Search results for: parallel algorithms

Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Francesco Lubrano, Fabrizio Bertone, Federico Stirano

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Modern societies strongly depend on Critical Infrastructures (CI). Hospitals, power supplies, water supplies, telecommunications are just few examples of CIs that provide vital functions to societies. CIs like hospitals are very complex environments, characterized by a huge number of cyber and physical systems that are becoming increasingly integrated. Ensuring a high level of security within such critical infrastructure requires a deep knowledge of vulnerabilities, threats, and potential attacks that may occur, as well as defence and prevention or mitigation strategies. The possibility to remotely monitor and control almost everything is pushing the adoption of network-connected devices. This implicitly introduces new threats and potential vulnerabilities, posing a risk, especially to those devices connected to the Internet. Modern medical devices used in hospitals are not an exception and are more and more being connected to enhance their functionalities and easing the management. Moreover, hospitals are environments with high flows of people, that are difficult to monitor and can somehow easily have access to the same places used by the staff, potentially creating damages. It is therefore clear that physical and cyber threats should be considered, analysed, and treated together as cyber-physical threats. This means that an integrated approach is required. SAFECARE, an integrated cyber-physical security solution, tries to respond to the presented issues within healthcare infrastructures. The challenge is to bring together the most advanced technologies from the physical and cyber security spheres, to achieve a global optimum for systemic security and for the management of combined cyber and physical threats and incidents and their interconnections. Moreover, potential impacts and cascading effects are evaluated through impact propagation models that rely on modular ontologies and a rule-based engine. Indeed, SAFECARE architecture foresees i) a macroblock related to cyber security field, where innovative tools are deployed to monitor network traffic, systems and medical devices; ii) a physical security macroblock, where video management systems are coupled with access control management, building management systems and innovative AI algorithms to detect behavior anomalies; iii) an integration system that collects all the incoming incidents, simulating their potential cascading effects, providing alerts and updated information regarding assets availability.

Keywords: cyber security, defence strategies, impact propagation, integrated security, physical security

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Esra Sengul, R. G. Aktas, M. E. Sitar, H. Isan

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Hepatocellular carcinoma (HCC) is a hepatocellular tumor commonly found on the surface of the chronic liver. HepG2 is the most commonly used cell type in HCC studies. The main proteins remaining in the blood serum after separation of plasma fibrinogen are albumin and globulin. The fact that the albumin showed hepatocellular damage and reflect the synthesis capacity of the liver was the main reason for our use. Alpha-Fetoprotein (AFP) is an albumin-like structural embryonic globulin found in the embryonic cortex, cord blood, and fetal liver. It has been used as a marker in the follow-up of tumor growth in various malign tumors and in the efficacy of surgical-medical treatments, so it is a good protein to look at with albumins. We have seen the morphological changes of dimethyl sulfoxide (DMSO) on HepG2 and decided to investigate its biochemical effects. We examined the effects of DMSO, which is used in cell cultures, on albumin, AFP and total protein at low doses. Material Method: Cell Culture: Medium was prepared in cell culture using Dulbecco's Modified Eagle Media (DMEM), Fetal Bovine Serum Dulbecco's (FBS), Phosphate Buffered Saline and trypsin maintained at -20 ° C. Fixation of Cells: HepG2 cells, which have been appropriately developed at the end of the first week, were fixed with acetone. We stored our cells in PBS at + 4 ° C until the fixation was completed. Area Calculation: The areas of the cells are calculated in the ImageJ (IJ). Microscope examination: The examination was performed with a Zeiss Inverted Microscope. Daytime photographs were taken at 40x, 100x 200x and 400x. Biochemical Tests: Protein (Total): Serum sample was analyzed by a spectrophotometric method in autoanalyzer. Albumin: Serum sample was analyzed by a spectrophotometric method in autoanalyzer. Alpha-fetoprotein: Serum sample was analyzed by ECLIA method. Results: When liver cancer cells were cultured in medium with 1% DMSO for 4 weeks, a significant difference was observed when compared with the control group. As a result, we have seen that DMSO can be used as an important agent in the treatment of liver cancer. Cell areas were reduced in the DMSO group compared to the control group and the confluency ratio increased. The ability to form spheroids was also significantly higher in the DMSO group. Alpha-fetoprotein was lower than the values of an ordinary liver cancer patient and the total protein amount increased to the reference range of the normal individual. Because the albumin sample was below the specimen value, the numerical results could not be obtained on biochemical examinations. We interpret all these results as making DMSO a caretaking aid. Since each one was not enough alone we used 3 parameters and the results were positive when we refer to the values of a normal healthy individual in parallel. We hope to extend the study further by adding new parameters and genetic analyzes, by increasing the number of samples, and by using DMSO as an adjunct agent in the treatment of liver cancer.

Keywords: hepatocellular carcinoma, HepG2, dimethyl sulfoxide, cell culture, ELISA

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Authors: Seyed Hossein Hosseini, Seyed Majid Hashemzadeh

