Search results for: time series data mining

Authors: Natalia A. Salazar, Erika K. Méndez, Catalina Álvarez, Carlos E. Orrego

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Lyophilization, also called freeze-drying, is an important dehydration technique mainly used for pharmaceuticals. Food industry also uses lyophilization when it is important to retain most of the nutritional quality, taste, shape and size of dried products and to extend their shelf life. Vacuum-Induced during freezing cycle (VI) has been used in order to control ice nucleation and, consequently, to reduce the time of primary drying cycle of pharmaceuticals preserving quality properties of the final product. This procedure has not been applied in freeze drying of foods. The present work aims to investigate the effect of VI on the lyophilization drying time, final moisture content, density and reconstitutional properties of mango (Mangifera indica L.) slices (MS) and mango pulp-maltodextrin dispersions (MPM) (30% concentration of total solids). Control samples were run at each freezing rate without using induced vacuum. The lyophilization endpoint was the same for all treatments (constant difference between capacitance and Pirani vacuum gauges). From the experimental results it can be concluded that at the high freezing rate (0.4°C/min) reduced the overall process time up to 30% comparing process time required for the control and VI of the lower freeze rate (0.1°C/min) without affecting the quality characteristics of the dried product, which yields a reduction in costs and energy consumption for MS and MPM freeze drying. Controls and samples treated with VI at freezing rate of 0.4°C/min in MS showed similar results in moisture and density parameters. Furthermore, results from MPM dispersion showed favorable values when VI was applied because dried product with low moisture content and low density was obtained at shorter process time compared with the control. There were not found significant differences between reconstitutional properties (rehydration for MS and solubility for MPM) of freeze dried mango resulting from controls, and VI treatments.

Keywords: drying time, lyophilization, mango, vacuum induced freezing

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Authors: Wei-Chou Hsu, Ching-Sung Lee, Han-Yin Liu

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This work uses H2O2 oxidation technique to improve the pH sensitivity of the AlGaN/GaN-based ion-sensitive field-effect transistors (ISFETs). 10-nm-thick Al2O3 was grown on the surface of the AlGaN. It was found that the pH sensitivity was improved from 41.6 mV/pH to 55.2 mV/pH. Since the H2O2-grown Al2O3 was served as a passivation layer and the problem of Fermi-level pinning was suppressed for the ISFET with the H2O2 oxidation process. Hysteresis effect in the ISFET with the H2O2 treatment also became insignificant. The hysteresis effect was observed by dipping the ISFETs into different pH value solutions and comparing the voltage difference between the initial and final conditions. The hysteresis voltage (Vhys) of the ISFET with the H2O2 oxidation process was improved from 8.7 mV to 4.8 mV. The hysteresis effect is related to the buried binding sites which are related to the material defects like threading dislocations in the AlGaN/GaN heterostructure which was grown by the hetero-epitaxy technique. The H2O2-grown Al2O3 passivate these material defects and the Al2O3 has less material defects. The long-term stability of the ISFET is estimated by the drift effect measurement. The drift measurement was conducted by dipping the ISFETs into a specific pH value solution for 12 hours and the ISFETs were operating at a specific quiescent point. The drift rate is estimated by the drift voltage divided by the total measuring time. It was found that the drift rate of the ISFET was improved from 10.1 mV/hour to 1.91 mV/hour in the pH 7 solution, from 14.06 mV/hour to 6.38 mV/pH in the pH 2 solution, and from 12.8 mV/hour to 5.48 mV/hour in the pH 12 solution. The drift effect results from the capacitance variation in the electric double layer. The H2O2-grown Al2O3 provides an additional capacitance connection in series with the electric double layer. Therefore, the capacitance variation of the electric double layer became insignificant. Generally, the H2O2 oxidation process is a simple, fast, and cost-effective method for the AlGaN/GaN-based ISFET. Furthermore, the performance of the AlGaN/GaN ISFET was improved effectively and the non-ideal effects were suppressed.

