Search results for: institutional memory

Authors: Sarah Isabelle Torres

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Using Lefebvre’s (1991) Spatial Triad, this study explores the relationship of geography and cinema and asks the question: how does geography shape the film culture of a regional city center located at the periphery of a country’s capital? This research aims to locate the contemporary film scene of the city in question, Davao City, Mindanao through contextualizing the politics and culture of its tri-people. This study shows that primarily because of local filmmakers' affection and sense of place, progressive films focusing on the tri-people and their struggles mainly due to issues on land have been born. To further understand the city’s film culture, this study maps the following areas: 1) filmmakers and cineastes, 2) films, 3) film festivals, 4) financial stakeholders, 5) institutions, and 6) screening places. From these, the researcher learned that although the local film community has established itself for decades, problems on audience, funding, and institutional support continue to persist. Aside from mapping, this study also explores Davao’s political, economic, and cultural position within the regional and the national arenas.

Keywords: cinema studies, Davao City, film culture, geography, Philippines, place, regional cinema, space

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Authors: Anne Kubai

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Since the Lisbon Summit in 2007, discussion and dialogue on ways of enhancing collaboration between Africa and the EU on the issues of migration, mobility and employment has intensified. The Africa-EU Partnership on migration, mobility and employment aims to provide far-reaching responses on migration and employment challenges; and facilitate mobility of people in Africa and the EU. However, since the outcomes of the proposed policies depend on the political interests and institutional capacities of both the EU and African states that are involved, the results have so far been uncoordinated and scattered. Also, many European countries have eased their entry regulations with regard to highly skilled migrants, and there is need to explore the implications of such changes. Therefore, this contribution will address the following questions: How has the progression of migration and border management in the Nordic countries, particularly Sweden, affected the flow and mobility of highly skilled migrants from Africa? What is the possible impact of the changes in receiving countries (such as introduction of tuition fees and more stringent admission regulations for foreign students in Sweden) on skilled migration and mobility? How can highly skilled immigrants be a source of research knowledge between international and local institutions and researchers both in sending and receiving countries?

Keywords: academic mobility, skilled, African, knowledge, research, migrants, Sweden

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: Jay P. Sipani, Riki H. Patel, Trushit Upadhyaya

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Today there is a demand to monitor environmental factors almost in all research institutes and industries and even for domestic uses. The analog data measurement requires manual effort to note readings, and there may be a possibility of human error. Such type of systems fails to provide and store precise values of parameters with high accuracy. Analog systems are having drawback of storage/memory. Therefore, there is a requirement of a smart system which is fully automated, accurate and capable enough to monitor all the environmental parameters with utmost possible accuracy. Besides, it should be cost-effective as well as portable too. This paper represents the Wireless Sensor (WS) data communication using DHT11, Arduino, SIM900A GSM module, a mobile device and Liquid Crystal Display (LCD). Experimental setup includes the heating arrangement of DHT11 and transmission of its data using Arduino and SIM900A GSM shield. The mobile device receives the data using Arduino, GSM shield and displays it on LCD too. Heating arrangement is used to heat and cool the temperature sensor to study its characteristics.

Keywords: wireless communication, Arduino, DHT11, LCD, SIM900A GSM module, mobile phone SMS

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Authors: Chia-Chi Cheng

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After the implementation of “the internet plus,” several puzzles emerge as below: why does China impose more regulation and laws on economic development on the Internet? Why does China urge the importance of manufacturing industry? Why does China’s local government passively implement the policy imposed by the central government? What kind of factors can influence China’s local government’s economic preference? In the framework of neo-institutionalism, this research considers China’s local government as changing agents to analyze its preferences and behavior. In general, the interests urged by the local government will decide its preference and behaviors. They will change its counterpart to cooperate if the change will bring more benefits. Thus, they will change its preference and behavior while the external environment alters. While the local government has the same definition on political activity and economic interest, they will prefer to cooperate with the local enterprises in the way of laying symbiont, within the presumption that the institution remains. While the local government has the different positions on political activity and economic interest, they will re-define the existed regulation or create new regulation in the condition of institution vacuum. Sequentially, they will replace the targets, and the policy, which does not fit in the Central government’s policy, will emerge.

