Search results for: institutional memory

Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Carlos Guillermo Lloga Sanz, Maria del Carmen Tamayo Asef

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As an object of study in Cuban universities, cinema is still in its infancy. This is relevant considering the significance of cinema within the local political culture and its impact on countries of the region. Discussions about the medium have been carried out mainly in the field of film criticism. The objective of this article is to reflect on the divergences between film studies and film criticism taking into account formal and theoretical features and to explore the transcendence of this debate for the intellectual ambiance of the Island. Methodologically, the study relies on theoretical elaborations based on literature review and non-structure interviews with Cuban film critics and scholars. The study finds that the gradation proposed by the Anglo-Saxon tradition, where film studies are considered a “higher stage," compared to criticism and cinephilia, does not apply to the Cuban space. Instead, to assess the state of reflection on cinema in Cuba, it is essential to consider it a starry node traversed by epistemic, institutional, and geopolitical matrices.

Keywords: film studies, film criticism, Cuban cinema, Cuban film studies

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Authors: Genevieve Jorolan-Quintero

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The recording as well as the preservation of indigenous literature is an important task as it deals with a significant heritage of pre-colonial culture. The beliefs and traditions of a people are reflected in their oral narratives, such as the folk epic, which must be written down to insure their preservation. The epic poem for instance, known as dawot among the Mandaya, one of the indigenous communities in the southern region of the Philippines, narrates the customs, the ways of life, and the adventures of an ancient people. Nabayra, an expert on Philippine folkloric studies, stresses that still extant after centuries of unknown origin, the dawot was handed down to the magdadawot (bard) by word of mouth, forming the greatest bulk of Mandaya oral tradition. Unhampered by modern means of communication to distract her/him, the magdadawot has a sharp memory of the intricacies of the ancient art of chanting the panayday (verses) of the epic poem. The dawot has several hullubaton (episodes), each of which takes several nights to chant . The language used in these oral traditions is archaic Mandaya, no longer spoken or clearly understood by the present generation. There is urgency to the task of recording and writing down what remain of the epic poem since the singers and storytellers who have retained the memory and the skill of chanting and narrating the dawot and other forms of oral tradition in their original forms are getting fewer. The few who are gifted and skilled to transmit these ancient arts and wisdom are old and dying. Unlike the other Philippine epics (i.e. the Darangen, the Ulahingan, the Hinilawod, etc.), the Mandaya epic is yet to be recognized and given its rightful place among the recorded epics in Philippine Folk Literature. The general aim of this study was to put together and preserve an intangible heritage, the Mandaya hullubaton (episodes of the dawot), in order to preserve and promote appreciation for the oral traditions and cultural legacy of the Mandaya. It was able to record, transcribe, and translate four hullubaton of the folk epic into two languages, Visayan and English to insure understanding of their contents and significance among non-Mandaya audiences. Evident in the contents of the episodes are the cultural practices, ideals, life values, and traditions of the ancient Mandaya. While the conquests and adventures of the Mandaya heroes Lumungtad, Dilam, and Gambong highlight heroic virtues, the role of the Mandaya matriarch in family affairs is likewise stressed. The recording and the translation of the hullubaton and the dawot into commonly spoken languages will not only promote knowledge and understanding about their culture, but will also stimulate in the members of this cultural community a sense of pride for their literature and culture. Knowledge about indigenous cultural system and philosophy derived from their oral literature will serve as a springboard to further comparative researches dealing with indigenous mores and belief systems among the different tribes in the Philippines, in Asia, in Africa, and other countries in the world.

Keywords: Dawot, epic poem, Mandaya, Philippine folk literature

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

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Authors: E. N. R. de Silva

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The main purpose of this research is to set a policy framework for establishing a legal, institutional and social infrastructure that enhances the welfare, health, safety and economic interest of the consumers in Sri Lanka. It will help to develop an approach to continuously and successfully advocate for a consumer protection legal reform agenda and also it is significant as it gives directions to create national consumer protection associations in Sri Lanka. The methodology adopted for this research is purely a qualitative approach and it is generally and specifically categorized. Generally, part of this research looked at the existing laws, regulations and how effective they are in order to protect consumers. It will analyze the consumer protection framework and specially, consumer protection enhanced by the public organizations in Japan. This research offers a model with methods and legal instruments to enforce advocacy group to enhance consumer welfare, also brings out reforms to be made in the national legal framework on consumer welfare.

