gaps between people, mutual trust can be created and thus contribute to an increase in sharing. The concept of adapting knowledge to new environments will be highlighted, as it is essential for companies to translate and modify information so that such information can fit the context of receiving organizations. Adaptation will ensure that the transfer process is carried out smoothly by preventing "stickiness". When developing the transfer process on cross-functional teams (as opposed to working groups), the team acquires the flexibility and responsiveness necessary to meet objectives. These types of cross-functional teams also generate synergy due to the array of different work backgrounds of their individuals. When synergy is established, a culture of continuous improvement is created.

Keywords: knowledge transfer, continuous improvement, teamwork, cognitive assets

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Authors: Gonul Kurt, Semra Aciksoz

Abstract:

All types of interventions that increase the knowledge and awareness of youths about Sexually Transmitted Diseases (STD) are considered to be important for safe sex life and sexual health. The aim of this study was to determine the knowledge levels of nursing students about STD and evaluate the effectiveness of peer education and brochure methods to increase the knowledge and awareness about STD. This interventional study was carried out by participation of nursing students attending the first and second grade in a school of nursing on February–May 2015. The study participants were 200 undergraduate nursing student volunteers. The students were given education by peer trainers and brochure methods. First-grade students were divided into five groups with block randomization method and each group were given education by five peer trainers. Second-grade students were given education with brochure by the researchers. The knowledge level of study groups was evaluated before and after educational intervention. The data were collected using the “Data Collection Form” and “Sexually Transmitted Diseases Information Form”. The questionnaire forms developed by the researchers after the literature review. The SPSS 15.0 package software was used for the evaluation of the data obtained from the study. Data were analyzed by Mann-Whitney-U-Test, Wilcoxon Signed Ranks Test and Mc Nemar Test. A p value of <0.05 was regarded as statistically significant. All of participants in the study were female nursing students. The mean age of students was 18.99±0.32 years old in the peer education group and 20.04±0.37 in the brochure education group. There was no statistically significant difference between knowledge levels of the students in both groups before the education (p>0.05). It was determined that an increase in knowledge levels of the students in both groups after the education. This increase was statistically significant (p<0.05). It was determined that knowledge level of the students about STD in brochure group was higher than the peer education group (p<0.001). The results of this study indicate that brochure education method was more effective than the peer education method in both increasing knowledge and awareness about STD.

Keywords: education method, knowledge, nursing students, sexually transmitted diseases

Procedia PDF Downloads 286