Search results for: dual task pardigm

Authors: Sherly Ferro Lensun, Donal Matheos Ratu, Elni Jeini Usoh, Helena M. L. Pandi, Mayske Rinny Liando

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This study aims to gain a deep understanding of the process and the increase resulting in mastery of Kanji with a Language Learning Strategies through multimedia. This research aims to gain scientific data on process and the result of improving kanji mastery by using Chokusetsu strategy in Kanji learning. The method used in this research is Action Research developed by Kemmis and Mc. Taggart is known as Spiral Model. This model consists of following stages: planning, implementation, observation, and reflection. The research results in following findings: (1) Kanji mastery comprises 4 major aspects, those are reading, writing, the use in sentence, and memorizing, and those aspects show gradual improvement from time to time. (2) Students have more participation in learning activities which can be identified from some positive behaviours such giving respond in finishing exercise in class. (3) Students’ better attention to the lesson shown by active behaviour in giving more questions or asking for more explanation to the lecturers, memorizing Kanji card, finishing the task of making Kanji card/house, doing the exercises more seriously, and finishing homework assignment punctually. (4) More attractive learning activities and tasks in the forms of more engaging colour and pictures enables students to conduct self-evaluation on their learning process.

Keywords: Kanji, action research, language learning strategies, multimedia

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Authors: Gobinathan Devathasan, Kezia Devathasan

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Objective: To improve the current modified Broca-Wernicke-Lichtheim-Kussmaul speech schema and provide insight into autism. Methods: We reviewed the pertinent literature. Current findings, involving Brodmann areas 22, 46, 9,44,45,6,4 are based on neuropathology and functional MRI studies. However, in primary autism, there is no lucid explanation and changes described, whether neuropathology or functional MRI, appear consequential. Findings: We forward an enhanced model which may explain the enigma related to autism. Vowel output is subcortical and does need cortical representation whereas consonant speech is cortical in origin. Left lateralization is needed to commence the circuitry spin as our life have evolved with L-amino acids and left spin of electrons. A fundamental species difference is we are capable of three syllable-consonants and bi-syllable expression whereas cetaceans and songbirds are confined to single or dual consonants. The 4 key sites for speech are superior auditory cortex, Broca’s two areas, and the supplementary motor cortex. Using the Argand’s diagram and Reimann’s projection, we theorize that the Euclidean three dimensional synaptic neuronal circuits of speech are quantized to coherent waves, and then decoherence takes place at area 6 (spherical representation). In this quantum state complex, 3-consonant languages are instantaneously integrated and multiple languages can be learned, verbalized and differentiated. Conclusion: We postulate that evolutionary human speech is elevated to quantum interaction unlike cetaceans and birds to achieve the three consonants/bi-syllable speech. In classical primary autism, the sudden speech switches off and on noted in several cases could now be explained not by any anatomical lesion but failure of coherence. Area 6 projects directly into prefrontal saccadic area (8); and this further explains the second primary feature in autism: lack of eye contact. The third feature which is repetitive finger gestures, located adjacent to the speech/motor areas, are actual attempts to communicate with the autistic child akin to sign language for the deaf.

Keywords: quantum neuronal paradigm, cetaceans and human speech, autism and rapid magnetic stimulation, coherence and decoherence of speech

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Authors: Koray Altintas, Ozalp Vayvay

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Increased global issues such as depletion of sources, environmental problems and social inequality triggered public awareness towards finding sustainable solutions in order to ensure the well-being of the current as well as future generations. Since energy plays a significant role in improved social and economic well-being and is imperative on both industrial and commercial wealth creation, it is a must to develop a standardized set of metrics which makes it possible to indicate the present condition relative to conditions in the past and to develop any perspective which is required to frame actions for the future. This is not an easy task by considering the complexity of the issue which requires integrating economic, environmental and social aspects of sustainable energy. Multi-criteria decision making (MCDM) can be considered as a form of integrated sustainability evaluation and a decision support approach that can be used to solve complex problems featuring; conflicting objectives, different forms of data and information, multi-interests and perspectives. On that matter, MCDM methods are useful for providing solutions to complex energy management problems. The aim of this study is to review MCDM approaches that can be used for examining sustainable energy management. This study presents an insight into MCDM techniques and methods that can be useful for engineers, researchers and policy makers working in the energy sector.

