Search results for: continuous learning

Authors: Yang Ye, Hongfei Qiu, Yaqi Li

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The planning and construction of traditional cities are oriented by cars, which leads to the problems of insufficient urban public space, fragmentation, and low utilization efficiency. With the development of driverless technology, the urban structure will change from the traditional single-core grid structure to the multi-core model. In terms of traffic organization, with the release of land for traffic facilities, public space will become more continuous and integrated with traffic space. In the context of driverless technology, urban public reconstruction is characterized by modularization and high efficiency, and its planning and layout features accord with points (service facilities), lines (smart lines), surfaces (activity centers). The public space of driverless urban roads will provide diversified urban public facilities and services. The intensive urban layout makes the commercial public space realize the functions of central activities and style display, respectively, in the interior (building atrium) and the exterior (building periphery). In addition to recreation function, urban green space can also utilize underground parking space to realize efficient dispatching of shared cars. The roads inside the residential community will be integrated into the urban landscape, providing conditions for the community public activity space with changing time sequence and improving the efficiency of space utilization. The intervention of driverless technology will change the thinking of traditional urban construction and turn it into a human-oriented one. As a result, urban public space will be richer, more connected, more efficient, and the urban space justice will be optimized. By summarizing the frontier research, this paper discusses the impact of unmanned driving on cities, especially urban public space, which is beneficial for landscape architects to cope with the future development and changes of the industry and provides a reference for the related research and practice.

Keywords: driverless, urban public space, construction strategy, urban design

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Authors: Sareh Rajabi, Taha Anjamrooz, Salwa Bheiry

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The continuous increase in the world population, resource shortage and the warning of climate change cause various environmental and social issues to the world. Thus, sustainability concept is much needed nowadays. Organizations are progressively falling under strong worldwide pressure to integrate sustainability practices into their project decision-making development. Construction projects in the industry are amongst the most significant, since it is one of the biggest divisions and of main significance for the national economy and hence has a massive effect on the environment and society. So, it is important to discover approaches to incorporate sustainability into the management of those projects. This study presents a combined sustainability index of projects with sustainable transportation which has been formed as per a comprehensive literature review and survey study. Transportation systems enable the movement of goods and services worldwide, and it is leading to economic growth and creating jobs while creating negative impacts on the environment and society. This research is study to quantify the sustainability indicators, through 1) identifying the importance of sustainable transportation indicators that are based on the sustainable practices used for the construction projects and 2) measure the effectiveness of practices through these indicators on the three sustainable pillars. A total 26 sustainability indicators have been selected and grouped under each related sustainability pillars. A survey was used to collect the opinion about the sustainability indicators by a scoring system. A combined sustainability index considering three sustainable pillars can be helpful in evaluating the transportation sustainable practices of a project and making decisions regarding project selection. In addition to focus on the issue of financial resource allocation in a project selection, the decision-maker could take into account the sustainability as an important key in addition to the project’s return and risk. The purpose of this study is to measure the performance of transportation sustainability which allow companies to assess multiple projects selection. This is useful to decision makers to rank and focus more on future sustainable projects.

Keywords: sustainable transportation, transportation performances, sustainable indicators, sustainable construction practice, sustainability

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Authors: Hepinda Fajari Nuharini, Sugiarti A. Musabiq

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Background: Various and continuous pressures that exceed individual resources can cause first-year undergraduate college students to experience psychological distress. Psychological distress can occur when individuals use rumination as cognitive coping strategies. Rumination is one of the cognitive coping strategies that can be used by individuals to respond to psychological distress that causes individuals to think about the causes and consequences of events that have occurred. Rumination had two subtypes, such as brooding and reflective. Therefore, the purpose of this study was determining the role of brooding and reflective as subtypes of rumination toward psychological distress in University of Indonesia first-year undergraduate students. Methods: Participants of this study were 403 University of Indonesia first-year undergraduate students aged between 18 and 21 years old. Psychological distress measured using self reporting questionnaire (SRQ-20) and brooding and reflective as subtypes of rumination measured using Ruminative Response Scale - Short Version (RRS - Short Version). Results: Binary logistic regression analyses showed that 22.8% of the variation in psychological distress could be explained by the brooding and reflective as subtypes of rumination, while 77.2% of the variation in psychological distress could be explained by other factors (Nagelkerke R² = 0,228). The results of the binary logistic regression analysis also showed rumination subtype brooding is a significant predictor of psychological distress (b = 0,306; p < 0.05), whereas rumination subtype reflective is not a significant predictor of psychological distress (b = 0,073; p > 0.05). Conclusion: The findings of this study showed a positive relationship between brooding and psychological distress indicates that a higher level of brooding will predict higher psychological distress. Meanwhile, a negative relationship between reflective and psychological distress indicates a higher level of reflective will predict lower psychological distress in University of Indonesia first-year undergraduate students. Added Values: The psychological distress among first-year undergraduate students would then have an impact on student academic performance. Therefore, the results of this study can be used as a reference for making preventive action to reduce the percentage and impact of psychological distress among first-year undergraduate students.

