Abstract: Over the past 20 years, internet has seen continual advancement and with the advent of online technology, various types of web-based games have been developed. Games are frequently being used among different age groups from baby boomers to generation Z. Games are not only used for entertainment but also utilized as a learning approach transmitting education to a level that is more interesting and effective for students. One of the popular web-based education games is Kahoot with growing popularity and usage, which is being used in different fields of studies. However, little knowledge is available on university students’ perception of Kahoot environment and application for learning subjects. Hence, the objective of the current study is to investigate students’ perceptions of Kahoot application and environment as a learning tool. The study employed a survey approach by distributing Google Forms –created questionnaire, with high level of reliability index, to 62 students (11 males and 51 females). The findings show that students have positive attitudes towards Kahoot application and its environment for learning. Regarding Kahoot application, it was indicated that activities created using Kahoot are more interesting for students, Kahoot is useful for collaborative learning, and Kahoot enhances interest in learning lesson. In terms of Kahoot environment, it was found that using this application through mobile is easy for students, its design is simple and useful, Kahoot-created activities can easily be shared, and the application can easily be used on any platform. The findings of the study have implications for instructors, policymakers and curriculum developers.
Abstract: Pedestrians are the fourth group among road traffic
users that most suffer accidents. Their death rate is even higher than
the motorcyclists group. This gives motivation for the development
of an urban vehicle capable of complying with the United Nations
Economic Commission for Europe pedestrian regulations. The
conceptual vehicle is capable of transporting two passengers and
small parcels for 100 km at a maximum speed of 90 km/h. This paper
presents the design of this vehicle using the finite element method
specially in connection with frontal crash test and car to pedestrian
collision. The simulation is based in a human body FE.
Abstract: The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.
Abstract: Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.
Abstract: Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.
Abstract: This paper provides guidelines for what constitutes a knowledge worker. Many graduates from non-managerial domains adopt, at some point in their professional careers, management roles at different levels, ranging from team leaders through to executive leadership. This is particularly relevant for professionals from an engineering background. Moving from a technical to an executive-level requires an understanding of those behaviour management techniques that can motivate and support individuals and their performance. Further, the transition to management also demands a shift of contextual enablers from tangible to intangible resources, which allows individuals to create new capacities, competencies, and capabilities. In this dynamic process, the knowledge worker becomes that key individual who can help members of the management board to transform information into relevant knowledge. However, despite its relevance in shaping the future of the organization in its transition to the knowledge economy, the role of a knowledge worker has not yet been studied to an appropriate level in the current literature. In this study, the authors review both the contextual enablers and behaviour outputs related to the role of the knowledge worker and relate these to their ability to deal with everyday management issues such as knowledge heterogeneity, varying motivations, information overload, or outdated information. This study highlights that the aggregate of capacities, competences and capabilities (CCCs) can be defined as knowledge structures, the study proposes several contextual enablers and behaviour outputs that knowledge workers can use to work cooperatively, acquire, distribute and knowledge. Therefore, this study contributes to a better comprehension of how CCCs can be managed at different levels through their contextual enablers and behaviour outputs.
Abstract: This study helps Public Water Bureaus in giving reliable answers to water concession requests. Rapidly increasing water requests can be supported provided that further uses of a river course are not totally compromised, and environmental features are protected as well. Strictly speaking, a water concession can be considered a continuous drawing from the source and causes a mean annual streamflow reduction. Therefore, deciding if a water concession is appropriate or inappropriate seems to be easily solved by comparing the generic demand to the mean annual streamflow value at disposal. Still, the immediate shortcoming for such a comparison is that streamflow data are information available only for few catchments and, most often, limited to specific sites. Subsequently, comparing the generic water demand to mean daily discharge is indeed far from being completely satisfactory since the mean daily streamflow is greater than the water withdrawal for a long period of a year. Consequently, such a comparison appears to be of little significance in order to preserve the quality and the quantity of the river. In order to overcome such a limit, this study aims to complete the information provided by flow duration curves introducing a link between Flow Duration Curves (FDCs) and recession curves and aims to show the chronological sequence of flows with a particular focus on low flow data. The analysis is carried out on 25 catchments located in North-Eastern Italy for which daily data are provided. The results identify groups of catchments as hydrologically homogeneous, having the lower part of the FDCs (corresponding streamflow interval is streamflow Q between 300 and 335, namely: Q(300), Q(335)) smoothly reproduced by a common recession curve. In conclusion, the results are useful to provide more reliable answers to water request, especially for those catchments which show similar hydrological response and can be used for a focused regionalization approach on low flow data. A mathematical link between streamflow duration curves and recession curves is herein provided, thus furnishing streamflow duration curves information upon a temporal sequence of data. In such a way, by introducing assumptions on recession curves, the chronological sequence upon low flow data can also be attributed to FDCs, which are known to lack this information by nature.
