Search results for: ram memory

Authors: G. A. Ravishankar, A. Ramakrishna

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Melatonin (MEL) and Serotonin (SER), also known as [5-Hydroxytryptamine (5-HT)] are reported to be in a range of plant types which are edible. Their occurrence in plants species appears to be ubiquitous. Their presence in high quantities in plants assumes significance owing to their physiological effects upon consumption by human beings. MEL is a well known animal hormone mainly released by the pineal gland known to influence circadian rhythm, sleep, apart from immune enhancement. Similarly, SER is a neurotransmitter that regulates mood, sleep and anxiety in mammals. It is implicated in memory, behavioral changes, scavenging reactive oxygen species, antipsychotic, etc. Similarly Role of SER and MEL in plant morphogenesis, and various physiological processes through intense research is beginning to unfold. These molecules are in common foods viz banana, pineapple, plum, nuts, milk, grape wine. N- Feruloyl serotonin and p-coumaroyl serotonin found in certain seeds are found to possess antioxidant, anti-inflammatory, antitumor, antibacterial, and anti-stress potential apart from reducing depression and anxiety. MEL is found in Mediterranean diets, nuts, cherries, tomato berries, and olive products. Consumption of foods rich in MEL is known to increase blood MEL levels which have been implicated in protective effect against cardiovascular damage, cancer initiation and growth. MEL is also found in wines, green tea, beer, olive oil etc. Moreover, presence of SER and MEL in Coffee beans (green and roasted beans) and decoction has been reported us. In this communication we report the occurrence of indole amines in edible plants and their implications in human health.

Keywords: serotonin, melatonin, edible plants, neurotransmitters, physiological effects

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Authors: Chapman Eze Nnadozie

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Cloud computing, a technology that is made possible through virtualization within networks represents a shift from the traditional ownership of infrastructure and other resources by distinct organization to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. In other words, cloud computing entails the renting of computing resources (such as storage space, memory, servers, applications, networks, etc.) by a third party to its clients on a pay-as-go basis. It is a new innovative technology that is globally embraced because of its renowned benefits, profound of which is its cost effectiveness on the part of organizations engaged with its services. In Nigeria, the services are provided either directly to companies mostly by the key IT players such as Microsoft, IBM, and Google; or in partnership with some other players such as Infoware, Descasio, and Sunnet. This action enables organizations to rent IT resources on a pay-as-you-go basis thereby salvaging them from wastages accruable on acquisition and maintenance of IT resources such as ownership of a separate data centre. This paper intends to appraise the challenges of cloud computing adoption in Nigeria, bearing in mind the country’s peculiarities’ in terms of infrastructural development. The methodologies used in this paper include the use of research questionnaires, formulated hypothesis, and the testing of the formulated hypothesis. The major findings of this paper include the fact that there are some addressable challenges to the adoption of cloud computing in Nigeria. Furthermore, the country will gain significantly if the challenges especially in the area of infrastructural development are well addressed. This is because the research established the fact that there are significant gains derivable by the adoption of cloud computing by organizations in Nigeria. However, these challenges can be overturned by concerted efforts in the part of government and other stakeholders.

Keywords: cloud computing, data centre, infrastructure, it resources, virtualization

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Authors: José Eduardo García-Mendiola

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In this work, a phenomenological approach to computational modeling of analogy processing is carried out. The paper goes through the consideration of the structure of the analogy, based on the possibility of sustaining the genesis of its elements regarding Husserl's genetic theory of association. Among particular processes which take place in order to get analogical inferences, there is one which arises crucial for enabling efficient base cases retrieval through long-term memory, namely analogical transference grounded on familiarity. In general, it has been argued that analogical reasoning is a way by which a conscious agent tries to determine or define a certain scope of objects and relationships between them using previous knowledge of other familiar domain of objects and relations. However, looking for a complete description of analogy process, a deeper consideration of phenomenological nature is required in so far, its simulation by computational programs is aimed. Also, one would get an idea of how complex it would be to have a fully computational account of the analogy elements. In fact, familiarity is not a result of a mere chain of repetitions of objects or events but generated insofar as the object/attribute or event in question is integrable inside a certain context that is taking shape as functionalities and functional approaches or perspectives of the object are being defined. Its familiarity is generated not by the identification of its parts or objective determinations as if they were isolated from those functionalities and approaches. Rather, at the core of such a familiarity between entities of different kinds lays the way they are functionally encoded. So, and hoping to make deeper inroads towards these topics, this essay allows us to consider that cognitive-computational perspectives can visualize, from the phenomenological projection of the analogy process reviewing achievements already obtained as well as exploration of new theoretical-experimental configurations towards implementation of analogy models in specific as well as in general purpose machines.