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Solar energy is one of the remarkable renewable energy sources that have particular characteristics such as unlimited, no environmental pollution, and free access. Generally, solar energy can be used in thermal and photovoltaic (PV) types. The cost of installation of the PV system is very high. Additionally, due to dependence on environmental situations such as solar radiation and ambient temperature, electrical power generation of this system is unpredictable and without power electronics devices, there is no guarantee to maximum power delivery at the output of this system. Maximum power point tracking (MPPT) should be used to achieve the maximum power of a PV string. MPPT is one of the essential parts of the PV system which without this section, it would be impossible to reach the maximum amount of the PV string power and high losses are caused in the PV system. One of the noticeable challenges in the problem of MPPT is the partial shading conditions (PSC). In PSC, the output photocurrent of the PV module under the shadow is less than the PV string current. The difference between the mentioned currents passes from the module's internal parallel resistance and creates a large negative voltage across shaded modules. This significant negative voltage damages the PV module under the shadow. This condition is called hot-spot phenomenon. An anti-paralleled diode is inserted across the PV module to prevent the happening of this phenomenon. This diode is known as the bypass diode. Due to the performance of the bypass diode under PSC, the P-V curve of the PV string has several peaks. One of the P-V curve peaks that makes the maximum available power is the global peak. Model-based Global MPPT (GMPPT) methods can estimate the optimal point with higher speed than other GMPPT approaches. Centralized, modular, and interleaved DC-DC converter topologies are the significant structures that can be used for GMPPT at a PV string. there are some problems in the centralized structure such as current mismatch losses at PV sting, loss of power of the shaded modules because of bypassing by bypass diodes under PSC, needing to series connection of many PV modules to reach the desired voltage level. In the modular structure, each PV module is connected to a DC-DC converter. In this structure, by increasing the amount of demanded power from the PV string, the number of DC-DC converters that are used at the PV system will increase. As a result, the cost of the modular structure is very high. We can implement the model-based GMPPT through the multi-input interleaved boost DC-DC converter to increase the power extraction from the PV string and reduce hot-spot and current mismatch error in a PV string under different environmental condition and variable load circumstances. The interleaved boost DC-DC converter has many privileges than other mentioned structures, such as high reliability and efficiency, better regulation of DC voltage at DC link, overcome the notable errors such as module's current mismatch and hot spot phenomenon, and power switches voltage stress reduction.

Keywords: solar energy, photovoltaic systems, interleaved boost converter, maximum power point tracking, model-based method, partial shading conditions

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Authors: N. C. van der Merwe, J. Oosthuizen, M. F. Makhetha, J. Adams, B. K. Dajee, S-R. Schneider

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Women representing high-risk breast cancer families, who tested negative for pathogenic mutations in BRCA1 and BRCA2, are four times more likely to develop breast cancer compared to women in the general population. Sequencing of genes involved in genomic stability and DNA repair led to the identification of novel contributors to familial breast cancer risk. These include BLM and PALB2. Bloom's syndrome is a rare homozygous autosomal recessive chromosomal instability disorder with a high incidence of various types of neoplasia and is associated with breast cancer when in a heterozygous state. PALB2, on the other hand, binds to BRCA2 and together, they partake actively in DNA damage repair. Archived DNA samples of 66 BRCA1/2 negative high-risk breast cancer patients were retrospectively selected based on the presence of an extensive family history of the disease ( > 3 affecteds per family). All coding regions and splice-site boundaries of both genes were screened using High-Resolution Melting Analysis. Samples exhibiting variation were bi-directionally automated Sanger sequenced. The clinical significance of each variant was assessed using various in silico and splice site prediction algorithms. Comprehensive screening identified a total of 11 BLM and 26 PALB2 variants. The variants detected ranged from global to rare and included three novel mutations. Three BLM and two PALB2 likely pathogenic mutations were identified that could account for the disease in these extensive breast cancer families in the absence of BRCA mutations (BLM c.11T > A, p.V4D; BLM c.2603C > T, p.P868L; BLM c.3961G > A, p.V1321I; PALB2 c.421C > T, p.Gln141Ter; PALB2 c.508A > T, p.Arg170Ter). Conclusion: The study confirmed the contribution of pathogenic mutations in BLM and PALB2 to the familial breast cancer burden in South Africa. It explained the presence of the disease in 7.5% of the BRCA1/2 negative families with an extensive family history of breast cancer. Segregation analysis will be performed to confirm the clinical impact of these mutations for each of these families. These results justify the inclusion of both these genes in a comprehensive breast and ovarian next generation sequencing cancer panel and should be screened simultaneously with BRCA1 and BRCA2 as it might explain a significant percentage of familial breast and ovarian cancer in South Africa.

Keywords: Bloom Syndrome, familial breast cancer, PALB2, South Africa

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Zeytu G. Asfaw, Serkalem K. Abrha, Demisew G. Degefu

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Background: HIV/AIDS remains a major public health problem in Ethiopia and heavily affecting people of productive and reproductive age. We aimed to compare the performance of Parametric Survival Analysis and Bayesian Survival Analysis using simulations and in a real dataset application focused on determining predictors of HIV patient survival. Methods: A Parametric Survival Models - Exponential, Weibull, Log-normal, Log-logistic, Gompertz and Generalized gamma distributions were considered. Simulation study was carried out with two different algorithms that were informative and noninformative priors. A retrospective cohort study was implemented for HIV infected patients under Highly Active Antiretroviral Therapy in Alamata General Hospital, North Ethiopia. Results: A total of 320 HIV patients were included in the study where 52.19% females and 47.81% males. According to Kaplan-Meier survival estimates for the two sex groups, females has shown better survival time in comparison with their male counterparts. The median survival time of HIV patients was 79 months. During the follow-up period 89 (27.81%) deaths and 231 (72.19%) censored individuals registered. The average baseline cluster of differentiation 4 (CD4) cells count for HIV/AIDS patients were 126.01 but after a three-year antiretroviral therapy follow-up the average cluster of differentiation 4 (CD4) cells counts were 305.74, which was quite encouraging. Age, functional status, tuberculosis screen, past opportunistic infection, baseline cluster of differentiation 4 (CD4) cells, World Health Organization clinical stage, sex, marital status, employment status, occupation type, baseline weight were found statistically significant factors for longer survival of HIV patients. The standard error of all covariate in Bayesian log-normal survival model is less than the classical one. Hence, Bayesian survival analysis showed better performance than classical parametric survival analysis, when subjective data analysis was performed by considering expert opinions and historical knowledge about the parameters. Conclusions: Thus, HIV/AIDS patient mortality rate could be reduced through timely antiretroviral therapy with special care on the potential factors. Moreover, Bayesian log-normal survival model was preferable than the classical log-normal survival model for determining predictors of HIV patients survival.