Keywords: sustainable energy, sustainability criteria, multi-criteria decision making, sustainability dimensions

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Om Prakash Bharti, Aanchal Verma, R. K. Saket

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The Doubly-Fed Induction Generator (DFIG) is suggested for Wind Energy Conversion System (WECS) to extract wind power. DFIG is preferably employed due to its robustness towards variable wind and rotor speed. DFIG has the adaptable property because the system parameters are smoothly dealt with, including real power, reactive power, DC-link voltage, and the transient and dynamic responses, which are needed to analyze constantly. The analysis becomes more prominent during any unusual condition in the electrical power system. Hence, the study and improvement in the system parameters and transient response performance of DFIG are required to be accomplished using some controlling techniques. For fulfilling the task, the present work implements and compares the optimization methods for the design of the DFIG controller for WECS. The bio-inspired optimization techniques are applied to get the optimal controller design parameters for DFIG-based WECS. The optimized DFIG controllers are then used to retrieve the transient response performance of the six-order DFIG model with a step input. The results using MATLAB/Simulink show the betterment of the Firefly algorithm (FFA) over other control techniques when compared with the other controller design methods.

Keywords: doubly-fed induction generator, wind turbine, wind energy conversion system, induction generator, transfer function, proportional, integral, derivatives

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Authors: Vojtěch Gybas, Libor Klubal, Kateřina Kostolányová

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Nowadays, mobile touch technologies, such as tablets, are an integral part of teaching and learning in many special elementary schools. Many special education teachers tend to choose an iPad tablet with iOS. The reason is simple; the iPad has a function for pupils with special educational needs. If we decide to use tablets in teaching, in general, first we should try to stimulate the cognitive abilities of the pupil at the highest level, while holding the pupil’s attention on the task, when working with the device. This paper will describe how student attention can be increased by eliminating the working environment of selected applications, while using iPads with pupils in a special elementary school. Assisted function approach is highly effective at eliminating unwanted touching by a pupil when working on the desktop iPad, thus actively increasing the pupil´s attention while working on specific educational applications. During the various stages of the action, the research was conducted via data collection and interpretation. After a phase of gaining results and ideas for practice and actions, we carried out the check measurement, this time using the tool-assisted approach. In both cases, the pupils worked in the Math Board application and the resulting differences were evident.

Keywords: special elementary school, a mobile touch device, iPad, attention, Math Board

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Authors: Naomi Weiner-Levy

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The Mofet Institute of Research, established a School of Professional Development, the only one of its kind in Israel and throughout the world. It offers specialized programs for teacher educators, providing them with the professional knowledge and skills. The studies aim at updating teachers about rapidly changing knowledge and skills. Teacher educators are conceptualized as shifting from first order practitioners (school teachers) to second order practitioners. Those who train teachers are referred to as third order practitioners. The instructors in the School of Professional Development are third-order practitioners – teacher educators specializing in teaching their colleagues. Collegial guidance by teachers’ college staff members is no simple task: Tutors must be expert in their field of specialization, as well as in instruction. Moreover, although colleagues, they have to position themselves within the group as authoritative figures in terms of instruction and knowledge. To date, the role and professional identity of these third-order practitioners, has not been studied. To understand the nature and development of professional identity, a qualitative study was conducted in which 12 tutors of various subjects were interviewed. These were analyzed by categorical content analysis. The findings, assessed professional identity through a post-modern prism, while examining the interplay among events that tutors experienced, the knowledge they acquired and the structuring of their professional identity. The Tutors’ identity transformed through negotiating with ‘self’ and ‘other’ in the class, and constructed by their mutual experiences as tutors and learners. Understanding the function and identity of tutors facilitates comprehension of this unique training process for teacher educators.

