Search results for: neural perception.

Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Diego Caballero Vélez

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In 1997 the Schengen Convention was incorporated in the mainstream of EU law by the Amsterdam Treaty. It came into effect in 1999 with the abolition of internal border controls in the EU, a milestone in the European integration project. In the meantime, due to the Yugoslav wars, nearly 700,000 asylum applications were filed in the European countries provoking a major refugee crisis. During this period, the opening of Eastern Europe fostered more cooperation and policy-making at the EU level in migration issues. Currently, a similar migratory crisis is taking place in Europe. The Syrian war has caused the most massive influx of immigrants in Europe since World War II. Nevertheless, the EU is adopting different migration policies from those implemented during the Yugoslav migration crisis. The current crisis has not led to a common European position but national responses have been offered on migration policies and responsibility for border security and asylum-seekers. A lot of factors can explain this change from a cooperation scenario to a no cooperation one, such as the economic crisis, but this research is focused on the premise that 'threat perception' lies at the core of some states grand strategies towards migration and it also influences in multilateral or unilateral responses. Migration rests at the nexus of three dimensions of security, including geopolitical interests, material production, and internal security. According to some scholars, migration policy is an 'integral instrument' of state grand strategy in that context. Political integration at the EU might be altered with the emergence of existential threats. In other words, some areas of the European cooperation can be transformed when a 'critical juncture' occurs, for instance a migration crisis. In that instance, Member states could see migration as a matter of threat that modifies their national interests and willingness to embrace international cooperation. This research will focus on EU Member states´ perceptions of the 90´s migration crisis and the current one. The goal is to evaluate to what extent the perceptions of threat are one of the main factors for explaining the transition from a cooperation scenario to a no-cooperation one in European asylum and security policies. To analyze threat perception in both migration crisis, some relevant Member states are treated as cases of study and a comparative analysis is carried out based on public opinion polls, public and policy discourse in migration, voting practices and deconstruction of the migration policies themselves both at EU level and a national one.

Keywords: cooperation, migration crisis, national responses, threat perception

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Authors: Saima Yaqoob

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Due to advancement in technologies and cutting cost competition, especially in manufacturing business, organizations are now becoming more focused toward achieving exceptional quality standards with low manufacturing cost. In this concern, many process improvement strategies are becoming popular in the way of increasing productivity and output. Lean manufacturing principles are among one of them, increasingly used for improving productivity by reducing wastages. Many success factors are involved in lean implementation. But, the role of senior management is most important in developing the lean culture in an organization. Purpose of this study is to investigate the perception of executive level management related to the successful implementation of lean practices and its comparison with the existing practices in the organization. In order to collect data, survey questionnaire comprised of eight statements rendering the critical success factors were sent to the top management of fifty (50) automotive manufacturing companies of Karachi. After analyzing their feedbacks, the trend of lean manufacturing principles and the commitment of senior management toward its implementation was identified in the manufacturing industries of Karachi, Pakistan.

Keywords: lean manufacturing, process improvement, senior management, perception, involvement, waste reduction

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Authors: Bhavini Pandya

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This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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Authors: Shivam Kushwaha, Veena Bansal

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Timeshare refers to real estate that is owned simultaneously by many, for a specified time in a year, for a specified numbers of years and is maintained and managed by an agency. Timeshare falls under the umbrella of tourism and is often used for vacation. Timeshare industry has attracted significantly less number of customers in India as compared to the US and Europe. In more than 40 years of existence of timeshare industry, it has not been able to grow its roots among Indian customers. The purpose of the study: To explore perception of Indian customers towards the adoption of timeshare segment of the hospitality industry and identify the factors. Source of data: Survey has been done on existing owners of holidays memberships, resorts or those who at least tourism experience in their past purchases. Methodology: Logistic Regression is used to predict binary responses of the customers based on identified critical factors which might influence timeshare purchases. Result: The study identified four factors: discretionary income, exchange options, ownership pride, risk, and measured their influence on intention to purchases in India. It is recognized that is all four variables are statistically significant while explaining in purchase intentions of customers in India.