Keywords: antiretroviral therapy (ART), Bayesian analysis, HIV, log-normal, parametric survival models

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

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The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

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Authors: Gulden Aykinbaeva, Assem Umirzakova, Assel Makhadiyeva

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The problem of studying of young married couples in connection with a change of social institute of a family and marriage is represented very actual for family consultation, considering a family role in the development of modern society. Results of numerous researchs say that one of difficult in formation and stabilization of a matrimony is the period of a young family. This period is characterized by various processes of integration, adaptation and emotional compatibility of spouses. The young family in it the period endures the first standard crisis which postpones a print for the further development of the family scenario. Emergence new, earlier not existing, systems of values render a huge value on the process of formation of a young family and each of spouses separately. Possibly to solve the set family tasks at the development of the uniform system of the family relations in which socially mature persons capable to consider a family as the creativity of each other act as subjects. Due to the research objective in work the following techniques were used: a questionnaire of satisfaction with V. V. Stolin's marriage and A. N. Volkova's technique directed on detection of coherence of family values and role installations in a married couple, and also content – the analysis. Development of an internal basis of a family on mutual clearing of values is important during the work with married couples. 'The mature view' of the partner in the marriage union provides coherence between the expected and real behavior of the partner that is important for the realization of the purposes of adaptation in a family. For research of communication of the data obtained by means of A. N. Volkova's techniques, V. V. Stolina and content – the analysis, the correlation analysis, with the application of the criterion of Spirmen was used. The analysis of results of the conducted research allowed us to determine the number of consistent patterns: 1. Nature of change of satisfaction with marriage at spouses testifies that the matrimonial relations undergo high-quality changes at different stages of formation of a young family. 2. The matrimonial relations in the course of their development, formation and functioning in young marriage undergo considerable changes on psychological, social and psychological and insignificant — at the psychophysiological and sociocultural levels. The material received by us allows to plan ways of further detailed researches of the development of the matrimonial relations not only in the young marriage but also at further stages of development of a matrimony. We believe that the results received in this research can be almost applied at creation of algorithms of selection of marriage partners, at diagnostics of character and the maintenance of matrimonial disharmonies, at the forecast of stability of marriage and a family.

Keywords: married couples, formation of the young family, psychological consultation, matrimony

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Model assessment, in the Bayesian context, involves evaluation of the goodness-of-fit and the comparison of several alternative candidate models for predictive accuracy and improvements. In posterior predictive checks, the data simulated under the fitted model is compared with the actual data. Predictive model accuracy is estimated using information criteria such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the Deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). The goal of an information criterion is to obtain an unbiased measure of out-of-sample prediction error. Since posterior checks use the data twice; once for model estimation and once for testing, a bias correction which penalises the model complexity is incorporated in these criteria. Cross-validation (CV) is another method used for examining out-of-sample prediction accuracy. Leave-one-out cross-validation (LOO-CV) is the most computationally expensive variant among the other CV methods, as it fits as many models as the number of observations. Importance sampling (IS), truncated importance sampling (TIS) and Pareto-smoothed importance sampling (PSIS) are generally used as approximations to the exact LOO-CV and utilise the existing MCMC results avoiding expensive computational issues. The reciprocals of the predictive densities calculated over posterior draws for each observation are treated as the raw importance weights. These are in turn used to calculate the approximate LOO-CV of the observation as a weighted average of posterior densities. In IS-LOO, the raw weights are directly used. In contrast, the larger weights are replaced by their modified truncated weights in calculating TIS-LOO and PSIS-LOO. Although, information criteria and LOO-CV are unable to reflect the goodness-of-fit in absolute sense, the differences can be used to measure the relative performance of the models of interest. However, the use of these measures is only valid under specific circumstances. This study has developed 11 models using normal, log-normal, gamma, and student’s t distributions to improve the PCR stutter prediction with forensic data. These models are comprised of four with profile-wide variances, four with locus specific variances, and three which are two-component mixture models. The mean stutter ratio in each model is modeled as a locus specific simple linear regression against a feature of the alleles under study known as the longest uninterrupted sequence (LUS). The use of AIC, BIC, DIC, and WAIC in model comparison has some practical limitations. Even though, IS-LOO, TIS-LOO, and PSIS-LOO are considered to be approximations of the exact LOO-CV, the study observed some drastic deviations in the results. However, there are some interesting relationships among the logarithms of pointwise predictive densities (lppd) calculated under WAIC and the LOO approximation methods. The estimated overall lppd is a relative measure that reflects the overall goodness-of-fit of the model. Parallel log-likelihood profiles for the models conditional on equal posterior variances in lppds were observed. This study illustrates the limitations of the information criteria in practical model comparison problems. In addition, the relationships among LOO-CV approximation methods and WAIC with their limitations are discussed. Finally, useful recommendations that may help in practical model comparisons with these methods are provided.

Keywords: cross-validation, importance sampling, information criteria, predictive accuracy