Keywords: AlGaN/GaN, Al2O3, hysteresis effect, drift effect, reliability, passivation, pH sensors

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Authors: Richard Byrne, Emma Mulliner

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Smart cities are a vision for the future that is increasingly becoming a reality. While a key concept of the smart city is the ability to capture, communicate, and process data that has long been produced through day-to-day activities of the city, much of the assessment models in place neglect this fact to focus on ‘smartness’ concepts. Although it is true technology often provides the opportunity to capture and communicate data in more effective ways, there are also human processes involved that are just as important. The growing importance with regards to the use and ownership of data in society can be seen by all with companies such as Facebook and Google increasingly coming under the microscope, however, why is the same scrutiny not applied to cities? The research area is therefore of great importance to the future of our cities here and now, while the findings will be of just as great importance to our children in the future. This research aims to understand the influence data is having on organisations operating throughout the smart cities sector and employs a mixed-method research approach in order to best answer the following question: Would a data-based evaluation model for smart cities be more appropriate than a smart-based model in assessing the development of the smart city? A fully comprehensive literature review concluded that there was a requirement for a data-driven assessment model for smart cities. This was followed by a documentary analysis to understand the root source of data integration to the smart city. A content analysis of city data platforms enquired as to the alternative approaches employed by cities throughout the UK and draws on best practice from New York to compare and contrast. Grounded in theory, the research findings to this point formulated a qualitative analysis framework comprised of: the changing environment influenced by data, the value of data in the smart city, the data ecosystem of the smart city and organisational response to the data orientated environment. The framework was applied to analyse primary data collected through the form of interviews with both public and private organisations operating throughout the smart cities sector. The work to date represents the first stage of data collection that will be built upon by a quantitative research investigation into the feasibility of data network effects in the smart city. An analysis into the benefits of data interoperability supporting services to the smart city in the areas of health and transport will conclude the research to achieve the aim of inductively forming a framework that can be applied to future smart city policy. To conclude, the research recognises the influence of technological perspectives in the development of smart cities to date and highlights this as a challenge to introduce theory applied with a planning dimension. The primary researcher has utilised their experience working in the public sector throughout the investigation to reflect upon what is perceived as a gap in practice of where we are today, to where we need to be tomorrow.

Keywords: data, planning, policy development, smart cities

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Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Rachel Goh, Alexander McNab, Brent Alsop, David O'Hare

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In a busy airport, the decision whether to take passengers aside and search their luggage for dangerous items can have important consequences. If an officer fails to search and stop a bag containing a dangerous object, a life-threatening incident might occur. But stopping a bag unnecessarily means that the officer might lose time searching the bag and face an angry passenger. Passengers’ bags, however, are often cluttered with personal belongings of varying shapes and sizes. It can be difficult to determine what is dangerous or not, especially if the decisions must be made quickly in cases of busy flight schedules. Additionally, the decision to search bags is often made with input from the surrounding officers on duty. This scenario raises several questions: 1) Past findings suggest that humans are more reliant on an automated aid when under time pressure in a visual search task, but does this translate to human-human reliance? 2) Are humans more likely to agree with another person if the person is assumed to be an expert or a novice in these ambiguous situations? In the present study, forty-one participants performed a simulated luggage-screening task. They were partnered with an advisor of two different statuses (expert vs. novice), but of equal accuracy (90% correct). Participants made two choices each trial: their first choice with no advisor input, and their second choice after advisor input. The second choice was made within either 2 seconds or 8 seconds; failure to do so resulted in a long time-out period. Under the 2-second time pressure, participants were more likely to disagree with their own first choice and agree with the expert advisor, regardless of whether the expert was right or wrong, but especially when the expert suggested that the bag was safe. The findings indicate a tendency for people to assume less responsibility for their decisions and defer to their partner, especially when a quick decision is required. This over-reliance on others’ opinions might have negative consequences in real life, particularly when relying on fallible human judgments. More awareness is needed regarding how a stressful environment may influence reliance on other’s opinions, and how better techniques are needed to make the best decisions under high stress and time pressure.

Keywords: advisors, decision-making, time pressure, trust

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Vivian Isaac, A. T. Joteeshwaran, Craig McLachlan

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The University of New South Wales (UNSW) Rural Clinical School has a strategic collaborative focus on chronic disease and public health. Our objectives are to understand rural environmental and biological interactions in vulnerable community populations. The UNSW Rural Clinical School translational model is a spoke and hub network. This spoke and hub model connects rural data and biological specimens with city based collaborative public health research networks. Similar spoke and hub models are prevalent across research centers in India. The Australia-India Council grant was awarded so we could establish sustainable public health and community research collaborations. As part of the collaborative network we are developing strategies around data and biological sharing platforms between Indian Institute of Public Health, Public Health Foundation of India (PHFI), Hyderabad and Rural Clinical School UNSW. The key objective is to understand how research collaborations are conducted in India and also how data can shared and tracked with external collaborators such as ourselves. A framework to improve data sharing for research collaborations, including DNA was proposed as a project outcome. The complexities of sharing biological data has been investigated via a visit to India. A flagship sustainable project between Rural Clinical School UNSW and PHFI would illustrate a model of data sharing platforms.