Keywords: timeshare, holiday, tourism, customer perception, intent to use, Indian tourism

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Valerii Dudin

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With the continuous increase in quantity and importance of the information surrounding our daily lives, it has become crucial to understand what makes information stand out and affect our point of view, regardless of the accuracy of the facts involved. Alaa Al-Aswany’s numerous works have been an inspiration for millions of his readers in Egypt and all across the Arab World. While highly factual, the author’s political essays are both lexically and stylistically rich; they also implement descriptive allusions and proverbs to support the presented opinions. We have undertaken an effort to explore the impact on the individual perception through these political works of the author. In this study, we have overviewed previously made research on similar subjects and through contextual, intertextual, linguistic and corpus analyses we have come to realize the presence of suggestive themes in these works, capable of shaping the reader’s perception regarding a certain topic, specifically targeting the reader’s emotional bias. The findings presented in the study will reveal an overview of such examples of suggestive elements used in the author’s works, as well as various new insights on what can be considered suggestive in the context of modern Arabic printed press.

Keywords: Alaa al-Aswany, cognitive linguistics, political essays, suggestion

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Authors: Shibo Wei, Ting Jiang

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Convolutional-recurrent neural networks (CRN) have achieved much success recently in the speech enhancement field. The common processing method is to use the convolution layer to compress the feature space by multiple upsampling and then model the compressed features with the LSTM layer. At last, the enhanced speech is obtained by deconvolution operation to integrate the global information of the speech sequence. However, the feature space compression process may cause the loss of information, so we propose to model the upsampling result of each step with the residual LSTM layer, then join it with the output of the deconvolution layer and input them to the next deconvolution layer, by this way, we want to integrate the global information of speech sequence better. The experimental results show the network model (RES-CRN) we introduce can achieve better performance than LSTM without residual and overlaying LSTM simply in the original CRN in terms of scale-invariant signal-to-distortion ratio (SI-SNR), speech quality (PESQ), and intelligibility (STOI).

Keywords: convolutional-recurrent neural networks, speech enhancement, residual LSTM, SI-SNR

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Authors: Cherouk Amr Yassin

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This study aims to provide insight into consumer decision-making, which has become very complicated to understand and predict in the existing world of sustainable development. The deficiency of a good understanding of the tourist's perception and attitude toward sustainable development in the tourism industry may impede the ability of organizations to build a sustainable marketing orientation and may negatively influence predicted consumer response. Therefore, this research paper adds further insights into the attitude toward recommended eco-friendly hotel advertisements and their effect on the purchase intention of eco-friendly services. Structural equational modeling was completed to realize the effects of the variables under investigation. The findings revealed that consumer decision-making in choosing eco-friendly hotels is affected by the positive attitude toward sustainable development ads, influenced by informativeness and credibility as values perceived by eco-friendly hotels. This study provides practical implications for tourism, marketers, hotel managers, promoters, and consumers.

Keywords: attitude, consumer behavior, consumer decision making, eco-friendly hotels, perception, the tourism industry

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Silpita Paul, Susanta Sadhukhan, Biswanath Maity, Madhusudan Das

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Neural tube defects (NTDs) are one of the most common forms of birth defect and affect ~300,000 new born worldwide each year. The prevalence is higher in Northern India (11 per 1000 birth) compare to southern India (5 per 1000 birth). NTDs are one of the common birth defects related with low blood folate and Hcy concentration. Though the mechanism is still unknown, but it is now established that, NTDs in human are polygenic in nature and follow the heterogeneous trait. In spite of its heterogeneity, polymorphism in few genes affects significantly the trait of NTDs. Polymorphisms in the genes FOLH1 and MTHFR plays important role in NTDs. In this study, the polymorphisms of these genes were screened by bi-directional sequencing from 30 mothers with NTD babies as case. The result revealed that 26.67% patients had bi-allelic FOLH1 polymorphism. The polymorphism has been identified as p.Y60H and frequent to cause NTDs. The study of MTHFR gene showed 2 different SNPs rs1801131 (at exon 4) and rs1801131 (at exon 7). The study showed 6.67% patients of both mono- and bi-allelic MTHFR-rs1801131 polymorphism and 6.67% patients of bi-allelic MTHFR-rs1801131 polymorphism. These polymorphisms has been responsible for p.A222V and p.E429A change respectively and frequently involved in NTD formation. Those polymorphisms affect mainly the absorption of dietary folate from intestine and the formation of 5-methylenetetrahydrofolate (5 MTHF) from 5,10-methylenetetrahydrofolate (5,10- MTHF), which is the functional folate form in our system. Though the study is not complete yet, but these polymorphisms play crucial roles in the formation of NTDs in other world population. Based on the result till date, it can be concluded that they also play significant role in our population too as in control samples we have not found any changes.