Keywords: China, institutional change, government enterprise relationship, e-commercial policy

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Authors: Hisyam Hidayatullah

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Utilization game on a lesson needs to be done in order to provide proper motoric learning model to improve students' skills. Approach to the game, as one of the models of a motoric learning, is intended to improve student learning outcomes math trigonometry materials generally that prioritize a Memory or rote. The purpose of this study is producting innovation to improve a cognitive abilities of students in the field, to improve student performance, and ultimately to improve student understanding in determining a value of trigonometry in various quadrants, and it apply a approach to the game Domino "Cassava H154M" who is adopted from cassava game and it has made total revised in cassava content. The game is divided into 3 sessions: sine cassava, cosine cassava and cassava tangent. Researchers using action of research method, which consists of several stages such as: planning, implementation, observation, reporting and evaluation. Researchers found that a game approaches can improve student learning outcomes, enhance students' creativity in terms of their motoric learning, and creating a supportive learning environment.

Keywords: cassava "H154M", motoric, value of trigonometry, quadrant

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Authors: Bryan Young, Andrew Wodehouse, Marion Sheridan

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The availability of working memory has long been identified as a critical aspect of an instructional design. Many conventional instructional procedures impose irrelevant or unrelated cognitive loads on the learner due to the fact that they were created without contemplation, or understanding, of cognitive work load. Learning to physically operate traditional products can be viewed as a learning process akin to any other. As such, many of today's products, such as cars, boats, and planes, which have traditional controls that predate modern user-centered design techniques may be imposing irrelevant or unrelated cognitive loads on their operators. The goal of the research was to investigate the fundamental relationships between physical inputs, resulting actions, and learnability. The results showed that individuals can quickly adapt to input/output reversals across dimensions, however, individuals struggle to cope with the input/output when the dimensions are rotated due to the resulting increase in cognitive load.

Keywords: cognitive load theory, instructional design, physical product interactions, usability design

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Authors: Masood Roohi, Amir Taghavipour

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This paper describes a strategy to develop an energy management system (EMS) for a charge-sustaining power-split hybrid electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit from the advantages of both parallel and series architecture. However, it gets relatively more complicated to manage power flow between the battery and the engine optimally. The applied strategy in this paper is based on nonlinear model predictive control approach. First of all, an appropriate control-oriented model which was accurate enough and simple was derived. Towards utilization of this controller in real-time, the problem was solved off-line for a vast area of reference signals and initial conditions and stored the computed manipulated variables inside look-up tables. Look-up tables take a little amount of memory. Also, the computational load dramatically decreased, because to find required manipulated variables the controller just needed a simple interpolation between tables.

Keywords: hybrid electric vehicles, energy management system, nonlinear model predictive control, real-time

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Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

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Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: Sylvie Michel, François Cocula

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Digital agriculture is an approach that exploits information and communication technologies. These encompass data acquisition tools like mobile applications, satellites, sensors, connected devices, and smartphones. Additionally, it involves transfer and storage technologies such as 3G/4G coverage, low-bandwidth terrestrial or satellite networks, and cloud-based systems. Furthermore, embedded or remote processing technologies, including drones and robots for process automation, along with high-speed communication networks accessible through supercomputers, are integral components of this approach. While farm-level adoption studies regarding digital agricultural technologies have emerged in recent years, they remain relatively limited in comparison to other agricultural practices. To bridge this gap, this study delves into understanding farmers' intention to adopt digital tools, employing the technology, organization, environment framework. A qualitative research design encompassed semi-structured interviews, totaling fifteen in number, conducted with key stakeholders both prior to and following the 2020-2021 COVID-19 lockdowns in France. Subsequently, the interview transcripts underwent thorough thematic content analysis, and the data and verbatim were triangulated for validation. A coding process aimed to systematically organize the data, ensuring an orderly and structured classification. Our research extends its contribution by delineating sub-dimensions within each primary dimension. A total of nine sub-dimensions were identified, categorized as follows: perceived usefulness for communication, perceived usefulness for productivity, and perceived ease of use constitute the first dimension; technological resources, financial resources, and human capabilities constitute the second dimension, while market pressure, institutional pressure, and the COVID-19 situation constitute the third dimension. Furthermore, this analysis enriches the TOE framework by incorporating entrepreneurial orientation as a moderating variable. Managerial orientation emerges as a pivotal factor influencing adoption intention, with producers acknowledging the significance of utilizing digital sales support tools to combat "greenwashing" and elevate their overall brand image. Specifically, it illustrates that producers recognize the potential of digital tools in time-saving and streamlining sales processes, leading to heightened productivity. Moreover, it highlights that the intent to adopt digital sales support tools is influenced by a market mimicry effect. Additionally, it demonstrates a negative association between the intent to adopt these tools and the pressure exerted by institutional partners. Finally, this research establishes a positive link between the intent to adopt digital sales support tools and economic fluctuations, notably during the COVID-19 pandemic. The adoption of sales support tools in agriculture is a multifaceted challenge encompassing three dimensions and nine sub-dimensions. The research delves into the adoption of digital farming technologies at the farm level through the TOE framework. This analysis provides significant insights beneficial for policymakers, stakeholders, and farmers. These insights are instrumental in making informed decisions to facilitate a successful digital transition in agriculture, effectively addressing sector-specific challenges.