Keywords: consumer protection association, consumer protection law, consumer welfare, legal framework

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Authors: Hyun Chul Koag, Hyun-Sik Ahn

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In this paper, we propose a design approach for the safety mechanism of an actuator used in a commercial vehicle’s EPS system. As the number of electric/electronic system in a vehicle increases, the importance of the functional safety has been receiving much attention. EPS(Electric Power Steering) systems for commercial vehicles require large power than passenger vehicles, and hence, dual motor can be applied to get more torque. We show how to formulate the development process for the design of hardware and software of an EPS system using dual motors. A lot of safety mechanisms for the processor, sensors, and memory have been suggested, however, those for actuators have not been fully researched. It is shown by metric analyses that the target ASIL(Automotive Safety Integrated Level) is satisfied in the point of view of hardware of EPS controller.

Keywords: safety mechanism, functional safety, commercial vehicles, electric power steering

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Mikel Alonso López, María Francisca Blasco López, Víctor Molero Ayala

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The present study explains the somatic marker theory of Antonio Damasio, which indicates that when making a decision, the stored or possible future scenarios (future memory) images allow people to feel for a moment what would happen when they make a choice, and how this is emotionally marked. This process can be conscious or unconscious. The development of new Neuromarketing techniques such as functional magnetic resonance imaging (fMRI), carries a greater understanding of how the brain functions and consumer behavior. In the results observed in different studies using fMRI, the evidence suggests that the somatic marker and future memories influence the decision-making process, adding a positive or negative emotional component to the options. This would mean that all decisions would involve a present emotional component, with a rational cost-benefit analysis that can be performed later.

Keywords: emotions, decision making, somatic marker, consumer´s brain

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Authors: N. Hadjigeorgiou, A. C. Tsalikidou, E. Hristoforou, P. P. Sotiriadis

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During the last few decades, the continuously increasing demand for accurate and reliable magnetic measurements has paved the way for the development of different types of magnetic sensing systems as well as different measurement techniques. Sensor sensitivity and linearity, signal-to-noise ratio, measurement range, cross-talk between sensors in multi-sensor applications are only some of the aspects that have been examined in the past. In this paper, a fully analog closed loop system in order to optimize the performance of AMR sensors has been developed. The operation of the proposed system has been tested using a Helmholtz coil calibration setup in order to control both the amplitude and direction of magnetic field in the vicinity of the AMR sensor. Experimental testing indicated that improved linearity of sensor response, as well as low noise levels can be achieved, when the system is employed.

Keywords: AMR sensor, closed loop, memory effects, chopper, linearity improvement, sensitivity improvement, magnetic noise, electronic noise

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Authors: Xueqing Ivy Chen, Christo De Coning

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It is clear from practice that a gap exists between Result-Based Monitoring and Evaluation (RBME) as a discipline, and Knowledge Management (KM) on the other hand. Whereas various government departments have institutionalised these functions, KM and M&E has functioned in isolation from each other in a practical sense in the public sector. It’s therefore necessary to explore the relationship between KM and M&E and the necessity for integration, so that a convergence of these disciplines can be established. An integration of KM and M&E will lead to integration and improvement of evidence-based information and policy decision-making. M&E and KM process models are available but the complementarity between specific process steps of these process models are not exploited. A need exists to clarify the relationships between these functions in order to ensure evidence based information and policy decision-making. This paper will depart from the well-known policy process models, such as the generic model and consider recent on the interface between policy, M&E and KM.

Keywords: result-based monitoring and evaluation, RBME, knowledge management, KM, evident based decision making, public policy, information systems, institutional arrangement

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Authors: Gagandeep Kaur

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Cloud computing uses various computing resources such as CPU, memory, processor etc. which is used to deliver service over the network and is one of the emerging fields for large scale distributed computing. In cloud computing, execution of large number of tasks with available resources to achieve high performance, minimal total time for completion, minimum response time, effective utilization of resources etc. are the major research areas. In the proposed research, an algorithm has been proposed to achieve high performance in load balancing and resource utilization. The proposed algorithm is used to reduce the makespan, increase the resource utilization and performance cost for independent tasks. Further scheduling metrics based on algorithm in cloud computing has been proposed.