Keywords: professional development, professional identity, teacher education, tutoring

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

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Authors: Othman Maklouf, Matouk Elamari, M. Rgeai, Fateh Alej

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In modern avionics the main fundamental component is the flight management system (FMS). An FMS is a specialized computer system that automates a wide variety of in-flight tasks, reducing the workload on the flight crew to the point that modern civilian aircraft no longer carry flight engineers or navigators. The main function of the FMS is in-flight management of the flight plan using various sensors such as Global Positioning System (GPS) and Inertial Navigation System (INS) to determine the aircraft's position and guide the aircraft along the flight plan. GPS which is satellite based navigation system, and INS which generally consists of inertial sensors (accelerometers and gyroscopes). GPS is used to locate positions anywhere on earth, it consists of satellites, control stations, and receivers. GPS receivers take information transmitted from the satellites and uses triangulation to calculate a user’s exact location. The basic principle of an INS is based on the integration of accelerations observed by the accelerometers on board the moving platform, the system will accomplish this task through appropriate processing of the data obtained from the specific force and angular velocity measurements. Thus, an appropriately initialized inertial navigation system is capable of continuous determination of vehicle position, velocity and attitude without the use of the external information. The main objective of article is to introduce a comparative study between the two systems under different conditions and scenarios using MATLAB with SIMULINK software.

Keywords: flight management system, GPS, IMU, inertial navigation system

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Authors: Rahul Bishnoi

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Theatre, as Ann Ubersfld calls it, is a paradox. At once, it is both a literary work and a physical representation. Theatre as a text is eternal, reproducible, and identical while as a performance, theatre is momentary and never identical to the previous performances. In this dual existence of theatre, who is the author? Is the author the playwright who writes the dramatic text, or the director who orchestrates the performance, or the actor who embodies the text? From the poststructuralist lens of Barthes, the author is dead. Barthes’ argument of discrete temporality, i.e. the author is the before, and the text is the after, does not hold true for theatre. A published literary work is written, edited, printed, distributed and then gets consumed by the reader. On the other hand, theatrical production is immediate; an actor performs and the audience witnesses it instantaneously. Time, so to speak, does not separate the author, the text, and the reader anymore. The question of authorship gets further complicated in Augusto Boal’s “Theatre of the Oppressed” movement where the audience is a direct participant like the actors in the performance. In this research, through an experimental performance, the duality of theatre is explored with the authorship discourse. And the conventional definition of authorship is subjected to additional complexity by erasing the distinction between an actor and the audience. The design/methodology of the experimental performance is as follows: The audience will be asked to produce a text under an anonymous virtual alias. The text, as it is being produced, will be read and performed by the actor. The audience who are also collectively “authoring” the text, will watch this performance and write further until everyone has contributed with one input each. The cycle of writing, reading, performing, witnessing, and writing will continue until the end. The intention is to create a dynamic system of writing/reading with the embodiment of the text through the actor. The actor is giving up the power to the audience to write the spoken word, stage instruction and direction while still keeping the agency of interpreting that input and performing in the chosen manner. This rapid conversation between the actor and the audience also creates a conversion of authorship. The main conclusion of this study is a perspective on the nature of dynamic authorship of theatre containing a critical enquiry of the collaboratively produced text, an individually performed act, and a collectively witnessed event. Using practice as a methodology, this paper contests the poststructuralist notion of the author as merely a ‘scriptor’ and breaks it further by involving the audience in the authorship as well.