Keywords: brooding as subtypes of rumination, first-year undergraduate students, psychological distress, reflective as subtypes of rumination

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Jack Santiago Monell

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In America, misrepresentations of black males have been perpetuated throughout the history of popular culture. Because of these narratives, varying communities have developed biases and stereotypes about what black male masculinity represents and more importantly, how they respond to them. The researcher explored the perspectives of police officers in the following states, Maryland, Pennsylvania, and North Carolina. Because of the nature of police and community relations, and national attention to high profile cases, having officers provide context into how black males are viewed from their lens, was critical while expanding on the theoretical explanations to describe attitudes towards police confrontations. As one of the objectives was to identify specific themes relevant to why police officers may view African American males differently, hence, responding more aggressively, this proved to be the most beneficial method of initial analysis to identify themes. The following nodes (appearance, acting suspicious/ troublesome behavior, upbringing about black males, excessive force) were identified to analyze the transcripts to discern associations. The data was analyzed through NVivo 11, and several themes resulted to elaborate on the data received. In analyzing the data, four themes were identified: appearance, acting suspicious/ troublesome behavior, upbringing about black males, and excessive force. The data conveyed that continuous stereotypes about African American men will ultimately result in excessive use of force or pervasive shootings, albeit the men are armed or unarmed. African American males are consistently targeted because of their racial makeup and appearance over any other probable circumstances. As long as racial bias and stereotypical practices continue in policing, African American males will endlessly be unjustly targeted and at times, the victims of violent encounters with police officers in the United States.

Keywords: African American males, police perceptions, masculinity, popular culture

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Authors: Somruay Apichatibutarapong, Chookiat Pudprommart

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The purpose of this study was to develop of statistical analysis by using R programming via internet applied for agriculture experimental design. Data were collected from 65 items in completely randomized design, randomized block design, Latin square design, split plot design, factorial design and nested design. The quantitative approach was used to investigate the quality of learning media on statistical analysis by using R programming via Internet by six experts and the opinions of 100 students who interested in experimental design and applied statistics. It was revealed that the experts’ opinions were good in all contents except a usage of web board and the students’ opinions were good in overall and all items.

Keywords: experimental design, r programming, applied statistics, statistical analysis

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Authors: Dev Pratap Singh, Harshika Hasija, Ashwini S.

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computers using hand gestures and voice commands. The system leverages advanced computer vision techniques using the Media Pipe framework and OpenCV to detect and interpret real-time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the speech recognition library allows for seamless execution of tasks like web searches, location navigation, and gesture control in the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks, natural language processing, voice assistant

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Authors: Keith Schofield, Leigh Morland