Keywords: data sharing, collaboration, public health research, chronic disease

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Authors: Mohamed Bekhit Gad Khalil

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This dam is the oldest dam in history. It was built by the ancient Egyptian around (2650 B.C) control flooding. It is believed to have been built between the third and fourth dynasties .It contains the oldest dam in history. Many studies have been conducted for the dam. This report was prepared under my supervision and in cooperation with the Ministry of Tourism and Antiquities. The dam was re-documented and photographed again. The dam on the northern side Consists of irregularly shaped stones of varying sizes used randomly. Sand and soil fill the gaps between the stones. creating layers to form the body of the dam. The eastern. side of the dam Consists of a series of regular shaped stones that have been cut and constructed into a stepped pyramid-like structure with width of (15,7) meters and height of (10) meters. The surface has significant erosion and wear on the stones due to weather Conditions. which has resulted in deep cavities in most of the stone blocks forming the surface.

Keywords: ministry of tourism and antiquities, excavations, registration, documentation

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Authors: Min Kyu Kim, Eun Young Lee, Dong Woo Son, Yoon Seok Chang

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In this paper, we introduce a flexible box erecting machine (BEM) that swiftly and automatically transforms cardboard into a three dimensional box. Recently, the parcel service and home-shopping industries have grown rapidly, and there is an increasing need for various box types to ship various products. However, workers cannot fold thousands of boxes manually in a day. As such, automatic BEMs are garnering greater attention. This study takes equipment operation into consideration as well as mechanical improvements in order to design a BEM that is able to outperform its conventional counterparts. We analyzed six dispatching rules – First In First Out (FIFO), Shortest Processing Time (SPT), Earliest Due Date (EDD), Setup Avoidance, EDD + SPT, and EDD + Setup Avoidance – to determine which one was most suitable for BEM operation. Consequently, SPT and Setup Avoidance were found to be the most critical rules, followed by EDD + Setup Avoidance, EDD + SPT, EDD, and FIFO. This hierarchy was valid for both our conventional BEM and our new flexible BEM from the viewpoint of processing time. We believe that this research can contribute to flexible BEM management, which has the potential to increase productivity and convenience.

Keywords: automation, box erecting machine, dispatching rule, setup time

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Authors: Rui Calejo Rodrigues

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Building Refurbishing and Maintenance is a major area of knowledge ultimately dispensed to user/occupant criteria. The optimization of the service life of a building needs a special background to be assessed as it is one of those concepts that needs proficiency to be implemented. ISO 15686-2 Buildings and constructed assets - Service life planning: Part 2, Service life prediction procedures, states a factorial method based on deterministic data for building components life span. Major consequences result on a deterministic approach because users/occupants are not sensible to understand the end of components life span and so simply act on deterministic periods and so costly and resources consuming solutions do not meet global targets of planet sustainability. The estimation of 2 thousand million conventional buildings in the world, if submitted to a probabilistic method for service life planning rather than a deterministic one provide an immense amount of resources savings. Since 1989 the research team nowadays stating for CEES–Center for Building in Service Studies developed a methodology based on Montecarlo method for probabilistic approach regarding life span of building components, cost and service life care time spans. The research question of this deals with the importance of probabilistic approach of buildings life planning compared with deterministic methods. It is presented the mathematic model developed for buildings probabilistic lifespan approach and experimental data is obtained to be compared with deterministic data. Assuming that buildings lifecycle depends a lot on component replacement this methodology allows to conclude on the global impact of fixed replacements methodologies such as those on result of deterministic models usage. Major conclusions based on conventional buildings estimate are presented and evaluated under a sustainable perspective.