Keywords: practice based research, performance studies, post-humanism, Avant-garde art, theatre

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Authors: Ronal Muresano, Andrea Pagano

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Nowadays, the mathematical/statistical applications are developed with more complexity and accuracy. However, these precisions and complexities have brought as result that applications need more computational power in order to be executed faster. In this sense, the multicore environments are playing an important role to improve and to optimize the execution time of these applications. These environments allow us the inclusion of more parallelism inside the node. However, to take advantage of this parallelism is not an easy task, because we have to deal with some problems such as: cores communications, data locality, memory sizes (cache and RAM), synchronizations, data dependencies on the model, etc. These issues are becoming more important when we wish to improve the application’s performance and scalability. Hence, this paper describes an optimization method developed for Systemic Model of Banking Originated Losses (SYMBOL) tool developed by the European Commission, which is based on analyzing the application's weakness in order to exploit the advantages of the multicore. All these improvements are done in an automatic and transparent manner with the aim of improving the performance metrics of our tool. Finally, experimental evaluations show the effectiveness of our new optimized version, in which we have achieved a considerable improvement on the execution time. The time has been reduced around 96% for the best case tested, between the original serial version and the automatic parallel version.

Keywords: algorithm optimization, bank failures, OpenMP, parallel techniques, statistical tool

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Authors: Bilha Paryente, Rivi Frei Landau

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Autoethnography - a combination of autobiography and ethnography - focuses on the intersection of personal experiences and the culture in which they take place and is considered a viable method for exploring human experiences. The Russo-Ukrainian war has resulted in millions of forcibly displaced asylum-seeking refugees facing ambiguous loss. Whereas much is known about refugees' support needs, little is known about the needs and experiences of the humanitarian delegation members (HDM) who assist them. Through a debrief-based co-autoethnographic account of a female HDM who supported Ukrainian refugee mothers and children on the Polish borders, we explored the lived experiences involved in such a mission. Specifically, we conducted a transnational dyadic autoethnography debrief-based co-autoethnography which included both verbal and photo-based debriefing (8 two-hour sessions) alongside a reflexive (10-day) field diary analysis. Content analysis revealed cognitive dilemmas, emotional struggles, and practical adaptations occurring within the HDM's three identity-related domains: personal, professional (psychologist), and ethnic. The methodology presented and demonstrated in this paper enhances our theoretical understanding of the challenges faced by HDMs and may contribute to better future design of HDM training. Practically, the findings of the current study suggest the need for a three-stage accompaniment for HDMs relating to their personal, professional, and ethnic identities and considering their cognitive, emotional, and adaptive aspects. First, before leaving, HDMs should be briefed on personal and professional aspects of their experiences and ways of coping with them, as well as ethnic and religious affiliation issues. Second, while volunteering every evening their dilemmas, emotional struggles, and ways of adapting should be addressed for the three layers of identities. And finally, shortly after their return, there should be a final meeting to discuss all aspects of their identities and layers of personality. In this way, HDMs can become more effective in the important mission they fulfill. We hope that future HDMs and the bodies that send them on humanitarian missions of paramount importance will adopt these recommendations and generate proactive insights for members of future delegations.

Keywords: autoethnography, refugees, humanitarian delegation, ambiguous loss, Russo-Ukraine War, parenting

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Authors: Ahmed Fawzy, Khaled Ayad, Gh. M. Koura, W. Reda

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The excessive frontal plane motion of the lower extremity during sports activities is thought to be a contributing factor to many traumatic and overuse injuries of the knee joint, little is known about the biomechanical factors that contribute to this loading pattern. Objectives: The purpose of this study was to investigate if there is a relationship between hip external rotators isometric strength and the value of frontal plane projection angle (FPPA) during single leg landing tasks in normal male subjects. Methods: One hundred (male) subjects free from lower extremity injuries for at least six months ago participated in this study. Their mean age was (23.25 ± 2.88) years, mean weight was (74.76 ± 13.54) (Kg), mean height was (174.23 ± 6.56) (Cm). The knee frontal plane projection angle was measured by digital video camera using single leg landing task. Hip external rotators isometric strength were assessed by portable hand held dynamometer. Muscle strength had been normalized to the body weight to obtain more accurate measurements. Results: The results demonstrated that there was no significant relationship between hip external rotators isometric strength and the value of FPPA during single leg landing tasks in normal male subjects. Conclusion: It can be concluded that there is no relationship between hip external rotators isometric strength and the value of FPPA during functional activities in normal male subjects.