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Storytelling is an established means of leadership and management development and is also considered a form of leadership of self and others in its own right. This study focuses on the utility of storytelling in the development of management narratives in an MBA programme; sources include programme participants as well as international recruiters, whose voices are often only heard in terms of economic contribution and globalisation. For many MBA candidates, the return to study requires the development of a new identity which complements their professional identity; each candidate has their own journey and expectations, the use of story can enable candidates to explore their aspirations and assumptions and give voice to previously unspoken ideas. For international recruitment, the story of market development and change must be captured if MBAs are to remain fit for purpose. If used effectively, story acts as a form of critical reflection that can inform the learning journeys of individuals, emerging identities as well as the ongoing design and development of programmes. The landscape of management education is shifting; the MBA begins to attract a different kind of candidate, some are younger than before, others are seeking validation for their existing work practices, yet more are entrepreneurial and wish to capitalise on an institutional experience to further their career. There is a shift in context, creating uncertainty and ambiguity for programme managers and recruiters, thus requiring institutions to create a new MBA narrative. This study utilises Lego SeriousPlay as the means to engaging programme participants and international agents in telling the story of their MBA. We asked MBA participants to tell the story of their leadership and management aspirations and compare these to stories of their development journeys, allowing for critical reflection of their respective development gaps. We asked international recruiters, who act as university agents and promote courses in the student’s country of origin, to explore their mental models of MBA candidates and their learning agenda. The purpose of this process was to explore the agent’s perception of the MBA programme and to articulate the student journey from a recruitment perspective. The paper’s unique contribution is in combining these stories in order to explore the assumptions that determine programme design. Data drawn from reflective statements together with images of Lego ‘builds’ created the opportunity for reflection between the mental models of these groups. Findings will inform the design of the MBA journey and experience; we review the extent to which the changing identities of learners are congruent with programme design. Data from international recruiters also determines the extent to which marketing and recruitment strategies identify with would be candidates.

Keywords: critical reflection, programme management, recruitment, storytelling

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Authors: Wen-Jye Shyr, Chun-Yuan Chang, Bo-Lin Wei, Chia-Ming Lin

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The task of creating an augmented reality technology was described in this study to give operators a user interface that might be a part of an automated storage and retrieval system. Its objective was to give graduate engineering and technology students a system of tools with which to experiment with the creation of augmented reality technologies. To collect and analyze data for maintenance applications, the students used augmented reality technology. Our findings support the evolution of artificial intelligence towards Industry 4.0 practices and the planned Industry 4.0 research stream. Important first insights into the study's effects on student learning were presented.

Keywords: augmented reality, storage and retrieval system, user interface, programmable logic controller

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Authors: Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar

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In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.

Keywords: early stage prediction, heart rate variability, linear and non-linear analysis, sudden cardiac death

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Authors: Hamid Gholami

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During the years, the language teaching methods have been the offshoots of schools of thought in psychology. The methods were mainly influenced by their contemporary psychological approaches, as Audiolingualism was based on behaviorism and Communicative Language Teaching on constructivism. In 1950s, the text books were full of repetition exercises which were encouraged by Behaviorism. In 1980s they got filled with communicative exercises as suggested by constructivism. The trend went on to nowadays that sees no specific method as prevalent since none of the schools of thought seem to be illustrative of the complexity in human being learning. But some changes can be notable; some textbooks are giving more and more space to repetition exercises at least to enhance some aspects of language proficiency, namely collocations, rhythm and intonation, and conversation models. These changes may mark the reemergence of one of the once widely accepted schools of thought in psychology; behaviorism.

Keywords: language teaching methods, psychology, schools of thought, Behaviorism

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Authors: Emile Niringiyimana, LI Ji Qing, Giovanni Dushimimana, Virginie Umwere

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The development and utilization of renewable energy (RE) can not only effectively reduce carbon dioxide (CO2) emissions, but also became a solution to electricity shortage mitigation in rural areas. Hybrid RE systems are promising ways to provide consistent and continuous power for isolated areas. This work investigated the prospect and cost effectiveness of hybrid system complementarity between a 100kW solar PV system and a small-scale 200kW hydropower station in the South of Rwanda. In order to establish the optimal size of a RE system with adequate sizing of system components, electricity demand, solar radiation, hydrology, climate data are utilized as system input. The average daily solar radiation in Rukarara is 5.6 kWh/m2 and average wind speed is 3.5 m/s. The ideal integrated RE system, according to Homer optimization, consists of 91.21kW PV, 146kW hydropower, 12 x 24V li-ion batteries with a 20kW converter. The method of enhancing such hybrid systems control, sizing and choice of components is to reduce the Net present cost (NPC) of the system, unmet load, the cost of energy and reduction of CO2. The power consumption varies according to dominant source of energy in the system by controlling the energy compensation depending on the generation capacity of each power source. The initial investment of the RE system is $977,689.25, and its operation and maintenance expenses is $142,769.39 over a 25-year period. Although the investment is very high, the targeted profits in future are huge, taking into consideration of high investment in rural electrification structure implementations, tied with an increase of electricity cost and the 5 years payback period. The study outcomes suggest that the standalone hybrid PV-Hydropower system is feasible with zero pollution in Rukara community.