Keywords: resource utilization, response time, load balancing, performance cost

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Authors: Josephine R. Migalbin

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Halal is an Arabic word meaning "lawful" or "permitted". When it comes to food and consumables, Halal is the dietary standard of Muslims. The Autonomous Region in Muslim Mindanao (ARMM) has a comparative advantage when it comes to Halal Industry because it is the only Muslim region in the Philippines and the natural starting point for the establishment of a halal industry in the country. The region has identified goat production not only for domestic consumption but for export market. Goat production is one of its strengths due to cultural compatibility. There is a high demand for goats during Ramadhan and Eid ul-Adha. The study aimed to provide an overview of the ARMM Halal Goat Industry; to map out the specific supply chain of halal goat, and to analyze the performance of the halal goat supply chain in terms of efficiency, flexibility, and overall responsiveness. It also aimed to identify areas for improvement in the supply chain such as behavioural, institutional, and process to provide recommendations for improvement in the supply chain towards efficient and effective production and marketing of halal goats, subsequently improving the plight of the actors in the supply chain. Generally, the raising of goats is characterized by backyard production (92.02%). There are four interrelated factors affecting significantly the production of goats which are breeding prolificacy, prevalence of diseases, feed abundance and pre-weaning mortality rate. The institutional buyers are mostly traders, restaurants/eateries, supermarkets, and meat shops, among others. The municipalities of Midsayap and Pikit in another region and Parang are the major goat sources and the municipalities in ARMM among others. In addition to the major supply centers, Siquijor, an island province in the Visayas is becoming a key source of goats. Goats are usually gathered by traders/middlemen and brought to the public markets. Meat vendors purchase them directly from raisers, slaughtered and sold fresh in wet markets. It was observed that there is increased demand at 2%/year and that supply is not enough to meet the demand. Farm gate price is 2.04 USD to 2.11 USD/kg liveweight. Industry information is shared by three key participants - raisers, traders and buyers. All respondents reported that information is through personal built-upon past experiences and that there is no full disclosure of information among the key participants in the chain. The information flow in the industry is fragmented in nature such that no total industry picture exists. In the last five years, numerous local and foreign agencies had undertaken several initiatives for the development of the halal goat industry in ARMM. The major issues include productivity which is the greatest challenge, difficulties in accessing technical support channels and lack of market linkage and consolidation. To address the various issues and concerns of the various industry players, there is a need to intensify appropriate technology transfer through extension activities, improve marketing channels by grouping producers, strengthen veterinary services and provide capital windows to improve facilities and reduce logistics and transaction costs in the entire supply chain.

Keywords: autonomous region in Muslim Mindanao, halal, halal goat industry, supply chain improvement

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Harriet Koshie Lamptey, Richard Boateng

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Mobile learning (m-learning) is a novel approach to knowledge acquisition and dissemination and is gaining global attention. Steady progress in wireless technologies and the portability of communication devices continue to broaden the scope and use of mobiles. With the convergence of Web functionality onto mobile platforms and the affordability and availability of mobile technology, m-learning has the potential of being the next prevalent channel of education in both formal and informal settings. There is substantive literature on developed countries but the state in developing countries (DCs) however appears vague. This paper is a synthesis of extant literature on mobile learning in DCs. The research interest is based on the fact that in DCs, mobile communication and internet connectivity are popular. However, its use in education is under explored. There are some reviews on the state, conceptualizations, trends and teacher education, but to the authors’ knowledge, no study has focused on mobile learning adoption and integration issues. This study examines issues and gaps associated with its adoption and integration in DCs higher education institutions. A qualitative build-up of literature was conducted using articles pooled from electronic databases (Google Scholar and ERIC). To enable criteria for inclusion and incorporate diverse study perspectives, search terms used were m-learning, DCs, higher education institutions, challenges, benefits, impact, gaps and issues. The synthesis revealed that though mobile technology has diffused globally, its pedagogical pursuit in DCs remains quite low. The absence of a mobile Web and the difficulty of resource conversion into mobile format due to lack of funding and technical competence is a stumbling block. Again, the lack of established design and implementation rules to guide the development of m-learning platforms in DCs is a hindrance. The absence of access restrictions on devices poses security threats to institutional systems. Negative perceptions that devices are taking over faculty roles lead to resistance in some situations. Resistance to change can be a hindrance to the acceptance and success of new systems. Lack of interest for m-learning is also attributed to lower technological literacy levels of the underprivileged masses. Scholarly works on m-learning in DCs is yet to mature. Most technological innovations are handed down from developed countries, and this constantly creates a lag for DCs. Lack of theoretical grounding was also identified which reduces the objectivity of study reports. The socio-cultural terrain of DCs results in societies with different views and needs that have been identified as a hindrance to research. Institutional commitment decisions, adequate funding for the necessary infrastructural development as well as multiple stakeholder participation is important for project success. Evidence suggests that while adoption decisions are readily made, successful integration of the concept for its full benefits to be realized is often neglected. Recommendations to findings were made to provide possible remedies to identified issues.