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Authors: Eleni Karasavvidou

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Social and anthropological research, parallel to Gender Studies, highlighted the relationship between social structures and symbolic forms as an important field of interaction and recording of 'social trends.' Since the study of representations can contribute to the understanding of the social functions and power relations, they encompass. This ‘mirage,’ however, has not only to do with the representations themselves but also with the ways they are received and the film or critical narratives that are established as dominant or alternative. Cinema and the criticism of its cultural products are no exception. Even in the rapidly changing media landscape of the 21st century, movies remain an integral and widespread part of popular culture, making films an extremely powerful means of 'legitimizing' or 'delegitimizing' visions of domination and commonsensical gender stereotypes throughout society. And yet it is film criticism, the 'language per se,' that legitimizes, reinforces, rewards and reproduces (or at least ignores) the stereotypical depictions of female roles that remain common in the realm of film images. This creates the need for this issue to have emerged (also) in academic research questioning gender criteria in film reviews as part of the effort for an inclusive art and society. Qualitative content analysis is used to examine female roles in selected Oscar-nominated films against their reviews from leading websites and newspapers. This method was chosen because of the complex nature of the depictions in the films and the narratives they evoke. The films were divided into basic scenes depicting social functions, such as love and work relationships, positions of power and their function, which were analyzed by content analysis, with borrowings from structuralism (Gennette) and the local/universal images of intercultural philology (Wierlacher). In addition to the measurement of the general ‘representation-time’ by gender, other qualitative characteristics were also analyzed, such as: speaking time, sayings or key actions, overall quality of the character's action in relation to the development of the scenario and social representations in general, as well as quantitatively (insufficient number of female lead roles, fewer key supporting roles, relatively few female directors and people in the production chain and how they might affect screen representations. The quantitative analysis in this study was used to complement the qualitative content analysis. Then the focus shifted to the criteria of film criticism and to the rhetorical narratives that exclude or highlight in relation to gender identities and functions. In the criteria and language of film criticism, stereotypes are often reproduced or allegedly overturned within the framework of apolitical "identity politics," which mainly addresses the surface of a self-referential cultural-consumer product without connecting it more deeply with the material and cultural life. One of the prime examples of this failure is the Bechtel Test, which tracks whether female characters speak in a film regardless of whether women's stories are represented or not in the films analyzed. If perceived unbiased male filmmakers still fail to tell truly feminist stories, the same is the case with the criteria of criticism and the related interventions.

Keywords: representations, context analysis, reviews, sexist stereotypes

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Authors: Sandhya Rijal, Saroj Adhikari, Ramesh R. Pant

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Non-Timber Forest Products (NTFPs) have attracted substantial interest in the recent years with the increasing recognition that these can provide essential community needs for improved and diversified rural livelihood and support the objectives of biodiversity conservation. Nevertheless, various challenges are witnessed in their sustainable harvest and management. Assuming that sustainable management with community stewardship can offer one of the solutions to existing challenges, the study assesses the linkages between NTFPs and rural livelihood in Lamabagar village of Dolakha, Nepal. The major objective was to document the status of NTFPs and their contributions in households of Lamabagar. For status documentation, vegetation sampling was done using systematic random sampling technique. 30 plots of 10 m × 10 m were laid down in six parallel transect lines at horizontal distance of 160 m in two different community forests. A structured questionnaire survey was conducted in 76 households (excluding non-response rate) using stratified random sampling technique for contribution analysis. Likewise, key informant interview and focus group discussions were also conducted for data triangulations. 36 different NTFPs were recorded from the vegetation sample in two community forests of which 50% were used for medicinal purposes. The other uses include fodder, religious value, and edible fruits and vegetables. Species like Juniperus indica, Daphne bholua Aconitum spicatum, and Lyonia ovalifolia were frequently used for trade as a source of income, which was sold in local market. The protected species like Taxus wallichiana and Neopicrorhiza scrophulariiflora were also recorded in the area for which the trade is prohibited. The protection of these species urgently needs community stewardship. More than half of the surveyed households (55%) were depending on NTFPs for their daily uses, other than economic purpose whereas 45% of them sold those products in the market directly or in the form of local handmade products as a source of livelihood. NTFPs were the major source of primary health curing agents especially for the poor and unemployed people in the study area. Hence, the NTFPs contributed to livelihood under three different categories: subsistence, supplement income and emergency support, depending upon the economic status of the households. Although the status of forest improved after handover to the user group, the availability of valuable medicinal herbs like Rhododendron anthopogon, Swertia nervosa, Neopicrorhiza scrophulariiflora, and Aconitum spicatum were declining. Inadequacy of technology, lack of easy transport access, and absence of good market facility were the major limitations for external trade of NTFPs in the study site. It was observed that people were interested towards conservation only if they could get some returns: economic in terms of rural settlements. Thus, the study concludes that NTFPs could contribute rural livelihood and support conservation objectives only if local communities are provided with the easy access of technology, market and capital.

Keywords: contribution, medicinal, subsistence, sustainable harvest

Procedia PDF Downloads 105

Authors: Muccifora S., Rinallo C., Bellani L.

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Calcium (Ca²+) is an element essential to the plant being involved in plant growth and development. At high concentrations, it is toxic and can influence every stage, process and cellular activity of plant life. Given its toxicity, cells implement mechanisms to compartmentalize calcium in a vacuole, endoplasmic reticulum, mitochondria, plastids and cell wall. One of the most effective mechanisms to reduce the excess of calcium, thus avoiding cellular damage, is its complexation with oxalic acid to form calcium oxalate crystals that are no longer osmotically or physiologically active. However, the sequestered calcium can be mobilized when the plant needs it. Calcium crystals can be accumulated in the vacuole of specialized sink-cells called idioblasts, with different crystalline forms (druse, raphyde and styloid) of diverse physiological meanings. Actinidia deliciosa cv. Hayward presents raphydes and styloid localized in idioblasts in cells of photosynthetic and non-photosynthetic tissues. The purpose of this work was to understand if there is a relationship between the age of Actinidia leaves and the presence, distribution, dimension and shape of oxalate crystals by means of light, fluorescent, polarized and transmission electron microscopy. Three vines from female plants were chosen at the beginning of the season and used throughout the study. The leaves with petioles were collected at various stages of development from the bottom to the shoot of the plants monthly from April to July. The samples were taken in corresponding areas of the central and lateral parts of the leaves and of the basal portion of the petiole. The results showed that in the leaves, the number of raphyde idioblasts decreased with the progress of the growing season, while the styloid idioblasts increased progressively, becoming very numerous in the upper nodes of July. In June and in July samples, in the vacuoles of the highest nodes, a portion regular in shape strongly stained with rubeanic acid was present. Moreover, the chlortetracycline (CTC) staining for localization of free calcium marked the wall of the idioblasts and the wall of the cells near vascular bundles. In April petiole samples, moving towards the youngest nodes, the raphydes idioblast decreased in number and in the length of the single raphydes. Besides, crystals stained with rubeanic acid appeared in the vacuoles of some cells. In June samples, numerous raphyde idioblasts oriented parallel to vascular bundles were evident. Under the electron microscope, numerous idioblasts presented not homogeneous electrondense aggregates of material, in which a few crystals (styloids) in the form of regular holes were scattered. In July samples, an increase in the number of styloid idioblasts in the youngest nodes and little masses stained with CTC near styloids were observed. Peculiar cells stained with rubeanic acid were detected and hypothesized to be involved in the formation of the idioblasts. In conclusion, in Actinidia leaves and petioles, it seems to confirm the hypothesis that the formation of styloid idioblasts can be correlated to increasing calcium levels in growing tissues.