Keywords: analogy, association, encoding, retrieval

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Authors: Garima Joshi, Pratap Sharan, V. Sreenivas, Nand Kumar, Kameshwar Prasad, Ashima N. Wadhawan

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Schizophrenia is a debilitating disorder and is marked by cognitive impairment, which deleteriously impacts the social and professional functioning along with the quality of life of the patients and the caregivers. Often the cognitive symptoms are in their prodromal state and worsen as the illness progresses; they have proven to have a good predictive value for the prognosis of the illness. It has been shown that intensive cognitive rehabilitation (CR) leads to improvements in the healthy as well as cognitively-impaired subjects. As the majority of population in India falls in the lower to middle socio-economic status and have low education levels, using the existing packages, a majority of which are developed in the West, for cognitive rehabilitation becomes difficult. The use of technology is also restricted due to the high costs involved and the limited availability and familiarity with computers and other devices, which pose as an impedance for continued therapy. Cognitive rehabilitation in India uses a plethora of retraining methods for the patients with schizophrenia targeting the functions of attention, information processing, executive functions, learning and memory, and comprehension along with Social Cognition. Psychologists often have to follow an integrative therapy approach involving social skills training, family therapy and psychoeducation in order to maintain the gains from the cognitive rehabilitation in the long run. This paper reviews the methodologies and cognitive retaining programs used in India. It attempts to elucidate the evolution and development of methodologies used, from traditional paper-pencil based retraining to more sophisticated neuroscience-informed techniques in cognitive rehabilitation of deficits in schizophrenia as home-based or supervised and guided programs for cognitive rehabilitation.

Keywords: schizophrenia, cognitive rehabilitation, neuropsychological interventions, integrated approached to rehabilitation

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Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

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Authors: Sandhya Rao

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Kevin Lynch’s Theory on Imeageability, let on explore a city in terms of five elements, Nodes, Paths, Edges, landmarks and Districts. What happens when we try to record the same data in an Indian context? What happens when we apply the same theory of Imageability to a complex shifting urban pattern of the Indian cities and how can we as Urban Designers demonstrate our role in the image building ordeal of these cities? The organizational patterns formed through mental images, of an Indian city is often diverse and intangible. It is also multi layered and temporary in terms of the spirit of the place. The pattern of images formed is loaded with associative meaning and intrinsically linked with the history and socio-cultural dominance of the place. The embedded memory of a place in one’s mind often plays an even more important role while formulating these images. Thus while deriving an image of a city one is often confused or finds the result chaotic. The images formed due to its complexity are further difficult to represent using a single medium. Under such a scenario it’s difficult to derive an output of an image constructed as well as make design interventions to enhance the legibility of a place. However, there can be a combination of tools and methods that allows one to record the key elements of a place through time, space and one’s user interface with the place. There has to be a clear understanding of the participant groups of a place and their time and period of engagement with the place as well. How we can translate the result obtained into a design intervention at the end, is the main of the research. Could a multi-faceted cognitive mapping be an answer to this or could it be a very transient mapping method which can change over time, place and person. How does the context influence the process of image building in one’s mind? These are the key questions that this research will aim to answer.