Abstract: Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.
Abstract: Indonesia has several straits that are very important as a shipping lane, including the Sunda Strait and the Lombok Strait, which are the part of the Indonesian Archipelagic Sea Lane (IASL). An increase in traffic on the Marine Archipelago makes the task of monitoring sea routes increasingly difficult. Indonesia has proposed the establishment of a Traffic Separation Scheme (TSS) in the Sunda Strait and the Lombok Strait and the country now has the right to be able to conceptualize the TSS as well as the obligation to regulate it. Indonesia has the right to maintain national safety and sovereignty. In setting the TSS, Indonesia needs to issue national regulations that are in accordance with international law and the general provisions of the IMO (International Maritime Organization) can then be used as guidelines for maritime safety and security in the Sunda Strait and the Lombok Strait. The research method used is a qualitative method with the concept of linguistic and visual data collection. The source of the data is the analysis of documents and regulations. The results show that the determination of TSS was justified by International Law, in accordance with article 22, article 41, and article 53 of the United Nations Convention on the Law of the Sea (UNCLOS) 1982. The determination of TSS by the Indonesian government would be in accordance with COLREG (International Convention on Preventing Collisions at Sea) 10, which has been designed to follow IASL. Thus, TSS can provide a function as a safety and monitoring medium to minimize ship accidents or collisions, including the warship and aircraft of other countries that cross the IASL.
Abstract: Sentiment analysis is a very active research topic.
Every day, Facebook, Twitter, Weibo, and other social media,
as well as significant e-commerce websites, generate a massive
amount of comments, which can be used to analyse peoples
opinions or emotions. The existing methods for sentiment analysis
are based mainly on sentiment dictionaries, machine learning, and
deep learning. The first two kinds of methods rely on heavily
sentiment dictionaries or large amounts of labelled data. The third
one overcomes these two problems. So, in this paper, we focus
on the third one. Specifically, we survey various sentiment analysis
methods based on convolutional neural network, recurrent neural
network, long short-term memory, deep neural network, deep belief
network, and memory network. We compare their futures, advantages,
and disadvantages. Also, we point out the main problems of
these methods, which may be worthy of careful studies in the
future. Finally, we also examine the application of deep learning in
multimodal sentiment analysis and aspect-level sentiment analysis.
Abstract: Bangladesh is one of the poorest countries in the world. There are several reasons for this insufficiency and uncontrolled population growth is one of the prime reasons. Others include low economic progress, imbalanced resource management, unemployment and underemployment, urban migration and natural catastrophes etc. As a result, the rate of urban poor is increasing inevitably in every sphere of urban cities in Bangladesh and Dhaka is the most affected one. Besides there is scarcity of urban land, housing, urban infrastructure and amenities which create pressure on urban cities and mostly encroach the open space, wetlands that causes environmental degradation. Government has no or limited control over these due to poor government policy and management, political pressure and lack of resource management. Unfortunately, over centralization and bureaucracy creates unnecessary delay and interruptions in any government initiations. There is also no coordination between government and private sector developer to solve the problem of urban Poor. To understand the problem of these huge populations this paper analyzes one of the single largest slum areas in Dhaka, Korail Slum. The study focuses on socio demographic analysis, morphological pattern and role of different actors responsible for the improvements of the area and recommended some possible steps for determining the potential outcomes.
Abstract: Current trends in remote health monitoring to monetize on the Internet of Things applications have been raised in efficient and interference free communications in Wireless Body Area Network (WBAN) scenario. Co-existence interference in WBANs have aggravates the over-congested radio bands, thereby requiring efficient Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategies and improve interference management. Existing solutions utilize simplistic heuristics to approach interference problems. The scope of this research article is to investigate reinforcement learning for efficient interference management under co-existing scenarios with an emphasis on homogenous interferences. The aim of this paper is to suggest a smart CSMA/CA mechanism based on reinforcement learning called QIM-MAC that effectively uses sense slots with minimal interference. Simulation results are analyzed based on scenarios which show that the proposed approach maximized Average Network Throughput and Packet Delivery Ratio and minimized Packet Loss Ratio, Energy Consumption and Average Delay.