Keywords: calcium oxalate crystals, actinidia deliciosa, light and electron microscopy, idioblasts

Procedia PDF Downloads 59

Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Hatem Abou-Senna

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Interstate 4 (I-4) is a primary east-west transportation corridor between Tampa and Daytona cities, serving commuters, commercial and recreational traffic. I-4 is known to have severe recurring congestion during peak hours. The congestion spans about 11 miles in the evening peak period in the central corridor area as it is considered the only non-tolled limited access facility connecting the Orlando Central Business District (CBD) and the tourist attractions area (Walt Disney World). Florida officials had been skeptical of tolling I-4 prior to the recent legislation, and the public through the media had been complaining about the excessive toll facilities in Central Florida. So, in search for plausible mitigation to the congestion on the I-4 corridor, this research is implemented to evaluate the effectiveness of different toll pricing alternatives that might divert traffic from I-4 to the toll facilities during the peak period. The network is composed of two main diverging limited access highways, freeway (I-4) and toll road (SR 417) in addition to two east-west parallel toll roads SR 408 and SR 528, intersecting the above-mentioned highways from both ends. I-4 and toll road SR 408 are the most frequently used route by commuters. SR-417 is a relatively uncongested toll road with 15 miles longer than I-4 and $5 tolls compared to no monetary cost on 1-4 for the same trip. The results of the calibrated Orlando PARAMICS network showed that percentages of route diversion vary from one route to another and depends primarily on the travel cost between specific origin-destination (O-D) pairs. Most drivers going from Disney (O1) or Lake Buena Vista (O2) to Lake Mary (D1) were found to have a high propensity towards using I-4, even when eliminating tolls and/or providing real-time information. However, a diversion from I-4 to SR 417 for these OD pairs occurred only in the cases of the incident and lane closure on I-4, due to the increase in delay and travel costs, and when information is provided to travelers. Furthermore, drivers that diverted from I-4 to SR 417 and SR 528 did not gain significant travel-time savings. This was attributed to the limited extra capacity of the alternative routes in the peak period and the longer traveling distance. When the remaining origin-destination pairs were analyzed, average travel time savings on I-4 ranged between 10 and 16% amounting to 10 minutes at the most with a 10% increase in the network average speed. High propensity of diversion on the network increased significantly when eliminating tolls on SR 417 and SR 528 while doubling the tolls on SR 408 along with the incident and lane closure scenarios on I-4 and with real-time information provided. The toll roads were found to be a viable alternative to I-4 for these specific OD pairs depending on the user perception of the toll cost which was reflected in their specific travel times. However, on the macroscopic level, it was concluded that route diversion through toll reduction or elimination on surrounding toll roads would only have a minimum impact on reducing I-4 congestion during the peak period.

Keywords: congestion pricing, dynamic feedback assignment, microsimulation, paramics, route diversion

Procedia PDF Downloads 149

Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

Procedia PDF Downloads 255

Authors: Prajakta Patil, Yash Huzurbazar, Abhijeet Shinde

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Aim: To develop a treatment protocol for the management of tinnitus through multi-modality brain stimulation. Methodology: Present study included 33 adults with unilateral (31 subjects) and bilateral (2 subjects) chronic tinnitus with and/or without hearing loss independent of their etiology. The Treatment protocol included 5 consecutive sessions with follow-up of 6 months. Each session was divided into 3 parts: • Pre-treatment: a) Informed consent b) Pitch and loudness matching. • Treatment: Bimanual paper pen task with tinnitus masking for 30 minutes. • Post-treatment: a) Pitch and loudness matching b) Directive counseling and obtaining feedback. Paper-pen task is to be performed bimanually that included carrying out two different writing activities in different context. The level of difficulty of the activities was increased in successive sessions. Narrowband noise of a frequency same as that of tinnitus was presented at 10 dBSL of tinnitus for 30 minutes simultaneously in the ear with tinnitus. Result: The perception of tinnitus was no longer present in 4 subjects while in remaining subjects it reduced to an intensity that its perception no longer troubled them without causing residual facilitation. In all subjects, the intensity of tinnitus decreased by an extent of 45 dB at an average. However, in few subjects, the intensity of tinnitus also decreased by more than 45 dB. The approach resulted in statistically significant reductions in Tinnitus Functional Index and Tinnitus Handicap Inventory scores. The results correlate with pre and post treatment score of Tinnitus Handicap Inventory that dropped from 90% to 0%. Discussion: Brain mapping(qEEG) Studies report that there is multiple parallel overlapping of neural subnetworks in the non-auditory areas of the brain which exhibits abnormal, constant and spontaneous neural activity involved in the perception of tinnitus with each subnetwork and area reflecting a specific aspect of tinnitus percept. The paper pen task and directive counseling are designed and delivered respectively in a way that is assumed to induce normal, rhythmically constant and premeditated neural activity and mask the abnormal, constant and spontaneous neural activity in the above-mentioned subnetworks and the specific non-auditory area. Counseling was focused on breaking the vicious cycle causing and maintaining the presence of tinnitus. Diverting auditory attention alone is insufficient to reduce the perception of tinnitus. Conscious awareness of tinnitus can be suppressed when individuals engage in cognitively demanding tasks of non-auditory nature as the paper pen task used in the present study. To carry out this task selective, divided, sustained, simultaneous and split attention act cumulatively. Bimanual paper pen task represents a top-down activity which underlies brain’s ability to selectively attend to the bimanual written activity as a relevant stimulus and to ignore tinnitus that is the irrelevant stimuli in the present study. Conclusion: The study suggests that this novel treatment approach is cost effective, time saving and efficient to vanish the tinnitus or to reduce the intensity of tinnitus to a negligible level and thereby eliminating the negative reactions towards tinnitus.