Keywords: imageability, organizational patterns, legibility, cognitive mapping

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Authors: Chrysovalanto Sofia Karatosidi, Dimitra Iordanoglou

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The overlap of leadership skills with personality traits, beliefs, values, and the integration of cognitive abilities, analytical and critical thinking skills into leadership competencies raises the need to segregate further and investigate them. Hence, the domains of cognitive functions that contribute to leadership effectiveness should also be identified. Organizational cognitive neuroscience and neuroleadership can shed light on the study of these critical leadership skills. As the first part of our research, this pilot study aims to explore the relationships between higher-order cognitive functions (executive functions), trait emotional intelligence (EI), personality, and general cognitive ability in leadership. Twenty-six graduate and postgraduate students were assessed on neuropsychological tests that measure important aspects of executive functions (EF) and completed self-reported questionnaires about trait EI, personality, leadership styles, and leadership effectiveness. Specifically, we examined four core EF—fluency (phonemic and semantic), information updating and monitoring, working memory, and inhibition of prepotent responses. Leadership effectiveness was positively associated with phonemic fluency (PF), which involves mental flexibility, in turn, an increasingly important ability for future leaders in this rapidly changing world. Transformational leadership was positively associated with trait EI, extraversion, and openness to experience, a result that is following previous findings. The relationship between specific EF constructs and leadership effectiveness emphasizes the role of higher-order cognitive functions in the field of leadership as an individual difference. EF brings a new perspective into leadership literature by providing a direct, non-invasive, scientifically-valid connection between brain function and leadership behavior.

Keywords: cognitive neuroscience, emotional intelligence, executive functions, leadership

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Wajih Abbassi, Zouhaier Ben Khelifa

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The present research points towards the empirical validation of three options valuation models, the ad-hoc Black-Scholes model as proposed by Berkowitz (2001), the constant elasticity of variance model of Cox and Ross (1976) and the Kou jump-diffusion model (2002). Our empirical analysis has been conducted on a sample of 26,974 options written on three indexes, the S&P 500, Nasdaq 100 and the Russell 2000 that were negotiated during the year 2007 just before the sub-prime crisis. We start by presenting the theoretical foundations of the models of interest. Then we use the technique of trust-region-reflective algorithm to estimate the structural parameters of these models from cross-section of option prices. The empirical analysis shows the superiority of the Kou jump-diffusion model. This superiority arises from the ability of this model to portray the behavior of market participants and to be closest to the true distribution that characterizes the evolution of these indices. Indeed the double-exponential distribution covers three interesting properties that are: the leptokurtic feature, the memory less property and the psychological aspect of market participants. Numerous empirical studies have shown that markets tend to have both overreaction and under reaction over good and bad news respectively. Despite of these advantages there are not many empirical studies based on this model partly because probability distribution and option valuation formula are rather complicated. This paper is the first to have used the technique of nonlinear curve-fitting through the trust-region-reflective algorithm and cross-section options to estimate the structural parameters of the Kou jump-diffusion model.

Keywords: jump-diffusion process, Kou model, Leptokurtic feature, trust-region-reflective algorithm, US index options

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Authors: Sunil Kumar, Vinay Kumar, Mamta Bulla, Rita Dahiya

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Supercapacitors have emerged as a leading energy storage technology, gaining popularity in applications like digital telecommunications, memory backup, and hybrid electric vehicles. Their appeal lies in a long cycle life, high power density, and rapid recharge capabilities. These exceptional traits attract researchers aiming to develop advanced, cost-effective, and high-energy-density electrode materials for next-generation energy storage solutions. Two-dimensional (2D) nanostructures are highly attractive for fabricating nanodevices due to their high surface-to-volume ratio and good compatibility with device design. In the current study, a composite was synthesized by combining MoS2 with reduced graphene oxide (rGO) under optimal conditions and characterized using various techniques, including XRD, FTIR, SEM and XPS. The electrochemical properties of the composite material were assessed through cyclic voltammetry, galvanostatic charging-discharging and electrochemical impedance spectroscopy. The supercapacitor device demonstrated a specific capacitance of 153 F g-1 at a current density of 1 Ag-1, achieving an excellent energy density of 30.5 Wh kg-1 and a power density of 600 W kg-1. Additionally, it maintained excellent cyclic stability over 5000 cycles, establishing it as a promising candidate for efficient and durable energy storage solutions. These findings highlight the dynamic relationship between electrode materials and offer valuable insights for the development and enhancement of high-performance symmetric devices.