Keywords: adoption, digital agriculture, e-commerce, TOE framework

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Ohood Alshammary

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This study explores the current situation of teacher professional development, focusing on challenges experienced by English language teachers at a Saudi Arabian university. The study examines the current context of English language department (ELD) teachers in relation to PD activities available and the nature of the challenges they face in their attempts to engage in PD. The study adopted an interpretive approach to understanding the current situation of teachers working at the English language department (ELD) at one Saudi Arabian university. The study's findings reveal that participating teachers were aware of the significance of PD but were disappointed that the voices of teachers were not heard. The research reveals many challenges; lack of autonomy, insufficient time, heavy workloads, unsupportive working environments, and PD activities that were not considered necessary by the participants. Teachers viewed PD as subject to a top-down system, causing them to feel professionally undermined, lacking autonomy, and forced to comply with university rules. The study makes several recommendations for improving the PD experience and helping raise institutional awareness of the need to encourage teacher engagement and recommend enhancements to ELD teachers' professional development based on teachers' perspectives.

Keywords: adult learning., professional development, PD challenge, teacher perspective

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Hestika D. Waraningrum, Indriani N. Khairunnisa, Nabila Isnandini, Nadine Yasminah, Sekar A. Winesa

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Each individual’s ability to recall, whether they are male or female, is allegedly influenced by the environmental circumstances during the recalling process. The presence of a distraction is one of the environmental variables that affect recall ability in its capacity in the Short Term Memory. This study was made to see the difference in number of words that was successfully recalled by male participants and female participants with the presence of music as a distraction and also without music as a distraction. Data was taken using an experimental procedure from 75 female and male undergraduate students of Universitas Indonesia. The study design used was a 2x2 Factorial ANOVA, which aimed to see the difference between two variables, which were gender (male vs female) and the presence of a distraction (music serving as a distraction vs absence of music). The results indicated that there were no significant mean differences in the ability to recall between male and female participants. There are no significant mean differences between the presence and the absence of music as a distraction, but a significant interaction was found between gender and distraction with the ability to recall.

Keywords: college, gender, music, recall

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: virtualization, OS based virtualization, container based virtualization, hypervisor based virtualization

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Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

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Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

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Authors: Paul M. H. J. Roekaerts

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In recent years, it became clear that much intensive care (ICU) survivors develop a post-intensive care syndrome (PICS) consisting of psychiatric, cognitive and physical problems for a prolonged period after their ICU stay. Physical inactivity and delirium during the ICU stay are the main determinants of the post-ICU PICS. This presentation will focus on delirium, its epidemiology, prevalence, effect on outcome, risk factors and the current standard of care for managing delirium. Because ICU delirium is a predictor of prolonged length-of-stay in the ICU and of death, the use of a delirium prevention bundle (DPB) becomes mandatory in every ICU. In this presentation, a DPB bundle will be discussed consisting of six components: pain, sedation, sleep, sensory and intellectual stimulation, early mobilization, and hydration. For every of the six components, what to do and what not to do will be discussed. The author will present his own institutional policy on pharmacological and non-pharmacological interventions in the management of delirium. The component ‘early mobilization’ will be discussed more in detail, as this component is extremely important in the prevention of delirium as well as in the prevention of the PICS. The author will conclude his presentation with the remaining areas of uncertainties/work and research to be done.