Keywords: HOMER optimization, hybrid power system, renewable energy, NPC and solar pv systems

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Authors: Muhammad Abdul Basit Memon

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The importance of organizational knowledge sharing and knowledge management has been documented in numerous research studies in available literature, since knowledge sharing has been recognized as a founding pillar for superior organizational performance and a source of gaining competitive advantage. Built on this, most of the successful organizations perceive knowledge management and knowledge sharing as a concern of high strategic importance and spend huge amounts on the effective management and sharing of organizational knowledge. However, despite some very serious endeavors, many firms fail to capitalize on the benefits of knowledge sharing because of being unaware of the individual characteristics, interpersonal, organizational and contextual factors that influence knowledge sharing; simply the antecedent to knowledge sharing. The extant literature on antecedents to knowledge sharing, offers a range of antecedents mentioned in a number of research articles and research studies. Some of the previous studies about antecedents to knowledge sharing, studied antecedents to knowledge sharing regarding inter-organizational knowledge transfer; others focused on inter and intra organizational knowledge sharing and still others investigated organizational factors. Some of the organizational antecedents to KS can relate to the characteristics and underlying aspects of knowledge being shared e.g., specificity and complexity of the underlying knowledge to be transferred; others relate to specific organizational characteristics e.g., age and size of the organization, decentralization and absorptive capacity of the firm and still others relate to the social relations and networks of organizations such as social ties, trusting relationships, and value systems. In the same way some researchers have highlighted on only one aspect like organizational commitment, transformational leadership, knowledge-centred culture, learning and performance orientation and social network-based relationships in the organizations. A bulk of the existing research articles on antecedents to knowledge sharing has mainly discussed organizational or environmental factors affecting knowledge sharing. However, the focus, later on, shifted towards the analysis of individuals or personal determinants as antecedents for the individual’s engagement in knowledge sharing activities, like personality traits, attitude and self efficacy etc. For example, employees’ goal orientations (i.e. learning orientation or performance orientation is an important individual antecedent of knowledge sharing behaviour. While being consistent with the existing literature therefore, the antecedents to knowledge sharing can be classified as being individual and organizational. This paper is an endeavor to discuss a conceptual framework of the individual and organizational antecedents to knowledge sharing in the light of the available literature and empirical evidence. This model not only can help in getting familiarity and comprehension on the subject matter by presenting a holistic view of the antecedents to knowledge sharing as discussed in the literature, but can also help the business managers and especially human resource managers to find insights about the salient features of organizational knowledge sharing. Moreover, this paper can help provide a ground for research students and academicians to conduct both qualitative as well and quantitative research and design an instrument for conducting survey on the topic of individual and organizational antecedents to knowledge sharing.

Keywords: antecedents to knowledge sharing, knowledge management, individual and organizational, organizational knowledge sharing

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Authors: Danilo López, Nelson Vera, Luis Pedraza

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This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Raouf Ahmed Mohamed Hassan

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In Egypt, the excess sewage sludge from wastewater Treatment Plants (WWTPs) is rapidly increasing due to the continuous increase of population, urban planning and industrial developments. Also, cane bagasses constitute an important component of Urban Solid Waste (USW), especially at the south of Egypt, which are difficult to degrade under normal composting conditions. These wastes need to be environmentally managed to reduce the negative impacts of its application or disposal. In term of biogas recovery, the anaerobic digestion of sewage sludge or bagasse separately is inefficient, due to the presence of nutrients and minerals. Also, the Carbone-Nitrogen Ratio (C/N) play an important role, sewage sludge has a ratio varies from 6-16, where cane bagasse has a ratio around 150, whereas the suggested optimum C/N ratio for anaerobic digestion is in the range of 20 to 30. The anaerobic co-digestion is presented as a successful methodology that combines several biodegradable organic substrates able to decrease the amount of output wastes by biodegradation, sharing processing facilities, reducing operating costs, while enabling recovery of biogas. This paper presents the study of co-digestion of sewage sludge from wastewater treatment plants as a type of organic wastes and bagasse as agriculture wastes. Laboratory-scale mesophilic and thermophilic digesters were operated with varied hydraulic retention times. Different percentage of sludge and bagasse are investigated based on the total solids (TS). Before digestion, the bagasse was subjected to grinding pretreatment and soaked in distilled water (water pretreatment). The effect of operating parameters (mixing, temperature) is investigated in order to optimize the process in the biogas production. The yield and the composition of biogas from the different experiments were evaluated and the cumulative curves were estimated. The conducted tests did show that there is a good potential to using the co-digestion of wastewater sludge and bagasse for biogas production.