Keywords: neural tube defects, polymorphism, FOLH1, MTHFR

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

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This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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Authors: Umair Rashid

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Normally softmax loss is used as the supervision signal in face recognition (FR) system, and it boosts the separability of features. In the last two years, a number of techniques have been proposed by reformulating the original softmax loss to enhance the discriminating power of Deep Convolutional Neural Networks (DCNNs) for FR system. To learn angularly discriminative features Cosine-Margin based softmax has been adjusted as monotonically decreasing angular function, that is the main challenge for angular based softmax. On that issue, we propose monotonically decreasing element for Cosine-Margin based softmax and also, we discussed the effect of different monotonically decreasing parameters on angular Margin softmax for FR system. We train the model on publicly available dataset CASIA- WebFace via our proposed monotonically decreasing parameters for cosine function and the tests on YouTube Faces (YTF, Labeled Face in the Wild (LFW), VGGFace1 and VGGFace2 attain the state-of-the-art performance.

Keywords: deep convolutional neural networks, cosine margin face recognition, softmax loss, monotonically decreasing parameter

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

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

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

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Authors: Zhifeng Kong

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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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Authors: Clarisse Lawson-Guidigbe, Nicolas Louveton, Kahina Amokrane-Ferka, Jean-Marc Andre

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Trust in automated driving systems is receiving growing attention in the research community. Anthropomorphism has been identified by past research as a trust-building factor. In this paper, we consider three anthropomorphic interfaces integrating three versions of a virtual assistant. We attempt to measure the impact of each of these interfaces on trust in the automated driving system. An experiment following a between-subject design was conducted in a driving simulator (N = 36) to evaluate participants’ performance and experience in two handover situations (a simple one and a critical one). Perception of anthropomorphism and trust was measured using scales, while participants’ experience was measured during elicitation interviews. We found no significant difference between the three interfaces regarding the perception of anthropomorphism, trust levels, or experience. However, regarding participants’ performance, we found a significant difference between the three interfaces in the simple handover situations but not the critical one. Learnings from anthropomorphism and trust measurement scales are discussed and suggestions for further research are proposed.

Keywords: highly automated driving, trust, anthropomorphic design, mindful anthropomorphism, mindless anthropomorphism

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Authors: Funmilayo Juliana Afolabi, Joke Haafkens, Paul De Beer

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Globally, there is a growing concern on reducing workplace accidents in the informal sector. However, there is a dearth of study on the perception of the informal workers on occupational risks they are exposed to. The way a worker perceives the workplace risk will influence his/her risk tolerance and risk behavior. The aim of this paper, therefore, is to have an in-depth understanding of the way the artisans perceive the risks at their workplace and how it influences their risk tolerance and risk behavior. This will help in designing meaningful intervention for the artisans and it will assist the policy makers in formulating a policy that will help them. Methods: Forty-three artisans were purposely selected for the study; data were generated through observation of the workplace and work practices of the artisans and in-depth interview from automobile artisans (Panel beater, Mechanic, Vulcanizer, and Painters) in Osun State, Nigeria. The transcriptions were coded and analyzed using MAXQDA software. Results: The perceived occupational risks among the study groups are a danger of being run over by oncoming vehicles while working by the roadside, a risk of vehicle falling on workers while working under the vehicle, cuts, and burns, fire explosion, falls from height and injuries from bursting of tires. The identified risk factors are carelessness of the workers, pressure from customers, inadequate tools, preternatural forces, God’s will and lack of apprentices that will assist them in the workplace. Furthermore, the study revealed that artisans engage in risky behavior like siphoning fuel with mouth because of perception that fuel is good for expelling worms and will make them free from any stomach upset. Conclusions: The study concluded that risky behaviors are influenced by culture, beliefs, and perception of the artisans. The study, therefore, suggested proper health and safety education for the artisans.