Keywords: multi-modality brain stimulation, neural subnetworks, non-auditory areas, paper-pen task, top-down activity

Procedia PDF Downloads 128

Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

Procedia PDF Downloads 51

Authors: Tirtha Raj Adhikari, Lochan Prasad Devkota

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This research studied the hydrological regime of three glacierized river basins in Khumbu, Langtang and Annapurna regions of Nepal using the Hydraologiska Byrans Vattenbalansavde (HBV), HVB-light 3.0 model. Future scenario of discharge is also studied using downscaled climate data derived from statistical downscaling method. General Circulation Models (GCMs) successfully simulate future climate variability and climate change on a global scale; however, poor spatial resolution constrains their application for impact studies at a regional or a local level. The dynamically downscaled precipitation and temperature data from Coupled Global Circulation Model 3 (CGCM3) was used for the climate projection, under A2 and A1B SRES scenarios. In addition, the observed historical temperature, precipitation and discharge data were collected from 14 different hydro-metrological locations for the implementation of this study, which include watershed and hydro-meteorological characteristics, trends analysis and water balance computation. The simulated precipitation and temperature were corrected for bias before implementing in the HVB-light 3.0 conceptual rainfall-runoff model to predict the flow regime, in which Groups Algorithms Programming (GAP) optimization approach and then calibration were used to obtain several parameter sets which were finally reproduced as observed stream flow. Except in summer, the analysis showed that the increasing trends in annual as well as seasonal precipitations during the period 2001 - 2060 for both A2 and A1B scenarios over three basins under investigation. In these river basins, the model projected warmer days in every seasons of entire period from 2001 to 2060 for both A1B and A2 scenarios. These warming trends are higher in maximum than in minimum temperatures throughout the year, indicating increasing trend of daily temperature range due to recent global warming phenomenon. Furthermore, there are decreasing trends in summer discharge in Langtang Khola (Langtang region) which is increasing in Modi Khola (Annapurna region) as well as Dudh Koshi (Khumbu region) river basin. The flow regime is more pronounced during later parts of the future decades than during earlier parts in all basins. The annual water surplus of 1419 mm, 177 mm and 49 mm are observed in Annapurna, Langtang and Khumbu region, respectively.

Keywords: temperature, precipitation, water discharge, water balance, global warming

Procedia PDF Downloads 318

Authors: N. Schneider, M. Bruchhage, M. Hartweg, G. Mutungi, J. O Regan, S. Deoni

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Observational neuroimaging studies suggest differences between breast-fed and formula-fed infants in developmental myelination, a key brain process for learning and cognitive development. However, the possible effects of a nutrient formulation on myelin development in healthy term infants in an intervention study have not been investigated. Objective was, therefore, to investigate the efficacy of a nutrient formulation with higher levels of myelin-relevant nutrients as compared to a control formulation with lower levels of the same nutrients on brain myelination and cognitive development in the first 6 months of life. The study is an ongoing randomized, controlled, double-blind, two-center, parallel-group clinical trial with a nonrandomized, non-blinded arm of exclusively breastfed infants. The current findings result from a staged statistical analysis at 6 months; the recruitment and intervention period has been completed for all participants. Follow-up visits at 12, 18 and 24 months are still ongoing. N= 81 enrolled full term, neurotypical infants of both sexes were randomized into either the investigational (N= 42) or the control group (N= 39), and N= 108 children in the breast-fed arm served as a natural reference group. The effect of a blend of docosahexaenoic acid, arachidonic acid, iron, vitamin B12, folic acid as well as sphingomyelin from a uniquely proceed whey protein concentrate enriched in alpha-lactalbumin and phospholipids in an infant nutrition product matrix was investigated. The main outcomes for the staged statistical analyses at 6 months included brain myelination measures derived from MRI. Additional outcomes were brain volume, cognitive development and safety. The full analyses set at 6 months comprised N= 66 infants. Higher levels of myelin-relevant nutrients compared to lower levels resulted in significant differences in myelin structure, volume, and rate of myelination as early as 3 and 6 months of life. The cross-sectional change of means between groups for whole-brain myelin volume was 8.4% for investigational versus control formulation (3.5% versus the breastfeeding reference) group at 3 months and increased to 36.4% for investigational versus control formulation (14.1% versus breastfeeding reference) at 6 months. No statistically significant differences were detected for early cognition scores. Safety findings were largely similar across groups. This is the first pediatric nutritional neuroimaging study demonstrating the efficacy of a myelin nutrient blend on developmental myelination in well-nourished term infants. Myelination is a critical process in learning and development. The effects were demonstrated across the brain, particularly in temporal and parietal regions, known to be functionally involved in sensory, motor and language skills. These first results add to the field of nutritional neuroscience by demonstrating early life nutrition benefits for brain architecture which may be foundational for later cognitive and behavioral outcomes. ClinicalTrials.gov Identifier: NCT03111927 (Infant Nutrition and Brain Development - Full-Text View - ClinicalTrials.gov).