Keywords: 2D material, energy density, galvanostatic charge-discharge, hydrothermal reactor, specific capacitance

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Wang Qiaowei, Gao Yujiang

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With the economic growth and rapid urbanization after the reform and openness, some of China's fast-growing cities have demolished former dwellings and built modern residential quarters. The blind, incomplete reference to western modern cities and the one-off construction lacking feedback mechanism have intensified such phenomenon, causing the citizen gradually expanded their living scale with the popularization of car traffic, and the peer-to-peer lifestyle gradually settled. The construction of large-scale commercial centers has caused obstacles to small business around the residential areas, leading to space for residents' interaction has been compressed. At the same time, the advocated Central Business District (CBD) model even leads to the unsatisfactory reconstruction of many historical blocks such as the Hangzhou Southern Song Dynasty Imperial Street. However, the popularity of historical spaces such as Wuzhen and Hongcun also indicates the collective memory and needs of the street space for Chinese residents. The evolution of Shanghai TianZiFang also proves the importance of the motivation of space participants in space construction in the context of the “top-down” construction model in China. In fact, there are frequent occurrences of “reconstruction”, which may redefine the space, in various residential areas. If these activities can be selectively controlled and encouraged, it will be beneficial to activate the public space as well as the residents’ intercourse, so that the traditional Chinese street space can be reconstructed in the context of modern cities.

Keywords: rapid urbanization, traditional street space, space re-construction, bottom-up design

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Authors: Sultan Alharby, Nick Harris, Alex Weddell, Jeff Reeve

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The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Keywords: energy consumption, IEEE 802.15.4, IoT security, security cost evaluation

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: L. Freeborn

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This paper discusses the application of complex dynamic system theory (DST) to the study of individual differences in second language development. This transdisciplinary framework allows researchers to view the trajectory of language development as a dynamic, non-linear process. A DST approach views language as multi-componential, consisting of multiple complex systems and nested layers. These multiple components and systems continuously interact and influence each other at both the macro- and micro-level. Dynamic systems theory aims to explain and describe the development of the language system, rather than make predictions about its trajectory. Such a holistic and ecological approach to second language development allows researchers to include various research methods from neurological, cognitive, and social perspectives. A DST perspective would involve in-depth analyses as well as mixed methods research. To illustrate, a neurobiological approach to second language development could include non-invasive neuroimaging techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate areas of brain activation during language-related tasks. A cognitive framework would further include behavioural research methods to assess the influence of intelligence and personality traits, as well as individual differences in foreign language aptitude, such as phonetic coding ability and working memory capacity. Exploring second language development from a DST approach would also benefit from including perspectives from the field of applied linguistics, regarding the teaching context, second language input, and the role of affective factors such as motivation. In this way, applying mixed research methods from neurobiological, cognitive, and social approaches would enable researchers to have a more holistic view of the dynamic and complex processes of second language development.

Keywords: dynamic systems theory, mixed methods, research design, second language development

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Authors: Uranus Saadat

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ChatGPT, as one of the well-known artificial intelligence (AI) tools, has recently been integrated into English language education and has had several impacts on learners. Accordingly, concerns regarding the overuse of this tool among EFL/ESL learners are rising, which could lead to several disadvantages in their writing skills development. The use of ChatGPT in facilitating writing skills is a novel concept that demands further studies in different contexts and learners. In this study, a qualitative case study is applied to investigate the impact of ChatGPT on the writing skills of a group of EFL bachelor’s students from Uzbekistan studying Teaching English as the Second Language (TESL) at a private university in Malaysia. The data was collected through the triangulation of document analysis, semi-structured interviews, classroom observations, and focus group discussions. Subsequently, the data was analyzed by using thematic analysis. Some of the emerging themes indicated that ChatGPT is helpful in engaging students by reducing their anxiety in class and providing them with constructive feedback and support. Conversely, certain emerging themes revealed excessive reliance on ChatGPT, resulting in a decrease in students’ creativity and critical thinking skills, memory span, and tolerance for ambiguity. The study suggests a number of strategies to alleviate its negative impacts, such as peer review activities, workshops for familiarizing students with AI, and gradual withdrawal of AI support activities. This study emphasizes the need for cautious AI integration into English language education to cultivate independent learners with higher-order thinking skills.