Keywords: 2-dimensional motion analysis, hip strength, kinematics, knee injuries

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Authors: Nurullah Ekmekci, Omer Akkaya, Vural Cagliyan

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Increased use of the Internet has resulted in the emergence of a new retail types called online shopping or electronic retail (e-retail). The rapid growth of the Internet has enabled customers to search information about the product and buy these products or services from e-retailers. Although this new form of shopping has grown in a remarkable way because of offering easiness to people, it is not an easy task to capture the success by distinguishing from competitors in this environment which millions of players takes place. For the success, e-retailers should determine the factors which the customers take notice while they are buying from e-retailers. This paper aims to identify the factors that provide preferability for the online shopping websites and the importance levels of these factors. These main criteria which have taken notice are Customer Service Performance (CSP), Website Performance (WSP), Criteria Related to Product (CRP), Ease of Payment (EP), Security/Privacy (SP), Ease of Return (ER), Delivery Service Performance (DSP) and Order Fulfillment Performance (OFP). It has benefited from Analytic Hierarchy Process to determine the priority of the criteria. Based on analysis, Security/Privacy (SP) criteria seems to be most important criterion with 22 % weight. Companies should attach importance to the security and privacy for making their online website more preferable among the online shoppers.

Keywords: AHP (analytical hierarchy process), multi-criteria decision making, online shopping, shopping

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Authors: Bo-Bin Xiaoa, Chia-Yi Liua

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In the task of searching for anomalous radiation sources, personnel holding radiation detectors to search for radiation sources may be exposed to unnecessary radiation risk, and automated search using machines becomes a required project. The research uses various sophisticated algorithms, which are double Q learning, dueling network, and NoisyNet, of deep reinforcement learning to search for radiation sources. The simulation environment, which is a 10*10 grid and one shielding wall setting in it, improves the development of the AI model by training 1 million episodes. In each episode of training, the radiation source position, the radiation source intensity, agent position, shielding wall position, and shielding wall length are all set randomly. The three algorithms are applied to run AI model training in four environments where the training shielding wall is a full-shielding wall, a lead wall, a concrete wall, and a lead wall or a concrete wall appearing randomly. The 12 best performance AI models are selected by observing the reward value during the training period and are evaluated by comparing these AI models with the gradient search algorithm. The results show that the performance of the AI model, no matter which one algorithm, is far better than the gradient search algorithm. In addition, the simulation environment becomes more complex, the AI model which applied Double DQN combined Dueling and NosiyNet algorithm performs better.

Keywords: double Q learning, dueling network, NoisyNet, source searching

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Authors: T. Hari Prasath, P. Ithaya Rani

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In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: Anthony El Hachem, Hosam Salman

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Selecting the most effective construction method for drilled shafts under the high phreatic surface can be a challenging task that requires effective communication between the design and construction teams. Slurry placement, temporary casing, and permanent casing are the three most commonly used installation techniques to ensure the stability of the drilled hole before casting the concrete. Each one of these methods has its implications on the installation and performance of the drilled piers. Drilled shafts were designed to support a fire wall for an Energy project in Central Texas. The subsurface consisted of interlayers of sands and clays of varying shear strengths. The design recommended that the shafts be installed with temporary casing or slurry displacement due to the anticipated groundwater seepage through granular soils. During the foundation construction, it was very difficult to maintain the stability of the hole, and the contractor requested to install the shafts using permanent casings. Therefore, the foundation design was modified to ensure that the cased shafts achieve the required load capacity. Effective and continuous communications between the owner, contractor and design team during field shaft installations to mitigate the unforeseen challenges helped the team to successfully complete the project.