Keywords: delirium, delirium prevention bundle, early mobilisation in intensive care (ICU), post-intensive care syndrome (PICS)

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Authors: Emilia Olivos, R. Arroyave, A. Vargas-Calderon, J. E. Dominguez-Herrera

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More efficient cooling systems are needed to reduce building energy consumption and environmental impact. At present researchers focus mainly on environmentally-friendly magnetic materials and the potential application in cooling devices. The magnetic materials presented in this project belong to a group known as Heusler alloys. These compounds are characterized by a strong coupling between their structure and magnetic properties. Usually, a change in one of them can alter the other, which implies changes in other electronic or structural properties, such as, shape magnetic memory response or the magnetocaloric effect. Those properties and its dependence with external fields make these materials interesting, both from a fundamental point of view, as well as on their different possible applications. In this work, first principles and Monte Carlo simulations have been used to calculate exchange couplings and magnetic properties as a function of an applied magnetic field on Heusler alloys. As a result, we found a large dependence of the magnetic susceptibility, entropy and heat capacity, indicating that the magnetic field can be used in experiments to trigger particular magnetic properties in materials, which are necessary to develop solid-state refrigeration devices.

Keywords: ferromagnetic materials, magnetocaloric effect, materials design, solid state refrigeration

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Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

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Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: connected components, embrace threads, local weighted kernel, structuring elements

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Authors: Jie Li, Nick Manning

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The mass rural-urban migrants in China associated with the urbanization processes bear significant implications on public health, which is an important yet under-researched area. Urban social and built environment, such as noise, air pollution, high population density, and social segregation, has the potential to contribute to mental illness. In China, rural-urban migrants are also faced with institutional discrimination tied to the hukou (household registration) system, through which they are denied of full citizenship to basic social welfare and services, which may elevate the stress of urban living and exacerbate the risks to mental illness. This paper aims to link the sociospatial exclusion, everyday life experiences and its mental health consequences on rural to urban migrants living in the mega-city of Shanghai. More specifically, it asks what the daily experience of being a migrant in Shanghai is actually like, particularly regarding sources of stress from housing, displacement, service accessibility, and cultural conflict, and whether these stresses affect mental health? Secondary data from literature review on migration, urban studies, and epidemiology research, as well as primary data from preliminary field trip observations and interviews are used in the analysis.

Keywords: migration, urbanisation, mental health, China

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

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Authors: Paulo Lopes Henriques, Carla Curado

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According to literature, knowledge sharing behaviors are influenced by organizational values and structures, namely organizational climate. The manuscript examines the antecedents of the knowledge sharing oriented organizational climate. According to theoretical expectations the study adopts the following explanatory conditions: knowledge sharing costs, knowledge sharing incentives, perceptions of knowledge sharing contributing to performance and tenure. The study confronts results considering two groups of firms: nondigital (firms without intranet) vs digital (firms with intranet). The paper applies fsQCA technique to analyze data by using fsQCA 2.5 software (www.fsqca.com) testing several conditional arguments to explain the outcome variable. Main results strengthen claims on the relevancy of the contribution of knowledge sharing to performance. Secondly, evidence brings tenure - an explanatory condition that is associated to organizational memory – to the spotlight. The study provides an original contribution not previously addressed in literature, since it identifies the sufficient conditions sets to knowledge sharing oriented organizational climate using fsQCA, which is, to our knowledge, a novel application of the technique.

Keywords: fsQCA, knowledge sharing oriented organizational climate, knowledge sharing costs, knowledge sharing incentives

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Authors: Armijos-Orellana Ana, González-Calle María, Maldonado-Matute Juan, Guerrero-Maxi Pedro

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Nowadays, the digital economy represents a fundamental element to guarantee economic and social development, whose importance increased significantly with the arrival of the COVID-19 pandemic. However, despite the benefits it offers, it can also be detrimental to those developing countries characterized by a wide digital divide. It is for this reason that the objective of this research was to identify and describe the main characteristics, benefits, and obstacles of the digital economy for Latin American countries. Through a bibliographic review, using the analytical-synthetic method in the period 1995-2021, it was determined that the digital economy could give way to structural changes, reduce inequality, and promote processes of social inclusion, as well as promote the construction and participatory development of organizational structures and institutional capacities in Latin American countries. However, the results showed that the digital economy is still incipient in the region and at least three factors are needed to establish it: joint work between academia, the business sector and the State, greater emphasis on learning and application of digital transformation and the creation of policies that encourage the creation of digital organizations.