Keywords: building components life cycle, building maintenance, building sustainability, Montecarlo Simulation

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Authors: Iman Abu-Rub

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This research paper examines if Artificial Intelligence is, in fact, racist or not. Different studies from all around the world, and covering different communities were analyzed to further understand AI’s true implications over different communities. The black community, Asian community, and Muslim community were all analyzed and discussed in the paper to figure out if AI is biased or unbiased towards these specific communities. It was found that the biggest problem AI faces is the biased distribution of data collection. Most of the data inserted and coded into AI are of a white male, which significantly affects the other communities in terms of reliable cultural, political, or medical research. Nonetheless, there are various research was done that help increase awareness of this issue, but also solve it completely if done correctly. Governments and big corporations are able to implement different strategies into their AI inventions to avoid any racist results, which could cause hatred culturally but also unreliable data, medically, for example. Overall, Artificial Intelligence is not racist per se, but the data implementation and current racist culture online manipulate AI to become racist.

Keywords: social media, artificial intelligence, racism, discrimination

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Authors: Ruth Shapira

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Introduction: The rapid urbanization of the last century confronts planners, regulatory bodies, developers and most of all – the public with seemingly unsolved conflicts regarding values, capital, and wellbeing of the built and un-built urban space. This is reflected in the quality of the urban form and life which has known no significant progress in the last 2-3 decades despite the on-growing urban population. It is the objective of this paper to analyze some of these fundamental issues through the case study of a relatively small town in the center of Israel (Kiryat-Ono, 100,000 inhabitants), unfold the deep structure of qualities versus disruptors, present some cure that we have developed to bridge over and humbly suggest a practice that may be generic for similar cases. Basic Methodologies: The OBJECT, the town of Kiryat Ono, shall be experimented upon in a series of four action processes: De-composition, Re-composition, the Centering process and, finally, Controlled Structural Disintegration. Each stage will be based on facts, analysis of previous multidisciplinary interventions on various layers – and the inevitable reaction of the OBJECT, leading to the conclusion based on innovative theoretical and practical methods that we have developed and that we believe are proper for the open ended network, setting the rules for the contemporary urban society to cluster by. The Study: Kiryat Ono, was founded 70 years ago as an agricultural settlement and rapidly turned into an urban entity. In spite the massive intensification, the original DNA of the old small town was still deeply embedded, mostly in the quality of the public space and in the sense of clustered communities. In the past 20 years, the recent demand for housing has been addressed to on the national level with recent master plans and urban regeneration policies mostly encouraging individual economic initiatives. Unfortunately, due to the obsolete existing planning platform the present urban renewal is characterized by pressure of developers, a dramatic change in building scale and widespread disintegration of the existing urban and social tissue. Our office was commissioned to conceptualize two master plans for the two contradictory processes of Kiryat Ono’s future: intensification and conservation. Following a comprehensive investigation into the deep structures and qualities of the existing town, we developed a new vocabulary of conservation terms thus redefying the sense of PLACE. The main challenge was to create master plans that should offer a regulatory basis to the accelerated and sporadic development providing for the public good and preserving the characteristics of the PLACE consisting of a tool box of design guidelines that will have the ability to reorganize space along the time axis in a coherent way. In Conclusion: The system of rules that we have developed can generate endless possible patterns making sure that at each implementation fragment an event is created, and a better place is revealed. It takes time and perseverance but it seems to be the way to provide a healthy framework for the accelerated urbanization of our chaotic present.

Keywords: housing, architecture, urban qualities, urban regeneration, conservation, intensification

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Jun Gil Ahn, Jong Tae Kim

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In this paper, we study the sensor fusion structure on the multipoint control unit (MCU). Sensor fusion using Kalman filter for 9-axis sensors is considered. The 9-axis inertial sensor is the combination of 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We implement the sensor fusion structure among the sensor hubs in MCU and measure the execution time, power consumptions, and total energy. Experiments with real data from 9-axis sensor in 20Mhz show that the average power consumptions are 44mW and 48mW on Cortx-M0 and Cortex-M3 MCU, respectively. Execution times are 613.03 us and 305.6 us respectively.

Keywords: 9-axis sensor, Kalman filter, MCU, sensor fusion

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Authors: Jeferson Gomes Dos Santos, Mirelle Cristina De Abreu Quintela

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Considering that after the 1988 Constitution, in Brazil, municipalities have acquired new roles in the face of a financial reality that jeopardizes more substantial actions, the Public Budget is essential for the establishment of guidelines for action, within each budgetary reality. Within this, the present work sought to understand the budget profile of the mining municipality of Ataleia, with a view to identifying its budget composition, in relation to the main sources of revenue and expenditure. To achieve the purposes of the study, information was collected on the municipality's finances, from the years 2000 to 2016, visualizing the progress of its revenues in terms of funding and origin, and expenses in terms of nature and purpose. It was evidenced that the municipality, having its budget revenue in the period, still shows great dependence on intergovernmental transfers, as the own collection was relatively low. The budget expenditure of the period was mainly influenced by social expenditures, but it must be said that the municipality complied with the limits of spending, minimum and maximum, established by law.