Keywords: developing countries, higher education institutions, mobile learning, literature review

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Authors: Santosh Ghosh, Vinod Kumar Yadav, Vivekananda Mukherjee, Ishta Garg

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India, akin to the rest of the world today, is grappling with balancing act between ever increasing demand for energy and alarmingly high level of green house gas emission, which is inevitable corollary of energy production in the conventional way. Researchers and energy policy makers around the world are now focusing on renewable energy (RE) technologies to find solution to this crisis. In India various agencies at both national and state level has been set up and bestowed with responsibility of development of renewable energy technologies, viz. Ministry of New Renewable Energy (MNRE), National Vidyut Vyapar Nigam Ltd. (NVVNL), Indian Renewable Energy Development Agency Limited (IREDA) and RE Development Agencies in respective states. In the present work, the preparedness of India in terms of forming institutional and policy frame work briefly discussed. Status of implementation of RE technologies state wise and of India as a whole, critically reviewed.

Keywords: energy policy, energy sustainability, renewable energy, IREDA

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Authors: Kiran Baldavoo, Mishelle Doorasamy

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Universities have a role to play in the preservation of the environment, and the study attempted to evaluate the environmental management accounting (EMA) processes at UKZN. UKZN, a South African university, generates the same direct and indirect environmental impacts as the higher education sector worldwide. This is significant within the context of the South African environment which is constantly plagued by having to effectively manage the already scarce resources of water and energy, evident through the imposition of water and energy restrictions over the recent years. The study’s aim is to increase awareness of having a structured approach to environmental management in order to achieve the strategic environmental goals of the university. The research studied the experiences of key managers within UKZN, with the purpose of exploring the potential factors which influence the decision to adopt and apply EMA within the higher education sector. The study comprised two objectives, namely understanding the current state of accounting practices for managing major environmental costs and identifying factors influencing EMA adoption within the university. The study adopted a case study approach, comprising semi-structured interviews of key personnel involved in Management Accounting, Environmental Management, and Academic Schools within the university. Content analysis was performed on the transcribed interview data. A Theoretical Framework derived from literature was adopted to guide data collection and focus the study. Contingency and Institutional theory was the resultant basis of the derived framework. The findings of the first objective revealed that there was a distinct lack of EMA utilization within the university. There was no distinct policy on EMA, resulting in minimal environmental cost information being brought to the attention of senior management. The university embraced the principles of environmental sustainability; however, efforts to improve internal environmental accountability primarily from an accounting perspective was absent. The findings of the second objective revealed that five key barriers contributed to the lack of EMA utilization within the university. The barriers being attitudinal, informational, institutional, technological, and lack of incentives (financial). The results and findings of this study supported the use and application of EMA within the higher education sector. Participants concurred that EMA was underutilized and if implemented, would realize significant benefits for both the university and environment. Environmental management accounting is being widely acknowledged as a key management tool that can facilitate improved financial and environmental performance via the concept of enhanced environmental accountability. Historically research has been concentrated primarily on the manufacturing industry, due to it generating the greatest proportion of environmental impacts. Service industries are also an integral component of environmental management as they contribute significant environmental impacts, both direct and indirect. Educational institutions such as universities form part of the service sector and directly impact on the environment through the consumption of paper, energy, and water and solid waste generated, with the associated demands.