Keywords: construction challenges, deep foundations, drilled shafts, loose sands underwater table, permanent casing

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Authors: Qiang Wang, Hongyang Yu

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Multi-human 3D pose estimation is a challenging task in computer vision, which aims to recover the 3D joint locations of multiple people from multi-view images. In contrast to traditional methods, which typically only use color (RGB) images as input, our approach utilizes both color and depth (D) information contained in RGB-D images. We also employ a transformer-based model as the backbone of our approach, which is able to capture long-range dependencies and has been shown to perform well on various sequence modeling tasks. Our method is trained and tested on the Carnegie Mellon University (CMU) Panoptic dataset, which contains a diverse set of indoor and outdoor scenes with multiple people in varying poses and clothing. We evaluate the performance of our model on the standard 3D pose estimation metrics of mean per-joint position error (MPJPE). Our results show that the transformer-based approach outperforms traditional methods and achieves competitive results on the CMU Panoptic dataset. We also perform an ablation study to understand the impact of different design choices on the overall performance of the model. In summary, our work demonstrates the effectiveness of using a transformer-based approach with RGB-D images for multi-human 3D pose estimation and has potential applications in real-world scenarios such as human-computer interaction, robotics, and augmented reality.

Keywords: multi-human 3D pose estimation, RGB-D images, transformer, 3D joint locations

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Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim

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Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.

Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth

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Authors: A. Das, L. Hitihamillage, S. Moridpour

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Natural disasters are inevitable to the biodiversity. Disasters such as flood, tsunami and tornadoes could be brutal, harsh and devastating. In Australia, flooding is a major issue experienced by different parts of the country. In such crisis, delays in evacuation could decide the life and death of the people living in those regions. Congestion management could become a mammoth task if there are no steps taken before such situations. In the past to manage congestion in such circumstances, many strategies were utilised such as converting the road shoulders to extra lanes or changing the road geometry by adding more lanes. However, expansion of road to resolving congestion problems is not considered a viable option nowadays. The authorities avoid this option due to many reasons, such as lack of financial support and land space. They tend to focus their attention on optimising the current resources they possess and use traffic signals to overcome congestion problems. Traffic Signal Management strategy was considered a viable option, to alleviate congestion problems in the City of Geelong, Victoria. Arterial road with signalised intersections considered in this paper and the traffic data required for modelling collected from VicRoads. Traffic signalling software SIDRA used to model the roads, and the information gathered from VicRoads. In this paper, various signal parameters utilised to assess and improve the corridor performance to achieve the best possible Level of Services (LOS) for the arterial road.

Keywords: congestion, constraints, management, LOS

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Authors: Misbah Javaid, Vladimir Estivill-Castro, Rene Hexel

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The field of robotics is advancing rapidly to the point where robots have become an integral part of the modern society. These robots collaborate and contribute productively with humans and compensate some shortcomings from human abilities and complement them with their skills. Effective teamwork of humans and robots demands to investigate the critical issue of trust. The field of human-computer interaction (HCI) has already examined trust humans place in technical systems mostly on issues like reliability and accuracy of performance. Early work in the area of expert systems suggested that automatic generation of explanations improved trust and acceptability of these systems. In this work, we augmented a robot with the user-invoked explanation generation proficiency. To measure explanations effect on human’s level of trust, we collected subjective survey measures and behavioral data in a human-robot team task into an interactive, adversarial and partial information environment. The results showed that with the explanation capability humans not only understand and recognize robot as an expert team partner. But, it was also observed that human's learning and human-robot team performance also significantly improved because of the meaningful interaction with the robot in the human-robot team. Moreover, by observing distinctive outcomes, we expect our research outcomes will also provide insights into further improvement of human-robot trustworthy relationships.

Keywords: explanation interface, adversaries, partial observability, trust building

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Authors: Kwan Hee Han

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In today’s business environments, frequent change of customer requirements is a tough challenge to manufacturing company. To cope with these challenges, a production planning and scheduling (PP&S) function might be established to provide accountability for both customer service and operational efficiency. Nowadays, many manufacturing firms have utilized PP&S software solutions to generate a realistic production plan and schedule to adapt to external changes efficiently. However, companies which consider the introduction of PP&S software solution, still have difficulties for selecting adequate solution to meet their specific needs. Since the task of PP&S is the one of major building blocks of SCM (Supply Chain Management) architecture, which deals with short term decision making in the production process of SCM, it is needed that the functionalities of PP&S should be analysed within the whole SCM process. The aim of this paper is to analyse the PP&S functionalities and its system architecture from the SCM perspective by using the criteria of level of planning hierarchy, major 4 SCM processes and problem-solving approaches, and finally propose a classification framework of PP&S solutions to facilitate the comparison among various commercial software solutions. By using proposed framework, several major PP&S solutions are classified and positioned according to their functional characteristics in this paper. By using this framework, practitioners who consider the introduction of computerized PP&S solutions in manufacturing firms can prepare evaluation and benchmarking sheets for selecting the most suitable solution with ease and in less time.