Keywords: expenses, municipal budget, planning, revenue

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Authors: Akhtar Rasool, Tasneem Zahra Rizvi

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In this study, a series of the cadmium sulphide quantum dots/polyaniline nanocomposites with varying compositions were prepared by in-situ polymerization technique and were characterized using X-ray diffraction and Fourier transform infrared spectroscopy. The surface morphology was studied by scanning electron microscopy. UV-Visible spectroscopy was used to find out the energy band gap of the nanoparticles and the nanocomposites. Temperature dependence of DC electrical conductivity and temperature and frequency dependence of AC conductivity were investigated to study the charge transport mechanism in the nanocomposites. DC conductivity was found to be a typical for a semiconducting behavior following Mott’s 1D variable range hoping model. The frequency dependent AC conductivity followed the universal power law.

Keywords: conducting polymers, nanocomposites, polyaniline composites, quantum dots

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Authors: Divya Juneja, Sukhjeet Kaur Matharu

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Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Ravid Inbar

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CaMKII (calcium-calmodulin dependent protein kinase II) makes up 2% of the protein in our brain and has a critical role in memory formation and long-term potentiation of neurons. Despite this, research has yet to uncover the role of one of the domains on the activation of this kinase. The following proposes to express the protein without the hub domain in E. coli, leaving only the kinase and regulatory segment of the protein. Next, a series of kinase assays will be conducted to elucidate the role the hub domain plays on CaMKII sensitivity to calcium/calmodulin activation. The hub domain may be important for activation; however, it may also be a variety of domains working together to influence protein activation and not the hub alone. Characterization of a protein is critical to the future understanding of the protein's function, as well as for producing pharmacological targets in cases of patients with diseases.

Keywords: CaMKII, hub domain, kinase assays, kinase + reg seg

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Wiyanti Fransisca Simanullang, Shinya Yamanaka

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Grinding method was used to control the active site and to improve the specific surface area (SSA) of calcium oxide (CaO) derived from scallop shell as a sustainable resource. The dry grinding of CaO with acetone and tertiary butanol as a liquid additive was carried out using a planetary ball mill with a laboratory scale. The experiments were operated by stepwise addition with time variations to determine the grinding limit. The active site of CaO was measured by X-Ray Diffraction and FT-IR. The SSA variations of products with grinding time were measured by BET method. The morphology structure of CaO was observed by SEM. The use of liquid additive was effective for increasing the SSA and controlling the active site of CaO. SSA of CaO was increased in proportion to the amount of the liquid additive and the grinding time. The performance of CaO as a solid base catalyst for biodiesel production was tested in the transesterification reaction of used cooking oil to produce fatty acid methyl ester (FAME).

Keywords: active site, calcium oxide, grinding, specific surface area

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Authors: Mark Joseph Quinto, Roan Beronilla, Guiller Damian, Eliza Camaso, Ronaldo Alberto

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Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.

Keywords: carbon stock, forest inventory, LiDAR, tree count

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Authors: Lubna M. A. Hassan, Nasir Brema, Abdallah Mamy, Insaf Yahya, Tanzil A. G., Ahmed M. M. Hasoba, Omer A. Suliman

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The Nubian ibex species has been categorized as vulnerable by the International Union for Conservation of Nature (IUCN) due to a lack of population data in specific regions within their habitat. This species faces numerous challenges, including habitat loss caused by agricultural practices, livestock rearing, mining activity, and infrastructure development. Additionally, competition with non-native species and hunting pose significant threats to their survival. Unfortunately, studies on the distribution, conservation status, ecology, and health of the ibex are limited and primarily descriptive in nature. In order to bridge this knowledge gap, recent surveys were conducted in the Red Sea State of Sudan during specific periods in 2015, 2016, 2019, and 2021. These surveys have provided valuable insights into the distribution, habitats, and conservation status of the Nubian ibex in the Red Sea State. The findings indicate that the Capra nubiana ibex can be found across more than 17 mountains in the Red Sea State. However, the total population estimate from recent years suggests that there are fewer than 250 individuals remaining. The study has also identified the highest altitude at which the Nubian ibex habitats existed in Sudan's Red Sea State, measuring 1675 m. This area harbors a diverse array of Nubian ibex habitats, encompassing a total of 21 wild plant species from 10 distinct families. The region experiences an average annual temperature ranging from 20.64°C in January to 33.30°C in August. Precipitation occurs in November and December, although it is characterized by unreliability and erratic patterns. It is important to note that these population estimates were obtained through surveys conducted in collaboration with rangers and local communities, and adjustments to survey methods are necessary to accommodate the challenging mountainous terrain, such as utilizing aerial surveys. To effectively address these threats, it is imperative to establish comprehensive long-term monitoring programs.