Keywords: automobile artisans, informal, occupational risks, Nigeria, sociological enquiry

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Hanbey Hazar, Hakan Gul

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In this study, Thermal Barrier Coating (TBC) application is performed in order to reduce the engine emissions. Piston, exhaust, and intake valves of a single-cylinder four-cycle gasoline engine were coated with chromium carbide (Cr3C2) at a thickness of 300 µm by using the Plasma Spray coating method which is a TBC method. Gasoline engine was converted into an LPG system. The study was conducted in 4 stages. In the first stage, the piston, exhaust, and intake valves of the gasoline engine were coated with Cr3C2. In the second stage, gasoline engine was converted into the LPG system and the emission values in this engine were recorded. In the third stage, the experiments were repeated under the same conditions with a standard (uncoated) engine and the results were recorded. In the fourth stage, data obtained from both engines were loaded on Artificial Neural Networks (ANN) and estimated values were produced for every revolution. Thus, mathematical modeling of coated and uncoated engines was performed by using ANN. While there was a slight increase in exhaust gas temperature (EGT) of LPG engine due to TBC, carbon monoxide (CO) values decreased.

Keywords: LPG fuel, thermal barrier coating, artificial neural network, mathematical modelling

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: Elizabeth Godwin Mahenge

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This study seeks to investigate the portrayal of albinism in Swahili literature. People with albinism have faced many life-threatening challenges, from being hunted for their body parts of being assigned derogatory names that depict them as ghosts as or less than humans. Many studies have been conducted on the perception of people towards Persons with Albinism [PWA] worldwide. Findings showed there is negative perception or negative portrayal of PWA in different societies worldwide. These negative connotations raised hot debates around the world among different societies and associations of/for PWA. People with disability in different parts of the world started arguing the labeling and name calling same applied to persons with disability in Tanzania (albinism included). They went the same debate about name calling hence in 2010, the Tanzanian Parliament passed the bill on Persons with Disability Act which banned derogative names attached to disability in general and to albinism in particular. In Tanzanian societies, there have been a mixed feelings with regards to albinism. Some do have negative perceptions because of the killings with connection to superstitious believes, while in other societies are perceived positively as blessed children a family. From these two contradictory perceptions that exist in this society, the study seeks to find out how Swahili literature portrays albinism.

Keywords: albinism, portrayal, disability, Kiswahili literature

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Authors: L. Bruckert, A. Mansourzadeh

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The voice has a major influence on interpersonal communication in everyday life via the perception of pleasantness. The evolutionary perspective postulates that the mechanisms underlying the pleasantness judgments are universal adaptations that have evolved in the service of choosing a mate (through the process of sexual selection). From this point of view, the favorite voices would be those with more marked sexually dimorphic characteristics; for example, in men with lower voice pitch, pitch is the main criterion. On the other hand, one can postulate that the mechanisms involved are gradually established since childhood through exposure to the environment, and thus the prosodic elements could take precedence in everyday life communication as it conveys information about the speaker's attitude (willingness to communicate, interest toward the interlocutors). Our study focuses on voice pleasantness and its relationship with social impression formation, exploring both the spectral aspects (pitch, timbre) and the prosodic ones. In our study, we recorded the voices through two vocal corpus (five vowels and a reading text) of 25 French males speaking French and 25 Iranian males speaking Farsi. French listeners (40 male/40 female) listened to the French voices and made a judgment either on the voice's pleasantness or on the speaker (judgment about his intelligence, honesty, sociability). The regression analyses from our acoustic measures showed that the prosodic elements (for example, the intonation and the speech rate) are the most important criteria concerning pleasantness, whatever the corpus or the listener's gender. Moreover, the correlation analyses showed that the speakers with the voices judged as the most pleasant are considered the most intelligent, sociable, and honest. The voices in Farsi have been judged by 80 other French listeners (40 male/40 female), and we found the same effect of intonation concerning the judgment of pleasantness with the corpus «vowel» whereas with the corpus «text» the pitch is more important than the prosody. It may suggest that voice perception contains some elements invariant across culture/language, whereas others are influenced by the cultural/linguistic background of the listener. Shortly in the future, Iranian people will be asked to listen either to the French voices for half of them or to the Farsi voices for the other half and produce the same judgments as the French listeners. This experimental design could potentially make it possible to distinguish what is linked to culture and what is linked to language in the case of differences in voice perception.