Keywords: co-digestion, sewage sludge, bagasse, mixing, mesophilic, thermophilic

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Kunio Yoshikawa, Ding Lu

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Small-scale, distributed and low-cost biomass power generation technologies are highly required in the modern society. There are big needs for these technologies in the disaster areas of developed countries and un-electrified rural areas of developing countries. This work aims to present a technical feasibility of the portable ultra-small power generation system based on the gasification of carbonized wood pellets/briquettes. Our project is designed for enabling independent energy production from various kinds of biomass resources in the open-field. The whole process mainly consists of two processes: biomass and waste pretreatment; gasification and power generation. The first process includes carbonization, densification (briquetting or pelletization), and the second includes updraft fixed bed gasification of carbonized pellets/briquettes, syngas purification, and power generation employing an internal combustion gas engine. A combined pretreatment processes including carbonization without external energy and densification were adopted to deal with various biomass. Carbonized pellets showed a better gasification performance than carbonized briquettes and their mixture. The 100-hour continuous operation results indicated that pelletization/briquetting of carbonized fuel realized the stable operation of an updraft gasifier if there were no blocking issues caused by the accumulation of tar. The cold gas efficiency and the carbon conversion during carbonized wood pellets gasification was about 49.2% and 70.5% with the air equivalence ratio value of around 0.32, and the corresponding overall efficiency of the gas engine was 20.3% during the stable stage. Moreover, the maximum output power was 21 kW at the air flow rate of 40 Nm³·h⁻¹. Therefore, the comprehensive system covering biomass carbonization, densification, gasification, syngas purification, and engine system is feasible for portable, ultra-small power generation. This work has been supported by Innovative Science and Technology Initiative for Security (Ministry of Defence, Japan).

Keywords: biomass carbonization, densification, distributed power generation, gasification

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Danilo Di Donato

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The continuous new releases of free and open source software (FOSS) and the high costs of proprietary software -whose monopoly is characterized by closed codes and the low level of implementation and customization of software by end-users- impose a reflection on possible tools that can be chosen and adopted for the design and the representation of new steel constructions. The paper aims to show experimentation carried out to verify the actual potential and the effective applicability of FOSS supports to the BIM modeling of steel structures, particularly considering the goal of a possible workflow in order to achieve high level of development (LOD); allow effective interchange methods between different software. To this end, the examined software packages are those with open source or freeware licenses, in order to evaluate their use in architectural praxis. The test has primarily involved the experimentation of Freecad -the only Open Source software that allows a complete and integrated BIM workflow- and then the results have been compared with those of two proprietary software, Sketchup and TeklaBim Sight, which are released with a free version, but not usable for commercial purposes. The experiments carried out on Open Source, and freeware software was then compared with the outcomes that are obtained by two proprietary software, Sketchup Pro and Tekla Structure which has special modules particularly addressed to the design of steel structures. This evaluation has concerned different comparative criteria, that have been defined on the basis of categories related to the reliability, the efficiency, the potentiality, achievable LOD and user-friendliness of the analyzed software packages. In order to verify the actual outcomes of FOSS BIM for the steel structure projects, these results have been compared with a simulation related to a real case study and carried out with a proprietary software BIM modeling. Therefore, the same design theme, the project of a shelter of public space, has been developed using different software. Therefore the purpose of the contribution is to assess what are the developments and potentialities inherent in FOSS BIM, in order to estimate their effective applicability to professional practice, their limits and new fields of research they propose.