Keywords: brain development, infant nutrition, MRI, myelination

Procedia PDF Downloads 164

Authors: Jothilakshmy Nagammal

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This research paper deals with the different case studies, where the Form-Based Codes are adopted in general and the different implementation methods in particular are discussed to develop a method for formulating a new planning model. The organizing principle of the Form-Based Codes, the transect is used to zone the city into various context specific transects. An approach is adopted to develop the new planning model, city Specific Planning Model (CSPM), as a tool to achieve sustainability for any city in general. A case study comparison method in terms of the planning tools used, the code process adopted and the various control regulations implemented in thirty two different cities are done. The analysis shows that there are a variety of ways to implement form-based zoning concepts: Specific plans, a parallel or optional form-based code, transect-based code /smart code, required form-based standards or design guidelines. The case studies describe the positive and negative results from based zoning, Where it is implemented. From the different case studies on the method of the FBC, it is understood that the scale for formulating the Form-Based Code varies from parts of the city to the whole city. The regulating plan is prepared with the organizing principle as the transect in most of the cases. The various implementation methods adopted in these case studies for the formulation of Form-Based Codes are special districts like the Transit Oriented Development (TOD), traditional Neighbourhood Development (TND), specific plan and Street based. The implementation methods vary from mandatory, integrated and floating. To attain sustainability the research takes the approach of developing a regulating plan, using the transect as the organizing principle for the entire area of the city in general in formulating the Form-Based Codes for the selected Special Districts in the study area in specific, street based. Planning is most powerful when it is embedded in the broader context of systemic change and improvement. Systemic is best thought of as holistic, contextualized and stake holder-owned, While systematic can be thought of more as linear, generalisable, and typically top-down or expert driven. The systemic approach is a process that is based on the system theory and system design principles, which are too often ill understood by the general population and policy makers. The system theory embraces the importance of a global perspective, multiple components, interdependencies and interconnections in any system. In addition, the recognition that a change in one part of a system necessarily alters the rest of the system is a cornerstone of the system theory. The proposed regulating plan taking the transect as an organizing principle and Form-Based Codes to achieve sustainability of the city has to be a hybrid code, which is to be integrated within the existing system - A Systemic Approach with a Systematic Process. This approach of introducing a few form based zones into a conventional code could be effective in the phased replacement of an existing code. It could also be an effective way of responding to the near-term pressure of physical change in “sensitive” areas of the community. With this approach and method the new Context Specific Planning Model is created towards achieving sustainability is explained in detail this research paper.

Keywords: context based planning model, form based code, transect, systemic approach

Procedia PDF Downloads 314

Authors: Veslava Osinska, Adam Szalach, Dominik Piotrowski

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Contemporary VR technologies allow users to explore virtual 3D spaces where they can work, socialize, learn, and play. User's interaction with GUI and the pictures displayed implicate perceptual and also cognitive processes which can be monitored due to neuroadaptive technologies. These modalities provide valuable information about the users' intentions, situational interpretations, and emotional states, to adapt an application or interface accordingly. Virtual galleries outfitted by specialized assets have been designed using the Unity engine BITSCOPE project in the frame of CHIST-ERA IV program. Users interaction with gallery objects implies the questions about his/her visual interests in art works and styles. Moreover, an attention, curiosity, and other emotional states are possible to be monitored and analyzed. Natural gaze behavior data and eye position were recorded by built-in eye-tracking module within HTC Vive headset gogle for VR. Eye gaze results are grouped due to various users’ behavior schemes and the appropriate perpetual-cognitive styles are recognized. Parallelly usability tests and surveys were adapted to identify the basic features of a user-centered interface for the virtual environments across most of the timeline of the project. A total of sixty participants were selected from the distinct faculties of University and secondary schools. Users’ primary knowledge about art and was evaluated during pretest and this way the level of art sensitivity was described. Data were collected during two months. Each participant gave written informed consent before participation. In data analysis reducing the high-dimensional data into a relatively low-dimensional subspace ta non linear algorithms were used such as multidimensional scaling and novel technique technique t-Stochastic Neighbor Embedding. This way it can classify digital art objects by multi modal time characteristics of eye tracking measures and reveal signatures describing selected artworks. Current research establishes the optimal place on aesthetic-utility scale because contemporary interfaces of most applications require to be designed in both functional and aesthetical ways. The study concerns also an analysis of visual experience for subsamples of visitors, differentiated, e.g., in terms of frequency of museum visits, cultural interests. Eye tracking data may also show how to better allocate artefacts and paintings or increase their visibility when possible.

Keywords: eye tracking, VR, UX, visual art, virtual gallery, visual communication

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

Procedia PDF Downloads 33

Authors: Avent Grange, Frederic Bertrand, Jean-Baptiste Droin, Amandine Marrel, Jean-Henry Ferrasse, Olivier Boutin

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CEA and its industrial partners are designing a gas Power Conversion System (PCS) based on a Brayton cycle for the ASTRID Sodium-cooled Fast Reactor. Investigations of control and regulation requirements to operate this PCS during operating, incidental and accidental transients are necessary to adapt core heat removal. To this aim, we developed a methodology to optimize the thermal-hydraulic behavior of the reactor during normal operations, incidents and accidents. This methodology consists of a multi-objective optimization for a specific sequence, whose aim is to increase component lifetime by reducing simultaneously several thermal stresses and to bring the reactor into a stable state. Furthermore, the multi-objective optimization complies with safety and operating constraints. Operating, incidental and accidental sequences use specific regulations to control the thermal-hydraulic reactor behavior, each of them is defined by a setpoint, a controller and an actuator. In the multi-objective problem, the parameters used to solve the optimization are the setpoints and the settings of the controllers associated with the regulations included in the sequence. In this way, the methodology allows designers to define an optimized and specific control strategy of the plant for the studied sequence and hence to adapt PCS piloting at its best. The multi-objective optimization is performed by evolutionary algorithms coupled to surrogate models built on variables computed by the thermal-hydraulic system code, CATHARE2. The methodology is applied to a loss of off-site power sequence. Three variables are controlled: the sodium outlet temperature of the sodium-gas heat exchanger, turbomachine rotational speed and water flow through the heat sink. These regulations are chosen in order to minimize thermal stresses on the gas-gas heat exchanger, on the sodium-gas heat exchanger and on the vessel. The main results of this work are optimal setpoints for the three regulations. Moreover, Proportional-Integral-Derivative (PID) control setting is considered and efficient actuators used in controls are chosen through sensitivity analysis results. Finally, the optimized regulation system and the reactor control procedure, provided by the optimization process, are verified through a direct CATHARE2 calculation.