Keywords: ChatGPT, EFL/ESL learners, English writing skills, artificial intelligence tools, critical thinking skills

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Authors: Gabriella Dravecz, László Péter, Zsolt Kis

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Abstract— The growth of optical LiNbO3 single crystal and its physical and chemical properties are well known on the macroscopic scale. Nowadays the rare-earth doped single crystals became important for coherent quantum optical experiments: electromagnetically induced transparency, slow down of light pulses, coherent quantum memory. The expansion of applications is increasingly requiring the production of nano scaled LiNbO3 particles. For example, rare-earth doped nanoscaled particles of lithium niobate can be act like single photon source which can be the bases of a coding system of the quantum computer providing complete inaccessibility to strangers. The polyol method is a chemical synthesis where oxide formation occurs instead of hydroxide because of the high temperature. Moreover the polyol medium limits the growth and agglomeration of the grains producing particles with the diameter of 30-200 nm. In this work nano scaled LiNbO3 was prepared by the polyol method. The starting materials (niobium oxalate and LiOH) were diluted in H2O2. Then it was suspended in ethylene glycol and heated up to about the boiling point of the mixture with intensive stirring. After the thermal equilibrium was reached, the mixture was kept in this temperature for 4 hours. The suspension was cooled overnight. The mixture was centrifuged and the particles were filtered. Dynamic Light Scattering (DLS) measurement was carried out and the size of the particles were found to be 80-100 nms. This was confirmed by Scanning Electron Microscope (SEM) investigations. The element analysis of SEM showed large amount of Nb in the sample. The production of LiNbO3 nano particles were succesful by the polyol method. The agglomeration of the particles were avoided and the size of 80-100nm could be reached.

Keywords: lithium-niobate, nanoparticles, polyol, SEM

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Authors: Junita Batubara

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Children with special needs, especially those with disability in mental, physical or social/emotional interactions, are marginalized. Many people still view them as troublesome, inconvenience, having learning difficulties, unproductive and burdensome to society. This study intends to investigate; how musical drama can develop the ability to control the coordination of mental functions; how musical dramas can assist children to work together; how musical dramas can assist to maintain the child's emotional and physical health; how musical dramas can improve children creativity. The objectives of the research are: To know whether musical drama can control the coordination of mental function of children; to know whether musical drama can improve communication ability and expression of children; to know whether musical drama can help children work with people around them; to find out if musical dramas can develop the child's emotional and physical health; to find out if musical drama can improve children's creativity. The study employed a qualitative research approach. Data was collecting by listening, observing in depth through public hearings that select the key informants who were teachers and principals, parents and children. The data obtained from each public hearing was then processed (reduced), conclusion drawing/verification, presentation of data (data display). Furthermore, the model obtained was implementing for musical performance, where the benefits of the show are: musical drama can improve language skills; musical dramas are capable of developing memory and storage of information; developing communication skills and express themselves; helping children work together; assisting emotional and physical health; enhancing creativity.

Keywords: children Down syndrome, music, drama script, performance

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Authors: Ayan Handulle, Memory J. Tembo-Pankuku

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The social work profession utilizes Western and Eurocentric perspectives on social structures, culture, history, belief systems, and education. This affects social work practice with indigenous groups as well as other minorities who have different perspectives. Some of the challenges that characterize social work with families, especially immigrants in western countries, are a result of different world views on child-rearing practices in the global north and the global south. A shift towards cultural sensitivity and the promotion of cultural competence has been a move towards addressing some of the challenges in child welfare practice with immigrants. However, emphasis on cultural differences presents other challenges of stereotyping and discrimination, which call for the examination of current practices to fit other groups of people. In this paper, we introduce the need for emancipatory social work in child welfare practice with immigrant parents. Emancipatory social work is directed at heightening awareness of external sources of oppression and/or privilege that hold the possibility of increasing self-esteem and courage to confront structural sources of marginalization, oppression, and exclusion. This paper draws on two research projects, respectively, “Immigrant parents’ perceptions and experiences of the welfare system” and “Norwegian- Somali parents’ fears of the Norwegian Child welfare service. The first data set comprises 15 in-depth interviews with 18 nonWestern immigrant parents, representing 10 families. The second data set consists of nine months of ethnography, seven months in Oslo, and two months in Somalia among returnees from Norway. Based on these data sets, we explore how immigrant parents’ child-rearing practices might be perceived through a racialized lens.