Abstract: The prime idea of inclusive education is to mainstream every child in education. However, it will be challenging for implementation when there are policy and practice gaps. It will be even more challenging when children have disabilities. Generally, the focus will be on the policy gap, but the problem may not always be with policy. The proper practice could be a challenge in the countries like Nepal. In determining practice, the teachers’ perceptions toward inclusive will play a vital role. Nepal has categorized disability in 7 types (physical, visual, hearing, vision/hearing, speech, mental, and multiple). Out of these, hearing impairment is the study realm. In the context of a limited number of researches on children with disabilities and rare researches on CWHI and their education in Nepal, this study is a pioneering effort in knowing basically the problems and challenges of CWHI focused on inclusive education in the schools including gaps and barriers in its proper implementation. Philosophically, the paradigm of the study is post-positivism. In the post-positivist worldview, the quantitative approach with the description of the situation and inferential relationship are revealed out in the study. This is related to the natural model of objective reality. The data were collected from an individual survey with the teachers and head teachers of 35 schools in Nepal. The survey questionnaire was prepared and filled by the respondents from the schools where the CWHI study in 7 provincial 20 districts of Nepal. Through these considerations, the perceptions of CWHI focused inclusive education were explored in the study. The data were analyzed using both descriptive and inferential tools on which the Likert scale-based analysis was done for descriptive analysis, and chi-square mathematical tool was used to know the significant relationship between dependent variables and independent variables. The descriptive analysis showed that the majority of teachers have positive perceptions toward implementing CWHI focused inclusive education, and the majority of them have positive perceptions toward CWHI focused inclusive education, though there are some problems and challenges. The study has found out the major challenges and problems categorically. Some of them are: a large number of students in a single class; availability of generic textbooks for CWHI and no availability of textbooks to all students; less opportunity for teachers to acquire knowledge on CWHI; not adequate teachers in the schools; no flexibility in the curriculum; less information system in schools; no availability of educational consular; disaster-prone students; no child abuse control strategy; no disabled-friendly schools; no free health check-up facility; no participation of the students in school activities and in child clubs and so on. By and large, it is found that teachers’ age, gender, years of experience, position, employment status, and disability with him or her show no statistically significant relation to successfully implement CWHI focused inclusive education and perceptions to CWHI focused inclusive education in schools. However, in some of the cases, the set null hypothesis was rejected, and some are completely retained. The study has suggested policy implications, implications for educational authority, and implications for teachers and parents categorically.
Abstract: The article presents results from the AF3 project “Advanced Forest Fire Fighting” focused on Unmanned Aircraft Systems (UAS)-based 3D surveillance and 3D area mapping using high-resolution photogrammetric methods from multispectral imaging, also taking advantage of the 3D scanning techniques from the SCAN4RECO project. We also present a proprietary embedded sensor system used for the detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire or Mavic. Results from real-life pilot trials in Greece, Spain, and Israel demonstrated added-value in the use of UAS for precise and reliable detection of forest fires, as well as high-resolution 3D aerial modeling for accurate quantification of human resources and equipment required for firefighting.
Abstract: Wildfires are becoming an increasing concern worldwide, causing substantial social, economic, and environmental disruptions. This situation is especially relevant in Mediterranean-climate regions, present in all the five continents of the world, in which fire is not only a natural component of the environment but also perhaps one of the most important evolutionary forces. The rise in wildfire occurrences and their associated impacts suggests the need for identifying knowledge gaps and enhancing the basis of scientific evidence on how managers and policymakers may act effectively to address them. Considering that the main goal of a systematic map is to collate and catalog a body of evidence to describe the state of knowledge for a specific topic, it is a suitable approach to be used for this purpose. In this context, the aim of this study is to systematically map the research trends in wildfire management practices in Mediterranean-climate regions. A total of 201 wildfire management studies were analyzed and systematically mapped in terms of their: Year of publication; Place of study; Scientific outlet; Research area (Web of Science) or Research field (Scopus); Wildfire phase; Central research topic; Main objective of the study; Research methods; and Main conclusions or contributions. The results indicate that there is an increasing number of studies being developed on the topic (most from the last 10 years), but more than half of them are conducted in few Mediterranean countries (60% of the analyzed studies were conducted in Spain, Portugal, Greece, Italy or France), and more than 50% are focused on pre-fire issues, such as prevention and fuel management. In contrast, only 12% of the studies focused on “Economic modeling” or “Human factors and issues,” which suggests that the triple bottom line of the sustainability argument (social, environmental, and economic) is not being fully addressed by fire management research. More than one-fourth of the studies had their objective related to testing new approaches in fire or forest management, suggesting that new knowledge is being produced on the field. Nevertheless, the results indicate that most studies (about 84%) employed quantitative research methods, and only 3% of the studies used research methods that tackled social issues or addressed expert and practitioner’s knowledge. Perhaps this lack of multidisciplinary studies is one of the factors hindering more progress from being made in terms of reducing wildfire occurrences and their impacts.