Keywords: cross-cultural psychology, impression formation, pleasantness, voice perception

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Authors: Farshad Negintaji, Hamid Reza Zeraat Pisheh, Mahshid Ghanea, Zahra Khalifeh, Mohammad Bagher Rahami

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This study has been examined the anatomical components of mosques with the approach of attending to Islamic wisdom and investigated the distinction between the anatomical design of mosques (traditional and modern) by considering the category of perception in Islamic architecture. To this end, this article by reviewing the theoretical and empirical literature of mosques' anatomy and the role of anatomy on the architectural design of Iranian mosques by examining the quantitative and qualitative indicators and in order to understand and identify the anatomy of mosques, indicators such as: entrance, portico, minarets, domes, bedchamber and pool have been used. The aim of this study has been to investigate materials, the functional properties, technology, sizes and fitness of (traditional and modern) mosques. For this purpose, a questionnaire was prepared in which the anatomical and spiritual elements of the mosque shape have been questioned. Research is related to field and is of descriptive, analytical and inferential type and quantitative and qualitative indicators have been examined.

Keywords: Islamic wisdom, Islamic architecture, mosque anatomy, the minaret, dome, bedchamber, entrance, pool, perception

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Authors: Maria I. Nuñez-Hernández, Maria L. Riesco

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Background: Exclusive breastfeeding (EBF) up to 6 months old infant have been considered one of the most important factors in the overall development of children. Nevertheless, as resources are scarce, it is essential to identify the most vulnerable groups that have major risk of EBF abandonment, in order to deliver the best strategies. Children of adolescent mothers are within these groups. Aims: To determine the EBF abandonment rate among adolescent mothers and to analyze the associated factors. Methods: Prospective cohort study of adolescent mothers in the southern area of Santiago, Chile, conducted in primary care services of public health system. The cohort was established from 2014 to 2015, with a sample of 105 adolescent mothers and their children at 2 months of life. The inclusion criteria were: adolescent mother from 14 to 19 years old; not twin babies; mother and baby leaving the hospital together after birthchild; correct attachment of the baby to the breast; no difficulty understanding the Spanish language or communicating. Follow-up was performed at 4 and 6 months old infant. Data were collected by interviews, considering EBF as breastfeeding only, without adding other milk, tea, juice, water or other product that not breast milk, except drugs. Data were analyzed by descriptive and inferential statistics, by Kaplan-Meier estimator and Log-Rank test, admitting the probability of occurrence of type I error of 5% (p-value = 0.05). Results: The cumulative EBF abandonment rate at 2, 4 and 6 months was 33.3%, 52.2% and 63.8%, respectively. Factors associated with EBF abandonment were maternal perception of the quality of milk as poor (p < 0.001), maternal perception that the child was not satisfied after breastfeeding (p < 0.001), use of pacifier (p < 0.001), maternal consumption of illicit drugs after delivery (p < 0.001), mother return to school (p = 0.040) and presence of nipple trauma (p = 0.045). Conclusion: EBF abandonment rate was higher in the first 4 months of life and is superior to the population of women who breastfeed. Among the EBF abandonment factors, one of them is related to the adolescent condition, and two are related to the maternal subjective perception.

Keywords: adolescent, breastfeeding, midwifery, nursing

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Authors: David Yartel, Johnmark Ampomah Mensah Fobi, Ernest Yeboah Acheampong, Sintim Musah

Abstract:

Football has gone through a series of technological reforms targeted at improving the game for its audience. Yet, promote sanity of the game led to the introduction of the video assistant referee (VAR) to ‘check’ or ‘review’ an incident to clarify incidents and communicate the outcome to the referee and the fans. This is to reduce controversies regarding incidents on the pitch of play. In this study, we seek to survey the views of football fans to understand their perception of the video assistant referee, whether it has brought sanity or reduce the uncertainty regarding the decisions after reviews. The exploratory study focuses on 420 fans arbitrarily sampled on the university campuses to answer questionnaires based on the introduction of the video assistant referee. Results show that the VAR has interrupted the flow of the game, dropping passion, increased controversies including decisions from the referees’ call room leading to ensuing fans conflict, especially when it is against their team and vice versa. The study concludes by addressing some of their concerns as the VAR has come to minimise perceptions of incidents and engender fairness for teams.

Keywords: football fans, football incidents, football match, video assistant referee, technology

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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