Keywords: child welfare, immigrants, racialization, social work

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Authors: Walter Rafael Ramos Villanueva

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The overwhelming majority of scholarship on David Chariandy’s novel Soucouyant focuses on how Adele’s dementia represents the preservation of “cultural memory” and the perniciousness of “historical trauma.” However, by metaphorizing Adele’s mental condition, these critics risk treating her dementia as mostly figurative, and they thus elide a more detailed discussion of the literal ramifications of her dementia diagnosis. To move beyond these readings, then, my paper will approach Adele’s disorder as a literal medical condition and explore how her caregiving needs affect not only her but also those around her. Soucouyant subverts traditional caregiving narratives by depicting the difficult and typically invisible labour of informal caregiving that is undertaken by the families and friends of those who are ill or otherwise disabled. Because Adele’s family is unable to access proper public healthcare resources within the community, the burden of care falls upon the protagonist and his brother, who become “parentified children.” Parentified children, according to Nancy D. Chase, are “parents to their parents, and fulfill this role at the expense of their own developmentally appropriate needs and pursuits.” The novel provides a depiction of informal caregiving that is multi-faceted and asks us to question why is it exactly that we place the burden of care on those who are not equipped to handle such pressures instead of putting the onus on the government and the public healthcare system to take care of its most vulnerable members. Ageing Studies scholar Larry Polvika notes that although policymakers often offer “pious expressions of appreciation” and acknowledge that informal caregiving is “the backbone of our long-term care system,” governmental support for these caregivers remains inadequate. It is my belief that, by showcasing the struggles of informal caregivers, Chariandy’s text combats this dangerous and empty political rhetoric.

Keywords: caregiving, dementia, literature, parentified children

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Authors: Hossam Hegazi

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Global culture museums play a pivotal role in fostering cross-cultural connections, enhancing mutual understanding, and safeguarding the rich tapestry of cultural heritage. These institutions serve as dynamic bridges, facilitating the exchange of ideas and values among diverse societies. One of the primary functions of global culture museums is to connect people from different backgrounds. By showcasing the artistic expressions, traditions, and historical artifacts of various civilizations, these museums create a shared space for dialogue. Visitors are afforded the opportunity to explore and appreciate the nuances of cultures different from their own, promoting a sense of global interconnectedness. Moreover, these museums contribute significantly to mutual understanding. Through interactive exhibits, innovative technologies, and educational programs, they offer immersive experiences that transcend linguistic and geographical barriers. Visitors gain insights into the customs, beliefs, and lifestyles of others, fostering empathy and appreciation for cultural diversity. Preserving cultural heritage stands as another key objective of global culture museums. By housing and curating artifacts, artworks, and historical items, these institutions play a crucial role in safeguarding the collective memory of humanity. This preservation effort ensures that future generations have access to the cultural legacies that have shaped societies across the globe. In conclusion, global culture museums serve as dynamic hubs that bring people together, promote understanding, and safeguard the wealth of human cultural heritage. Their impact extends beyond the walls of exhibition halls, contributing to a more interconnected and culturally enriched world.