Abstract: It is recognized that people are the main drivers in almost all the processes that affect airworthiness assurance. This is especially true in the area of aircraft maintenance, which is an essential part of continuing airworthiness. This work investigates what impact English language proficiency, the intersection of the military and Thai cultures, and the lack of initial and continuing human factors training have on the work performance of maintenance personnel in the Royal Thai Air Force (RTAF). A quantitative research method based on a cross-sectional survey was used to gather data about these three key aspects of “people” in a military airworthiness environment. 30 questions were developed addressing the crucial topics of English language proficiency, impact of culture, and human factors training. The officers and the non-commissioned officers (NCOs) who work for the Aeronautical Engineering Divisions in the RTAF comprised the survey participants. The survey data were analysed to support various hypotheses by using a t-test method. English competency in the RTAF is very important since all of the service manuals for Thai military aircraft are written in English. Without such competency, it is difficult for maintenance staff to perform tasks and correctly interpret the relevant maintenance manual instructions; any misunderstandings could lead to potential accidents. The survey results showed that the officers appreciated the importance of this more than the NCOs, who are the people actually doing the hands-on maintenance work. Military culture focuses on the success of a given mission, and leverages the power distance between the lower and higher ranks. In Thai society, a power distance also exists between younger and older citizens. In the RTAF, such a combination tends to inhibit a just reporting culture and hence hinders safety. The survey results confirmed this, showing that the older people and higher ranks involved with RTAF aircraft maintenance believe that the workplace has a positive safety culture and climate, whereas the younger people and lower ranks think the opposite. The final area of consideration concerned human factors training and non-technical skills training. The survey revealed that those participants who had previously attended such courses appreciated its value and were aware of its benefits in daily life. However, currently there is no regulation in the RTAF to mandate recurrent training to maintain such knowledge and skills. The findings from this work suggest that the people involved in assuring the continuing airworthiness of the RTAF would benefit from: (i) more rigorous requirements and standards in the recruitment, initial training and continuation training regarding English competence; (ii) the development of a strong safety culture that exploits the uniqueness of both the military culture and the Thai culture; and (iii) providing more initial and recurrent training in human factors and non-technical skills.
Abstract: Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.
Abstract: This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.
Abstract: In the era of big data, public investors are faced with more complicated information related to investment decisions than ever before. To survive in the fierce competition, it has become increasingly urgent for investors to combine multi-source knowledge and evaluate the companies’ true value efficiently. For this, a rule-based ontology reasoning method is proposed to support steel companies’ value assessment. Considering the delay in financial disclosure and based on cost-benefit analysis, this paper introduces the supply chain enterprises financial analysis and constructs the ontology model used to value the value of steel company. In addition, domain knowledge is formally expressed with the help of Web Ontology Language (OWL) language and SWRL (Semantic Web Rule Language) rules. Finally, a case study on a steel company in China proved the effectiveness of the method we proposed.
Abstract: This article presents the application of the semi-analytic method (SAM) in the thermal management solution (TMS) of the energy storage system (ESS). The TMS studied in this work is fluid cooling. In fluid cooling, both effective heat conduction and heat convection are indispensable due to the heat transfer from solid to fluid. Correspondingly, an efficient TMS requires a design investigation of the following parameters: fluid inlet temperature, ESS initial temperature, fluid flow rate, working c rate, continuous working time, and materials properties. Their variation induces a change of thermal performance in the battery module, which is usually evaluated by numerical simulation. Compared to complicated computation resources and long computation time in simulation, the SAM is developed in this article to predict the thermal influence within a few seconds. In SAM, a fast prediction model is reckoned by combining numerical simulation with theoretical/empirical equations. The SAM can explore the thermal effect of boundary parameters in both steady-state and transient heat transfer scenarios within a short time. Therefore, the SAM developed in this work can simplify the design cycle of TMS and inspire more possibilities in TMS design.