Keywords: developing countries, digital divide, digital economy, digital literacy, digital transformation

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Authors: Jennifer Stephens, Nichole Parker, Kristin Petrovic

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To gain a better understanding of how students enrolled in a Bachelor of Nursing (BN) program are educated, faculty members in the BN program at Athabasca University (AU) in Alberta, Canada, developed a 3-phase comprehensive curriculum review project. Phase one of this review centered around hiring an external curriculum expert to examine and analyze the current curriculum and to propose recommendations focused on identifying gaps as well as building on strengths towards meeting changing health care trends. Phase two incorporated extensive institutional document analysis as well as qualitative and quantitative data collection in reciprocated critical reflection and has yielded insights into valuable processes, challenges, and solutions inherent to the complexities of undertaking curriculum review and analysis. Results of our phase one and two analysis generated a quality improvement (QI) template that could benefit other nursing education programs engaged in curriculum review and analysis. The key processes, lessons, and insights, as well as future project phase three plans, will be presented for iterative discussion and role modelling for other institutions undergoing, or planning, content-based curriculum review and evaluation.

Keywords: curriculum, education, nursing, nursing faculty practice, quality improvement

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Authors: Paulo Roberto Vasconcellos-Silva

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The "Janeiro Roxo" (Purple January) campaign in Brazil aims to promote awareness of leprosy and its early symptoms. The COVID-19 pandemic has adversely affected institutional campaigns, mostly considering leprosy a neglected disease by the media. Google Trends (GT) is a tool that tracks user searches on Google, providing insights into the popularity of specific search terms. Our prior research has categorized online searches into two types: "Reactive searches," driven by transient campaign-related stimuli, and "Proactive searches," driven by personal interest in early symptoms and self-diagnosis. Using GT we studied: (i) the impact of "Janeiro Roxo" on public interest in leprosy (assessed through reactive searches) and its early symptoms (evaluated through proactive searches) over the past five years; (ii) changes in public interest during and after the COVID-19 pandemic; (iii) patterns in the dynamics of reactive and proactive searches Methods: We used GT's "Relative Search Volume" (RSV) to gauge public interest on a scale from 0 to 100. "HANSENÍASE" (HAN) was a proxy for reactive searches, and "HANSENÍASE SINTOMAS" (leprosy symptoms) (H.SIN) for proactive searches (interest in leprosy or in self-diagnosis). We analyzed 261 weeks of data from 2018 to 2023, using polynomial trend lines to model trends over this period. Analysis of Variance (ANOVA) was used to compare weekly RSV, monthly (MM) and annual means (AM). Results: Over a span of 261 weeks, there was consistently higher Relative Search Volume (RSV) for HAN compared to H.SIN. Both search terms exhibited their highest (MM) in January months during all periods. COVID-19 pandemic: a decline was observed during the pandemic years (2020-2021). There was a 24% decrease in RSV for HAN and a 32.5% decrease for H.SIN. Both HAN and H.SIN regained their pre-pandemic search levels in January 2022-2023. Breakpoints indicated abrupt changes - in the 26th week (February 2019), 55th and 213th weeks (September 2019 and 2022) related to September regional campaigns (interrupted in 2020-2021). Trend lines for HAN exhibited an upward curve between 33rd-45th week (April to June 2019), a pandemic-related downward trend between 120th-136th week (December 2020 to March 2021), and an upward trend between 220th-240th week (November 2022 to March 2023). Conclusion: The "Janeiro Roxo" campaign, along with other media-driven activities, exerts a notable influence on both reactive and proactive searches related to leprosy topics. Reactive searches, driven by campaign stimuli, significantly outnumber proactive searches. Despite the interruption of the campaign due to the pandemic, there was a subsequent resurgence in both types of searches. The recovery observed in reactive and proactive searches post-campaign interruption underscores the effectiveness of such initiatives, particularly at the national level. This suggests that regional campaigns aimed at leprosy awareness can be considered highly successful in stimulating proactive public engagement. The evaluation of internet-based campaign programs proves valuable not only for assessing their impact but also for identifying the needs of vulnerable regions. These programs can play a crucial role in integrating regions and highlighting their needs for assistance services in the context of leprosy awareness.