Keywords: production planning, production scheduling, supply chain management, the advanced planning system

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

Procedia PDF Downloads 385

Authors: Khadija Amin

Abstract:

The significant contribution of foreign investment in promoting economic recovery, especially in countries recovering from conflict, is generally recognized. This study examines the influence of foreign investment on the economic development of countries that have had long-lasting internal conflicts. The study examines the complex correlation between foreign investment and economic progress using the production function framework based on endogenous growth theory. In addition to foreign investment, the research considers a range of factors that affect economic growth, such as trade dynamics, the spread of information, attempts to promote peace, changes in the labor market, and the accumulation of domestic capital. The study challenges common beliefs by revealing a statistically negligible negative association between GDP growth and foreign investment (FI) inflows in post-war economies. The existing literature highlights the positive impact of trade and foreign investment on economic growth. However, this study emphasizes that these impacts are complex and depend on various contextual factors such as trade policies, infrastructure development, domestic investment levels, human capital development, and macroeconomic stability. The results emphasize the crucial significance of foreign investment in stimulating development while also drawing attention to the intricacies of precisely assessing its economic consequences. Measuring the economic impact of foreign investment is a difficult task that requires detailed analysis considering many contextual elements and changing socioeconomic conditions.

Keywords: economic grouths, foreign investment, trade policies, domestic investment

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Authors: Kavita Bodke

Abstract:

Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Robyn Moloney, HuiLing Xu

Abstract:

Many countries wish to produce Asia-literate citizens, through language education. International contexts of Chinese language education are seeking pedagogical innovation to meet local contextual factors frequently holding back learner success. In multicultural Australia, innovative pedagogy is urgently needed to support motivation in sustained study, with greater strategic integration of technology. This research took a qualitative approach to identify need and solutions. The paper analyses strategies that three secondary school teachers are adopting to meet specific challenges in the Australian context. The data include teacher interviews, classroom observations and student interviews. We highlight the use of task-based learning and differentiated teaching for multilevel classes, and the role which digital technologies play in facilitating both areas. The strategy examples are analysed in reference both to a research-based framework for describing quality teaching, and to current understandings of motivation in language learning. The analysis of data identifies learning featuring deep knowledge, higher-order thinking, engagement, social support, utilisation of background knowledge, and connectedness, all of which work towards the learners having a sense of autonomy and an imagination of becoming an adult Chinese language user.

Keywords: Chinese pedagogy, digital technologies, motivation, secondary school

Procedia PDF Downloads 250

Authors: Parker Townes, Prabdeep Panesar, Chunlin Liu, Soo Youn Lee, Dan Devoe, Paul D. Arnold, Jennifer Crosbie, Russell Schachar

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Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are impairing childhood neurodevelopmental disorders with problems in executive functions. Executive functions are higher-level mental processes essential for daily functioning and goal attainment. There is genetic and neural overlap between ADHD and ASD. The aim of this meta-analysis was to evaluate if pediatric ASD and ADHD have distinct executive function profiles. This review was completed following Cochrane guidelines. Fifty-eight articles were identified through database searching, followed by a blinded screening in duplicate. A meta-analysis was performed for all task performance metrics evaluated by at least two articles. Forty-five metrics from 24 individual tasks underwent analysis. No differences were found between youth with ASD and ADHD in any domain under direct comparison. However, individuals with ASD and ADHD exhibited deficient attention, flexibility, visuospatial abilities, working memory, processing speed, and response inhibition compared to controls. No deficits in planning were noted in either disorder. Only 11 studies included a group with comorbid ASD+ADHD, making it difficult to determine whether common executive function deficits are a function of comorbidity. Further research is needed to determine if comorbidity accounts for the apparent commonality in executive function between ASD and ADHD.