Keywords: global culture museums, cross-cultural connections, mutual understanding, societal dialogue

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Authors: Jorge Duarte de Sá

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The project presented in this investigation is part of the multidisciplinary field of Architecture and explores an experience in media architecture, integrated in Arts, Science and Technology. The objective of this work is to create a visual experience comprehending Architecture, Media and Art. It is intended to specifically explore the sacred spaces that are losing social, cultural or religious dynamics and insert new Media technologies to create a new generate momentum, testing tools, techniques and methods of implementation. Given an architectural project methodology, it seems essential that 'the location' should be the starting point for the development of this technological apparatus: the church of Santa Clara in Santarém, Portugal emerged as an experimental space for apparatus, presenting itself as both temple and museum. We also aim to address the concept of rehabilitation through media technologies, directed at interventions that may have an impact on energizing spaces. The idea is emphasized on the rehabilitation of spaces that, one way or another, may gain new dynamics after a media intervention. Thus, we intend to affect the play with a sensitive and spiritual character which endemically, sacred spaces have, by exploring a sensitive aspect of the subject and drawing up new ideas for meditation and spiritual reflection. The work is designed primarily as a visual experience that encompasses the space, the object and the subject. It is a media project supported by a dual structure with two transparent screens operating in a holographic screen which will be projecting two images that complement the translucent overlay film, thus making the merger of two projections. The digitally created content reacts to the presence of observers through infrared cameras, placed strategically. The object revives the memory of the altarpiece as an architectural surface, promoting the expansion of messages through the media technologies.

Keywords: architecture, media, sacred, technology

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Authors: Kayode A. Olaniyi, Adeola A. Ogunleye, Tola M. Osifeko

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Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.

Keywords: battery state estimation, hybrid electric vehicle, hybrid energy storage, state of charge, state of health

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: O. V. Vyazovska

Abstract:

Behavioral efficiency is one of the main principles to be successful in nature. Accuracy of visual discrimination is determined by the attention, learning experience, and memory. In the experimental condition, pigeons’ responses to visual stimuli presented on the screen of the monitor are behaviorally manifested by pecking or not pecking the stimulus, by the number of pecking, reaction time, etc. The higher the probability of rewarding is, the more likely pigeons will respond to the stimulus. We trained 8 pigeons (Columba livia) on a stagewise go/no-go visual discrimination task.16 visual stimuli were created from all possible combinations of four binary dimensions: brightness (dark/bright), size (large/small), line orientation (vertical/horizontal), and shape (circle/square). In the first stage, we presented S+ and 4 S-stimuli: the first that differed in all 4-dimensional values from S+, the second with brightness dimension sharing with S+, the third sharing brightness and orientation with S+, the fourth sharing brightness, orientation and size. Then all 16 stimuli were added. Pigeons rejected correctly 6-8 of 11 new added S-stimuli at the beginning of the second stage. The results revealed that pigeons’ behavior at the beginning of the second stage was controlled by probabilities of rewarding for 4 dimensions learned in the first stage. More or fewer mistakes with dimension discrimination at the beginning of the second stage depended on the number S- stimuli sharing the dimension with S+ in the first stage. A significant inverse correlation between the number of S- stimuli sharing dimension values with S+ in the first stage and the dimensional learning rate at the beginning of the second stage was found. Pigeons were more confident in discrimination of shape and size dimensions. They made mistakes at the beginning of the second stage, which were not associated with these dimensions. Thus, the received results help elucidate the principles of dimensional stimulus control during learning compound multidimensional visual stimuli.

Keywords: visual go/no go discrimination, selective attention, dimensional stimulus control, pigeon

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Authors: Debabrata Bhadra, B. K. Chaudhuri

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The operation of organic thin film transistors (OFETs) with low voltage is currently a prevailing issue. We have fabricated anthracene thin-film transistor (TFT) with an ultrathin layer (~450nm) of Poly-vinylidene fluoride (PVDF)/CuO nanocomposites as a gate insulator. We obtained a device with excellent electrical characteristics at low operating voltages (<1V). Different layers of the film were also prepared to achieve the best optimization of ideal gate insulator with various static dielectric constant (εr ). Capacitance density, leakage current at 1V gate voltage and electrical characteristics of OFETs with a single and multi layer films were investigated. This device was found to have highest field effect mobility of 2.27 cm2/Vs, a threshold voltage of 0.34V, an exceptionally low sub threshold slope of 380 mV/decade and an on/off ratio of 106. Such favorable combination of properties means that these OFETs can be utilized successfully as voltages below 1V. A very simple fabrication process has been used along with step wise poling process for enhancing the pyroelectric effects on the device performance. The output characteristic of OFET after poling were changed and exhibited linear current-voltage relationship showing the evidence of large polarization. The temperature dependent response of the device was also investigated. The stable performance of the OFET after poling operation makes it reliable in temperature sensor applications. Such High-ε CuO/PVDF gate dielectric appears to be highly promising candidates for organic non-volatile memory and sensor field-effect transistors (FETs).