Keywords: gas power conversion system, loss of off-site power, multi-objective optimization, regulation, sodium fast reactor, surrogate model

Procedia PDF Downloads 282

Authors: Judith Sue Hwa Joo

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Guanyin (Avalokiteśvara in Sanskrit), the Bodhisattva of Mercy and Compassion, is the most widely worshipped Buddhist Divinity in Chinese societies and is also believed by more than half of Asian populations across various countries. The most overwhelming reason for the popularity of Guanyin in Chinese societies is, according to the Lotus Sutra, that Guanyin would apperceive voices of those suffering from immense afflictions and troubles, and liberate them upon crying for his/her holy name with wholeheartedness. Its pervasive social influence has spanned more than two thousand years and is still deeply affecting the lives of most Chinese people. This study aimed to investigate whether Guanyin Worship has evolved and changed in modern Chinese societies across the Taiwan Strait. Taiwan and China, albeit having the same language and culture, have been territorially divided and governed by two different political regimes for over 70 years. It would be scientifically intriguing to unveil any substantial changes in religious behaviors in the context of Guanyin Worship. A comprehensive anonymous questionnaire survey in Chinese communities was conducted from October 2017 to May 2019 across various countries, mostly in China, Taiwan, and Hong Kong areas. Since the religious survey is officially prohibited in China, the study was difficult and could only be exercised by means of snowball sampling. Demographic data (age, sex, education, religious belief) were registered and Guanyin’s salvation functions under various confronting situations were investigated. Psychological dimensions of religious belief in Guanyin were probed in terms of the worship experience, the willingness of veneration, and egoistic or altruistic ideations. A literature review on documented functional attributes was carried out in parallel for comparison analyses with traditional roles. Effective 1123 out of 1139 samples were obtained. Statistical analysis revealed that Guanyin Worship is still commonly practiced and deeply rooted in the hearts of all Chinese people regardless of gender, age, education, and residential area, even though they may not enshrine Guanyin at home nowadays. The conventional roles of Guanyin Bodhisattva are still valid and best satisfy the real interests of lifestyles in modern times. When comparing the traditional Buddhist Sutra and the documented literature, the divine power of modern Guanyin has notably empowered to recover, protect and transform fetal and infant spirits due to the sexual liberation, increased abortion rate, gender awakening and enhanced female autonomy in the reproductive decision. However, the One-Child policy may have critically impacted the trajectory of Guanyin Worship so that people in China prevail over those in Taiwan praying for aborted lives or premature deaths. Furthermore, particularly in Hong Kong and Macao, Guanyin not only serves as the sea guardian for the fishermen but also additional services a new function as the God of Wealth. The divine powers and salvation functions of Guanyin are indeed evolving and expanding to comply with the modern psychosocial, cultural and societal needs. This study sheds light on the modernization process of the two-thousand-year-old Guanyin Worship of contemporary Chinese societies.

Keywords: Buddhism, Guanyin, religious behavior, salvation function

Procedia PDF Downloads 94

Authors: Justin Byrne, Jose Ramon Cobo

Abstract:

The growing importance of cross- or intercultural competencies (used here interchangeably) for the business and management professionals is now a commonplace in both academic and professional literature. This reflects two parallel developments. On the one hand, it is a consequence of the increased attention paid to a whole range of 'soft skills', now seen as fundamental in both individuals' and corporate success. On the other hand, and more specifically, the increasing demand for interculturally competent professionals is a corollary of ongoing processes of globalization, which multiply and intensify encounters between individuals and companies from different cultural backgrounds. Business schools have, for some decades, responded to the needs of the job market and their own students by providing students with training in intercultural skills, as they are encouraged to do so by the major accreditation agencies on both sides of the Atlantic. Adapting Early and Ang's (2003) formulation of Cultural Intelligence (CQ), this paper aims to help fill the lagunae in the current literature on intercultural training in three main ways. First, it offers an in-depth analysis of the CQ of a little studied group: contemporary Millenial and 'Generation Z' Business School students. The level of analysis distinguishes between the four different dimensions of CQ, cognition, metacognition, motivation and behaviour, and thereby provides a detailed picture of the strengths and weaknesses in CQ of the group as a whole, as well as of different sub-groups and profiles of students. Secondly, by crossing these individual-level findings with respondents' socio-cultural and educational data, this paper also proposes and tests hypotheses regarding the relative impact and importance of four possible antecedents of intercultural skills identified in the literature: prior international experience; intercultural training, foreign language proficiency, and experience of cultural diversity in habitual country of residence. Third, we use this analysis to suggest data-based intercultural training priorities for today's management students. These conclusions are based on the statistical analysis of individual responses of some 300 Bachelor or Masters students in a major European Business School provided to two on-line surveys: Ang, Van Dyne, et al's (2007) standard 20-question self-reporting CQ Scale, and an original questionnaire designed by the authors to collate information on respondent's socio-demographic and educational profile relevant to our four hypotheses and explanatory variables. The data from both instruments was crossed in both descriptive statistical analysis and regression analysis. This research shows that there is no statistically significant and positive relationship between the four antecedents analyzed and overall CQ level. The exception in this respect is the statistically significant correlation between international experience, and the cognitive dimension of CQ. In contrast, the results show that the combination of international experience and foreign language skills acting together, does have a strong overall impact on CQ levels. These results suggest that selecting and/or training students with strong foreign language skills and providing them with international experience (through multinational programmes, academic exchanges or international internships) constitutes one effective way of training culturally intelligent managers of tomorrow.

Keywords: business school, cultural intelligence, millennial, training

Procedia PDF Downloads 133

Authors: Atsuko Okazaki, Jurg Ott

Abstract:

Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

Procedia PDF Downloads 102