Keywords: autism spectrum disorder, ADHD, neurocognition, executive function, youth

Procedia PDF Downloads 64

Authors: Camila Lemos Pinto, Juliana Alves Carneiro, Patrícia Borges Botelho, João Felipe Mota

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Sarcopenia, a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, currently affects over 50 million people and increases the risk of adverse outcomes such as physical disability, poor quality of life and death. The aim of this study was to examine the efficacy of creatine supplementation associated with resistance training on muscle mass in the elderly. A 12-week, double blind, randomized, parallel group, placebo controlled trial was conducted. Participants were randomly allocated into one of the following groups: placebo with resistance training (PL+RT, n=14) and creatine supplementation with resistance training (CR + RT, n=13). The subjects from CR+RT group received 5 g/day of creatine monohydrate and the subjects from the PL+RT group were given the same dose of maltodextrin. Participants were instructed to ingest the supplement on non-training days immediately after lunch and on training days immediately after resistance training sessions dissolved in a beverage comprising 100 g of maltodextrin lemon flavored. Participants of both groups undertook a supervised exercise training program for 12 weeks (3 times per week). The subjects were assessed at baseline and after 12 weeks. The primary outcome was muscle mass, assessed by dual energy X-ray absorptiometry (DXA). The secondary outcome included diagnose participants with one of the three stages of sarcopenia (presarcopenia, sarcopenia and severe sarcopenia) by skeletal muscle mass index (SMI), handgrip strength and gait speed. CR+RT group had a significant increase in SMI and muscle (p<0.0001), a significant decrease in android and gynoid fat (p = 0.028 and p=0.035, respectively) and a tendency of decreasing in body fat (p=0.053) after the intervention. PL+RT only had a significant increase in SMI (p=0.007). The main finding of this clinical trial indicated that creatine supplementation combined with resistance training was capable of increasing muscle mass in our elderly cohort (p=0.02). In addition, the number of subjects diagnosed with one of the three stages of sarcopenia at baseline decreased in the creatine supplemented group in comparison with the placebo group (CR+RT, n=-3; PL+RT, n=0). In summary, 12 weeks of creatine supplementation associated with resistance training resulted in increases in muscle mass. This is the first research with elderly of both sexes that show the same increase in muscle mass with a minor quantity of creatine supplementation in a short period. Future long-term research should investigate the effects of these interventions in sarcopenic elderly.

Keywords: creatine, dietetic supplement, elderly, resistance training

Procedia PDF Downloads 460

Authors: M. Tomaschko

Abstract:

The integration of technology in educational settings has gained a lot of interest. Especially the use of mobile devices and accompanying mobile applications can offer great potentials to complement traditional education with new technologies and enrich students’ learning in various ways. Nevertheless, the usability of the deployed mathematics application is an indicative factor to exploit the full potential of technology enhanced learning because directing cognitive load toward using an application will likely inhibit effective learning. For this reason, the purpose of this research study is the identification of possible usability issues of the mobile GeoGebra Graphing Calculator application. Therefore, eye tracking in combination with task scenarios, think aloud method, and a SUS questionnaire were used. Based on the revealed usability issues, the mobile application was iteratively redesigned and assessed in order to verify the success of the usability improvements. In this paper, the identified usability issues are presented, and recommendations on how to overcome these concerns are provided. The main findings relate to the conception of a mathematics keyboard and the interaction design in relation to an equation editor, as well as the representation of geometrical construction tools. In total, 12 recommendations were formed to improve the usability of a mobile graphing calculator application. The benefit to be gained from this research study is not only the improvement of the usability of the existing GeoGebra Graphing Calculator application but also to provide helpful hints that could be considered from designers and developers of mobile math applications.

Keywords: GeoGebra, graphing calculator, math education, smartphone, usability

Procedia PDF Downloads 115