Keywords: environmental management accounting, environmental impacts, higher education, Southern Africa

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Authors: Mai M. Farid, Ximeng Yang, Tomoharu Kuboyama, Chihiro Tohda

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Trigonelline is a major alkaloid component derived from Trigonella foenum-graecum L. (fenugreek) and has been reported before as a potential neuroprotective agent, especially in Alzheimer’s disease (AD). However, the previous data were unclear and used model mice were not well established. In the present study, the effect of trigonelline on memory function was investigated in Alzheimer’s disease transgenic model mouse, 5XFAD which overexpresses the mutated APP and PS1 genes. Oral administration of trigonelline for 14 days significantly enhanced object recognition and object location memories. Plasma and cerebral cortex were isolated at 30 min, 1h, 3h, and 6 h after oral administration of trigonelline. LC-MS/MS analysis indicated that trigonelline was detected in both plasma and cortex from 30 min after, suggesting good penetration of trigonelline into the brain. In addition, trigonelline significantly ameliorated axonal and dendrite atrophy in Amyloid β-treated cortical neurons. These results suggest that trigonelline could be a promising therapeutic candidate for AD.

Keywords: alzheimer’s disease, cortical neurons, LC-MS/MS analysis, trigonelline

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Authors: Stephanie K. Douglas, Gordon R. Haley

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Organizations face increasing disruptions, changes, and uncertainties through the rapid shifts in the economy and business environment. A capacity for resilience is necessary for organizations to survive and thrive in such adverse conditions. Learning is an essential component of an organization's capability for building resilience. Strategic human resource management is a principal component of learning and organizational resilience. To achieve organizational resilience, human resource management strategies must support individual knowledge, skills, and ability development through organizational learning. This study aimed to contribute to the comprehensive knowledge of the relationship between strategic human resource management and organizational learning to build organizational resilience. The organizational learning dimensions of knowledge acquisition, knowledge distribution, knowledge interpretation, and organizational memory can be fostered through human resource management strategies and then aggregated to the organizational level to build resilience.

Keywords: human resource development, human resource management, organizational learning, organizational resilience

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Authors: Thusitha B. Abeysekara, M. A. Nihal Chandrathilake

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The aim of this research is to examine the legal mechanism of resolving maritime disputes by arbitration, and it would be a reference point on the analysis of USA approaches. In doing so, the research aims to analyse the relevant legal principles in the context of current maritime arbitration practices in selected jurisdictions. The research also aims to analyse the advantages and applicability of arbitration in maritime dispute settlements over the litigation and further approaches the role of specialist maritime arbitration institutes in the USA and the position of international merchant organizations in maritime arbitration. Further, research values the legislative aspects of maritime arbitration. The study would evaluate the contemporary issues in maritime arbitration practices in the USA and further analyses the statistical information on maritime arbitration. Finally, the research made remarks to often parallel consequence in USA legal systems in maritime arbitration and despite the fundamental divergences of the applicable principles and practices of maritime arbitration. The research finally suggests the doctrine should reshape with equitable remedies and international maritime arbitration practices with its institutional impact rather than using as statutory rules related maritime arbitration.

Keywords: arbitration, international shipping, maritime dispute, New York convention

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss

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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.

Keywords: Alzheimer’s disease, budget, dementia, diagnosis.

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Mohammad Reza Haji Babai, Sepideh Ahmadkhan Beigi

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Internationalization and modernization of the globe have played their roles in the process of cultural interaction between globalized societies and, consequently, found their way to the world of literature under the name of ‘imagology’. Imagology has made it possible for the reader to understand the author’s thoughts and judgments of others. The present research focuses on the intercultural images portrayed in the novel of a popular Turkish-French writer, Elif Shafak, about the lifestyle, traditions, habits, and social norms of Turkish, Americans, and Armenians. The novel seeks to articulate a more intricate multicultural memory of Turkishness by grieving over the Armenian massacre. This study finds that, as a mixture of multiple lifestyles and discourses, The Bastard of Istanbul reflects not only images of oriental culture but also occidental cultures. This means that the author has attempted to maintain selfhood through historical and cultural recollection, which resulted in constructing the self and another identity.

Keywords: imagology, Elif Shafak, The Bastard of Istanbul, self-image, other-image

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Authors: Chaima Babi, Said Gadri

Abstract:

The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

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Authors: Martha Salinas, Valkyria Bernal

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Creativity is a possible response to the profound social, economic, and global changes society is living and education is the source to develop this kind of capacity. However, institutional pressures often prevent teachers from engaging in creative teaching practices and make innovation not the main curricular focus when building learning scenarios and experiences. This study proposes and validates the use of a prototype of Educative Video – Capsules from the perspective of teacher training, presenting the different stages of design, the content plan, as well as the influences of its components and characteristics from the perspective of creativity. The paper presents literature findings of the factors that influence the innovative behavior of teachers, the beliefs of teachers about creativity and its nature, as well as the creative pedagogies that have generated better results. The results show that the disposition of teachers towards creative pedagogies improves significantly with the use of a tool that is based on the principles of microlearning and is developed in a non-academic, autonomous, and non-imposed family environment as traditional teacher training processes usually occur.