Keywords: Nubian ibex, distribution, population, habitats

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Authors: Sania Ashraf

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The paper focuses on the prevalence of informational efficiency and integration of GCC Shariah Equity market for the period of 01st January 2010 to 31st June 2015 with daily equity returns of Kuwait, Oman, Qatar, Bahrain, Saudi Arabia and United Arab Emirates. The study employs traditional as well as the modern approach of tracing out the efficiency and integration in the return series. From the results of efficiency it was observed that the market lacked efficiency in terms of its past information. The results of integration test clearly indicates that there was a long memory in the returns of GCC Shariah during the study period. Hence it was concluded and proved that the returns of all GCC Equity Shariah were not informationally efficient but fractionally integrated during the study period.

Keywords: efficiency, Fama, GCC shariah, hurst exponent, integration, serial correlation

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Seham A. S. Mohamed, Sarah R. Baker, Christopher Deery, Mario V. Vettore

Abstract:

Background: The link between oral health conditions and school performance and attendance remain unclear among Middle Eastern children. The association has been studied extensively in the Western region; however, several concerns have been raised regarding the reliability and validity of measures, low quality of studies, inadequate inclusion of potential confounders, and the lack of a conceptual framework. These limitations have meant that, to date, there has been no detailed understanding of the association or of the key social, clinical, behavioural and parental factors which may impact the association. Aim: To examine the association between oral health conditions and children’s school performance and attendance at Grade 2 in Muharraq city in the Kingdom of Bahrain using Heilmann et al.’s (2015) life course framework for oral health. Objectives: To (1) describe the prevalence of oral health conditions among 7-8 years old schoolchildren in the city of Muharraq; (2) analyse the social, biological, behavioural, and parental pathways that link early and current life exposures with children’s current oral health status; (3) examine the association between oral health conditions and school performance and attendance among schoolchildren; (4) explore the early and current life course social, biological, behavioural and parental factors associated with children’s school outcomes. Design: A time-ordered-cross-sectional study was conducted with 466 schoolchildren aged 7-8 years and their parents from Muharraq city in KoB. Data were collected through parents’ self-administered questionnaires, children’s face-face interviews, and dental clinical examinations. Outcome variables, including school performance and school attendance data, were obtained from the parents and school records. The data were analysed using structural equation modelling (SEM). Results: Dental caries, the consequence of dental caries (PUFA/pufa), and enamel developmental defects (EDD) prevalence were 93.4%, 25.7%, and 17.2%, respectively. The findings from the SEM showed that children born in families with high SES were less likely to suffer from dentine dental caries (β= -0.248) and more likely to earn high school performance (β= 0.136) at 7-8 years of age in Muharraq. From the current life course of children, the dental plaque was associated significantly and directly with enamel caries (β= 0.094), dentine caries (β= 0.364), treated teeth (filled or extracted because of dental caries) (β= 0.121), and indirectly associated with dental pain (β= 0.057). Further, dentine dental caries was associated significantly and directly with low school performance (β= -0.155). At the same time, the dental plaque was indirectly associated with low school performance via dental caries (β = −0.044). Conversely, treated teeth were associated directly with high school performance (β= 0.100). Notably, none of the OHCs, biological, SES, behavioural, or parental conditions was related to school attendance in children. Conclusion: The life course approach was adequate to examine the role of OHCs on children’s school performance and attendance. Birth and current (7-8-year-olds) social factors were significant predictors of poor OH and poor school performance.