Keywords: health communication, leprosy, health campaigns, information seeking behavior, Google Trends, reactive searches, proactive searches, leprosy early identification

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Authors: Kevin Kien Hoa Chung

Abstract:

The present study was to investigate cognitive-linguistic skills that might distinguish the improved dyslexics from the non-improved dyslexics. Twenty-eight improved dyslexics and 28 non-improved dyslexics were selected from a pool of 254 students diagnosed as dyslexics in Grade 1 to 2. These students were administered measures: morphological skills, visual-orthographic skills, rapid naming skills, working memory, reading comprehension, writing, word reading, word dictation, and one-minute word reading. Findings showed that the improved dyslexics performed better than the non-improved dyslexics in visual-orthographic skills, word reading, one-minute reading, writing, and reading comprehension. Furthermore, the improved dyslexics showed fewer cognitive-linguistic deficits compared with the non-improved dyslexics. Among the 4 cognitive-linguistic measures, morphological skills and visual-orthographic skills showed the greatest power in discriminating the improved and non-improved dyslexics. Results underscore the importance of cognitive-linguistic skills underlying the manifestations of the improved and non-improved dyslexia in Chinese adolescents.

Keywords: adolescents, chinese language, improved dyslexics, non-improved dyslexics

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Authors: Abdulah Fajar

Abstract:

Electronic Procurement System has been implemented at government institution in Indonesia. This system has been developed centrally at Institution of National Procurement Policy (LKPP) and implemented autonomously at either local or national government institution. The lack of competency at many institution on Information Technology Management arise several major problems. The main concern of LKPP to local administrator is assured that the system is running normally and always be able to serve the needs of its users. Monitoring system has been identified as the one of solution to prevent the problems appeared. Monitoring system is developed using Simple Network Management Protocol (SNMP) and implemented at LKPP. There are two modules; Main Dashboard and Local Agent. Main Dashboard is intended for LKPP and Local Agent is intended to implement at local autonomous e-procurement system (LPSE). There are several resources that must be monitored such as computation, memory and network traffic. Agile paradigm is applied to this project to assure user and system requirement is met. The length of project is the one of reason why agile paradigm has been chosen. The system has been successfully delivered to LKPP.

Keywords: procurement system, SNMP, LKPP, LPSE

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Authors: Nebiat Lemenih Lenger

Abstract:

Today, the digital economy is evolving faster than ever in Ethiopia. Platforms that provide a ride-hailing service are growing fast in the country. The market welcomed them as they disrupt it with quality services and lower prices. This revolution is, however, not without challenges. These include cybersecurity breaches, facilitating illegal economic activities, and challenging concepts of privacy. To mitigate the risks and utilize the benefits, appropriate regulation should be introduced in the economy. By identifying legal and institutional gaps in Ethiopia`s digital economy, this research work assists the government`s effort to create a better digital economy. Moreover, this study, being a pioneer study in the area, will be an input for further studies in academia. The research employs a qualitative legal research method and analyzes various legal and policy instruments in Ethiopia in comparison with best international experiences. As this research applies a qualitative research method, a grounded theory method of data analysis is used. The research concluded that Ethiopia is far from designing appropriate legal and regulatory infrastructures. Due to the government monopoly of the sector, there is poor digital infrastructure in the country. The existing labor laws have no specific provisions on the rights and obligations of gig workers.

Keywords: Ethiopia, gig economy, digital, ride-hailing, regulation

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Authors: J. Jayasuriya

Abstract:

In this research paper, a procedure for installing and assessment of a solar PV plant to generate optimal solar energy SPAV hostel at Vijayawada city was analyzed. The hostel was experiencing power disruption and had a need for an unceasing energy source. The solar panel is one of the best solutions to obtain uninterrupted clean renewable energy for an institutional building as it neither makes din nor pollutes the atmosphere. The electricity usage per month was initially measured to discriminate the energy change. The solar array was installed with its financial and environmental assessment considering recent market prices. All the aspects related to a solar PV plant were considered for the feasibility and efficiency of PV plant near this site i.e., the orientation of the site, the size and shape of the terrace, the sun path were considered while installing panels. Various precautions were taken to intercept the factors which cause interference in energy generation, with respect to temperature, overshadowing, the wiring of panels, pollution etc. The solar panels were frequently installed, monitored and maintained properly to procure optimal energy output. Result obtained with the assessment of the proposed plant and deflation in the electric bill will show the maximal energy that can be generated in a month on that particular site.

Keywords: solar efficiency, building sustainability, PV panel, solar energy

Procedia PDF Downloads 136