Keywords: organic field effect transistors, thin film transistor, gate dielectric, organic semiconductor

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Louise Moles, Steve Riley, Sarah D. Lichenstein, Marzieh Babaeianjelodar, Robert Kohler, Annie Cheng, Corey Horien Abigail Greene, Wenjing Luo, Jonathan Ahern, Bohan Xu, Yize Zhao, Chun Chieh Fan, R. Todd Constable, Sarah W. Yip

Abstract:

Background: Risks for depression are myriad and include both genetic and brain-based factors. However, relationships between these systems are poorly understood, limiting understanding of disease etiology, particularly at the developmental level. Methods: We use a data-driven machine learning approach connectome-based predictive modeling (CPM) to identify functional connectivity signatures associated with polygenic risk scores for depression (DEP-PRS) among youth from the Adolescent Brain and Cognitive Development (ABCD) study across diverse brain states, i.e., during resting state, during affective working memory, during response inhibition, during reward processing. Results: Using 10-fold cross-validation with 100 iterations and permutation testing, CPM identified connectivity signatures of DEP-PRS across all examined brain states (rho’s=0.20-0.27, p’s<.001). Across brain states, DEP-PRS was positively predicted by increased connectivity between frontoparietal and salience networks, increased motor-sensory network connectivity, decreased salience to subcortical connectivity, and decreased subcortical to motor-sensory connectivity. Subsampling analyses demonstrated that model accuracies were robust across random subsamples of N’s=1,000, N’s=500, and N’s=250 but became unstable at N’s=100. Conclusions: These data, for the first time, identify neural networks of polygenic depression risk in a large sample of youth before the onset of significant clinical impairment. Identified networks may be considered potential treatment targets or vulnerability markers for depression risk.

Keywords: genetics, functional connectivity, pre-adolescents, depression

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Authors: Özge Selin Çevik, Leyla Şahin, Gülhan Örekeci Temel

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The early postnatal period is critical for the development of cognitive and emotional functions. Maternal separation is a detrimental postnatal influence, whereas environmental enrichment is a therapeutic and protective agent. It is unclear if long-term environmental enrichment can compensate for the effects of maternal separation stress on anxiety behavior. This study was designed to examine how environmental enrichment affects anxiety levels and corticosterone levels in maternally separated rats. There are six main groups in this study: control (C), maternal separation+standard cage (MS), maternal separation+enriched environment (MSE), enriched environment (E), the maternal separation that decapitated at postnatal (PN) 21 (MS21), and standard cage that decapitated at PN21 (STD21). The maternal separation procedure consisted of PN for 21 days (between 09:00 a.m and 12:00 a.m). Enriched (E, MSE) or standard cage environment rats (MS, C) spent PN (22-55) days in either enriched cages or standard cages. Anxiety and locomotor activity were examined with the open field and elevated plus-maze test. Blood corticosterone level was evaluated by the enzyme-linked immunosorbent assay (ELISA) method. Results showed that maternal separation (MS) increased locomotor activity and anxiety. An enriched environment (E) did not change the locomotor activity. MSE group’s anxiety and locomotor activity did not change. Corticosterone levels increased in the maternal separation group that decapitated at the PN 21 days. Maternal separation increases anxiety. Environmental enrichment alone was insufficient to cause alterations in the anxiety level. In addition, environmental enrichment did not ameliorate the anxiety level in maternally separated rats. However, environmental enrichment decreased the locomotor activity in the maternally separated rats.

Keywords: maternal separation, environment enrichment, stress, hippocampus, anxiety, memory, rat

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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