Keywords: dental caries, life course, Bahrain, school outcomes

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Authors: Iveta Bryjova

Abstract:

Recording of viscoelastic strain-vs-time curves with the aid of the suction method and a follow-up analysis, resulting into evaluation of standard viscoelastic parameters, is a significant technique for non-invasive contact diagnostics of mechanical properties of skin and assessment of its conditions, particularly in acute burns, hypertrophic scarring (the most common complication of burn trauma) and reconstructive surgery. For elimination of the skin thickness contribution, usable viscoelastic parameters deduced from the strain-vs-time curves are restricted to the relative ones (i.e. those expressed as a ratio of two dimensional parameters), like grosselasticity, net-elasticity, biological elasticity or Qu’s area parameters, in literature and practice conventionally referred to as R2, R5, R6, R7, Q1, Q2, and Q3. With the exception of parameters R2 and Q1, the remaining ones substantially depend on the position of inflection point separating the elastic linear and viscoelastic segments of the strain-vs-time curve. The standard algorithm implemented in commercially available devices relies heavily on the experimental fact that the inflection time comes about 0.1 sec after the suction switch-on/off, which depreciates credibility of parameters thus obtained. Although the Qu’s US 7,556,605 patent suggests a method of improving the precision of the inflection determination, there is still room for nonnegligible improving. In this contribution, a novel method of inflection point determination utilizing the advantageous properties of the Savitzky–Golay filtering is presented. The method allows computation of derivatives of smoothed strain-vs-time curve, more exact location of inflection and consequently more reliable values of aforementioned viscoelastic parameters. An improved applicability of the five inflection-dependent relative viscoelastic parameters is demonstrated by recasting a former study under the new method, and by comparing its results with those provided by the methods that have been used so far.

Keywords: Savitzky–Golay filter, scarring, skin, viscoelasticity

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Authors: Taner Ersoz, Filiz Ersoz

Abstract:

This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

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Authors: Jesika A. Walker, Mohammed Aswad, Guy Lacroix, Denis Cousineau

Abstract:

There is a fundamental disconnect between the experience of time passing and the chronometric units by which time is quantified. Specifically, there appears to be no relationship between the passage of time judgments (PoTJs) and verbal duration estimates at short durations (e.g., < 2000 milliseconds). When a duration is longer than several minutes, however, evidence suggests that a slower feeling of time passing is predictive of overestimation. Might the length of a task moderate the relation between PoTJs and duration estimates? Similarly, the estimation paradigm (prospective vs. retrospective) and the mental effort demanded of a task (task demand) have both been found to influence duration estimates. However, only a handful of experiments have investigated these effects for tasks of long durations, and the results have been mixed. Thus, might the length of a task also moderate the effects of the estimation paradigm and task demand on duration estimates? To investigate these questions, 273 participants performed either an easy or difficult visual and memory search task for either eight or 58 minutes, under prospective or retrospective instructions. Afterward, participants provided a duration estimate in minutes, followed by a PoTJ on a Likert scale (1 = very slow, 7 = very fast). A 2 (prospective vs. retrospective) × 2 (eight minutes vs. 58 minutes) × 2 (high vs. low difficulty) between-subjects ANOVA revealed a two-way interaction between task demand and task duration on PoTJs, p = .02. Specifically, time felt faster in the more challenging task, but only in the eight-minute condition, p < .01. Duration estimates were transformed into RATIOs (estimate/actual duration) to standardize estimates across durations. An ANOVA revealed a two-way interaction between estimation paradigm and task duration, p = .03. Specifically, participants overestimated the task more if they were given prospective instructions, but only in the eight-minute task. Surprisingly, there was no effect of task difficulty on duration estimates. Thus, the demands of a task may influence ‘feeling of time’ and ‘estimation time’ differently, contributing to the existing theory that these two forms of time judgement rely on separate underlying cognitive mechanisms. Finally, a significant main effect of task duration was found for both PoTJs and duration estimates (ps < .001). Participants underestimated the 58-minute task (m = 42.5 minutes) and overestimated the eight-minute task (m = 10.7 minutes). Yet, they reported the 58-minute task as passing significantly slower on a Likert scale (m = 2.5) compared to the eight-minute task (m = 4.1). In fact, a significant correlation was found between PoTJ and duration estimation (r = .27, p <.001). This experiment thus provides evidence for a compensatory effect at longer durations, in which people underestimate a ‘slow feeling condition and overestimate a ‘fast feeling condition. The results are discussed in relation to heuristics that might alter the relationship between these two variables when conditions range from several minutes up to almost an hour.

Keywords: duration estimates, long durations, passage of time judgements, task demands

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