Keywords: educational innovation, resistance to innovation, creativity, creative pedagogy

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Authors: Sam Khozama, Ali M. Mayya

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Witthaya Mekhum, Wutthikorn Malikong

Abstract:

The objective of this study is to investigate the increasing desktop computer performance after overclocking central processing unit or CPU by running a computer component at a higher clock rate (more clock cycles per second) than it was designed at the rate of 0.1 GHz for each level or 100 MHz starting at 4000 GHz-4500 GHz. The computer performance is tested for each level with 4 programs, i.e. Hyper PI ver. 0.99b, Cinebench R15, LinX ver.0.6.4 and WinRAR . After the CPU overclock, the computer performance increased. When overclocking CPU at 29% the computer performance tested by Hyper PI ver. 0.99b increased by 10.03% and when tested by Cinebench R15 the performance increased by 20.05% and when tested by LinX Program the performance increased by 16.61%. However, the performance increased only 8.14% when tested with Winrar program. The computer performance did not increase according to the overclock rate because the computer consists of many components such as Random Access Memory or RAM, Hard disk Drive, Motherboard and Display Card, etc.

Keywords: overclock, performance, central processing unit, computer

Procedia PDF Downloads 269

Authors: Alicja Zapolnik-Plachetka

Abstract:

The paper examines the impact of the Members’ of the European Parliament (MEPs) language choice on the linguistic quality of their political discourse as delivered by the interpreters. The study, designed by the author, who is an EU interpreter herself, consisted of three phases. First, a number of speeches of Polish and Spanish MEPs were analyzed to determine whether the incidence of use of certain figures of speech depending on whether the speech had been delivered in English or their respective mother tongue. Then the use of figures of speech was also analyzed based on speeches by some British MEPs, in order to determine what was the incidence for the native users of English. Subsequently, the speeches were compared with their interpretations to find out whether the interpreters managed to convey accurately the means of oratory used by the MEPs. The final result shows that in case of institutional environments dependant on simultaneous interpretation the speakers’ choices can, in fact, influence the linguistic quality of the political communication.

Keywords: content accuracy, European Parliament, political discourse, simultaneous interpreting

Procedia PDF Downloads 117

Authors: Sajjad Rostami-Sani, Mojtaba Valinataj, Amir-Hossein Khojir-Angasi

Abstract:

The cache has an important role in the reduction of access delay between a processor and memory in high-performance embedded systems. In these systems, the energy consumption is one of the most important concerns, and it will become more important with smaller processor feature sizes and higher frequencies. Meanwhile, the cache system dissipates a significant portion of energy compared to the other components of a processor. There are some elements that can affect the energy consumption of the cache such as replacement policy and degree of associativity. Due to these points, it can be inferred that selecting an appropriate configuration for the cache is a crucial part of designing a system. In this paper, we investigate the effect of different cache replacement policies on both cache’s performance and energy consumption. Furthermore, the impact of different Instruction Set Architectures (ISAs) on cache’s performance and energy consumption has been investigated.

Keywords: energy consumption, replacement policy, instruction set architecture, multicore processor

Procedia PDF Downloads 139

Authors: Galit Harel

Abstract:

This article describes the long-term psychoanalytic psychotherapy of a young woman who had experienced trauma during her childhood. The details of the trauma were unknown, as all memory of the trauma had been repressed. Past trauma is analyzable through a prism of transference, dreaming and dreams, mental states, and thinking processes that offer an opportunity to explore and analyze the influence of both reality and fantasy on the patient. The presented case describes a therapeutic process that strives to discover hidden meanings through the unconscious system and illustrates the movement from unconscious to conscious during exploration of the patient’s personal trauma in treatment. The author discusses the importance of classical and contemporary psychoanalytic models of childhood sexual trauma through the discovery of manifest and latent content, unconscious fantasies, and actual events of trauma. It is suggested that the complexity of trauma is clarified by the tension between these models and by the inclusion of aspects of both of them for a complete understanding.

Keywords: dreams, psychoanalytic psychotherapy, thinking processes, transference, trauma

Procedia PDF Downloads 67