Keywords: BIM, steel buildings, FOSS, LOD

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Authors: Eiko Takaoka, Yoshiyuki Fukushima, Koichiro Hirose, Tadashi Hasegawa

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Although all high school students in Japan are required to learn informatics, many of them do not learn this topic sufficiently. In response to this situation, we propose a support package for high school informatics classes. To examine what students learned and if they sufficiently understood the context of the lessons, a questionnaire survey was distributed to 186 students. We analyzed the results of the questionnaire and determined the weakest units, which were “basic computer configuration” and “memory and secondary storage”. We then developed a package for teaching these units. We propose that our package be applied in high school classrooms.

Keywords: computer science unplugged, computer science outreach, high school curriculum, experimental evaluation

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Poonsook Thatchaopas, Nik Kamariah Nikmat, Nattakarn Eakuru

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Since 2003, the Thai Government has implemented several initiatives to encourage and incubate entrepreneurial skills and motivation among her citizens. One of the initiatives is the “One College One Product” business model or well known as ‘OCOP’, launched by the Vocational Education Commission to encourage partnership between college students to choose at least one product for business venture. In line with this mission, several business enterprises were established such as food products, restaurants, spa, Thai massage, minimart, computer maintenance, karaoke centre, internet café, mini theater etc. Currently, these business incubator projects can be observed at 404 vocational colleges and 21 incubation centres to encourage entrepreneurial small and medium enterprise (SME) development. However, the number of successful OCOP projects is still minimal. Out of the 404 individual OCOP projects at Vocational Colleges around Thailand, very few became successful. The objective of this paper is to identify the critical success factors needed to be a successful OCOP business entrepreneur. This study uses qualitative method by interviewing business partners of an OCOP business called Crispy Roti Krua Acheeva Brand (CRKAB). It is a snack food company that is developed at Pattani Vocational College in South Thailand. This project was initiated by three female entrepreneurs who were alumni student cum owners of the CRKAB. The finding shows that the main critical success factors are self-confidence, creativity or innovativeness, knowledge, skills and perseverance. Additionally, they reiterated that the keys to business success are product quality, perceived price, promotion, branding, new packaging to increase sales and continuous developments. The results implies for a student business SME to be successful, the company should have credible partners and effective marketing plan.

Keywords: student entrepreneurship, business incubator, food industry, qualitative, Thailand

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Authors: Netasha Shaikh, Bryony Wood, Demitra Tsivos, Michael Knight, Risto Kauppinen, Elizabeth Coulthard

Abstract:

Background: Effective treatment of dementia will require early diagnosis, before significant brain damage has accumulated. Memory loss is an early symptom of Alzheimer’s disease (AD). The hippocampus, a brain area critical for memory, degenerates early in the course of AD. The hippocampus comprises several subfields. In contrast to healthy aging where CA3 and dentate gyrus are the hippocampal subfields with most prominent atrophy, in AD the CA1 and subiculum are thought to be affected early. Conventional clinical structural neuroimaging is not sufficiently sensitive to identify preferential atrophy in individual subfields. Here, we will explore the sensitivity of new magnetic resonance imaging (MRI) sequences designed to interrogate medial temporal regions as an early marker of Alzheimer’s. As it is likely a combination of tests may predict early Alzheimer’s disease (AD) better than any single test, we look at the potential efficacy of such imaging alone and in combination with standard and novel cognitive tasks of hippocampal dependent memory. Methods: 20 patients with mild cognitive impairment (MCI), 20 with mild-moderate AD and 20 age-matched healthy elderly controls (HC) are being recruited to undergo 3T MRI (with sequences designed to allow volumetric analysis of hippocampal subfields) and a battery of cognitive tasks (including Paired Associative Learning from CANTAB, Hopkins Verbal Learning Test and a novel hippocampal-dependent abstract word memory task). AD participants and healthy controls are being tested just once whereas patients with MCI will be tested twice a year apart. We will compare subfield size between groups and correlate subfield size with cognitive performance on our tasks. In the MCI group, we will explore the relationship between subfield volume, cognitive test performance and deterioration in clinical condition over a year. Results: Preliminary data (currently on 16 participants: 2 AD; 4 MCI; 9 HC) have revealed subfield size differences between subject groups. Patients with AD perform with less accuracy on tasks of hippocampal-dependent memory, and MCI patient performance and reaction times also differ from healthy controls. With further testing, we hope to delineate how subfield-specific atrophy corresponds with changes in cognitive function, and characterise how this progresses over the time course of the disease. Conclusion: Novel sequences on a MRI scanner such as those in route in clinical use can be used to delineate hippocampal subfields in patients with and without dementia. Preliminary data suggest that such subfield analysis, perhaps in combination with cognitive tasks, may be an early marker of AD.

Keywords: Alzheimer's disease, dementia, memory, cognition, hippocampus

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Authors: Murtaza Hassan Sheikh, Ahmed A. A. AlSaleh, Naser H. N. Jasem

Abstract:

Kuwait has not yet made its mark in the world of technology and research. Therefore, advancements have been made to fill in this gap. Since Robotics covers a wide variety of fields and helps innovation, efforts have been made to promote its education. Despite of the efforts made in Kuwait, robotics education is still on hold. The paper discusses the issues and obstacles in the implementation of robotics education in Kuwait and how a robotics kit “E-Robot” is making an impact in the Kuwait’s future education and innovation. Problems such as robotics competitions rather than education, complexity of robot programming and lack of organized open source platform are being addressed by the introduction of the E-Robot Kit in Kuwait. Due to its success since 2012 a total of 15 schools have accepted the Kit as a core subject, with 200 teaching it as an extracurricular activity.

Keywords: robotics education, Kuwait's education, e-robot kit, research and development, innovation and creativity

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Authors: Dmitry Fedoseyev

Abstract:

The ultimate success of unmanned aerial vehicles (UAVs) depends largely on the effective control of their flight, especially in variable wind conditions. This paper investigates different approaches to selecting the optimal flight level to maximize the range of UAVs. We propose to consider methods based on mathematical models of atmospheric conditions, as well as the use of sensor data and machine learning algorithms to automatically optimize the flight level in real-time. The proposed approaches promise to improve the efficiency and range of UAVs in various wind conditions, which may have significant implications for the application of these systems in various fields, including geodesy, environmental surveillance, and search and rescue operations.

Keywords: drone, UAV, flight trajectory, wind-searching, efficiency

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Authors: Laurie U. deBettencourt

Abstract:

During pre-service field experiences teacher candidates are often asked to reflect as part of their training and in this investigation candidates’ reflective journal entries were reviewed, coded and analyzed with results suggesting teacher candidates need more direct instruction on how to describe, analyze, and make judgements on their instructional practices so that their practices improve over time. Teacher education programs often incorporate reflective-based activities during field experiences. The purpose of this investigation was to determine if special education teacher candidate’s reflective practices changed as they completed their two supervised field experiences and to determine what topics the candidates focused on in their reflections. The six females graduate students were completing two field experiences in special education classrooms within one academic year as part of their coursework leading to a master’s degree and special education teacher state certification. Each candidate wrote 15 reflection journal entries (approximately 200 words each) per field experience. Each of the journal entries were reviewed sentence by sentence to determine a reflective practice score and to determine the topics discussed. The reflective practice score was calculated using four dimensions of reflection (describe, analyze, judge, and apply) in order to create a continuous variable representing their reflective practice across four points of time. A One-way Repeated Measures Analysis of Variance (ANOVA) suggested that special education teacher candidates did not change their reflective practices over time (i.e., at time-point one the practitioner’s mean score was 56.0 out of 100 (SD = 7.6), 53.8 (SD = 4.3) at time-point two, 51.2 (SD = 4.5) at time-point three, and 57.7 (SD = 8.2) at time-point four). Qualitative findings suggest candidates focused mostly on themselves in their reflections. Conclusions suggest the need for teacher preparation programs to provide more direct instruction on how a teacher should reflect. Specific implications are provided for teacher training and future research.

Keywords: field experiences, reflective practices, special educators, teacher preparation

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Authors: Justin Woulfe

Abstract:

Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

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Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

Abstract:

A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

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