Search results for: spatial audio processing

Authors: Manel Hannachi, Mustapha Nechiche, Said Azem

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This study focuses on biodegradable materials made from Poly-lactic acid (PLA) and vegetal reinforcements. Three materials are developed from PLA, as a matrix, and : (i) olive kernels (OK); (ii) alfa (α) short fibers and (iii) OK+ α mixture, as reinforcements. After processing of PLA pellets and olive kernels in powder and alfa stems in short fibers, three mixtures, namely PLA-OK, PLA-α, and PLA-OK-α are prepared and homogenized in Turbula®. These mixtures are then compacted at 180°C under 10 MPa during 15 mn. Scanning Electron Microscopy (SEM) examinations show that PLA matrix adheres at surface of all reinforcements and the dispersion of these ones in matrix is good. X-ray diffraction (XRD) analyses highlight an increase of PLA inter-reticular distances, especially for the PLA-OK case. These results are explained by the dissociation of some molecules derived from reinforcements followed by diffusion of the released atoms in the structure of PLA. This is consistent with Fourier Transform Infrared Spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC) analysis results.

Keywords: alfa short fibers, biodegradable composite, olive kernels, poly-lactic acid

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Authors: Saeed Vahedikamal, Ian Hepburn

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Microwave Kinetic Inductance Detectors (MKIDs) are considered as one of the most promising photon detectors of the future in many Astronomical applications such as exoplanet detections. The MKID advantages stem from their single photon sensitivity (ranging from UV to optical and near infrared), photon energy resolution and high temporal capability (~microseconds). There has been substantial progress in the development of these detectors and MKIDs with Megapixel arrays is now possible. The unique capability of recording an incident photon and its energy (or wavelength) while also registering its time of arrival to within a microsecond enables an array of MKIDs to produce a four-dimensional data block of x, y, z and t comprising x, y spatial, z axis per pixel spectral and t axis per pixel which is temporal. This offers the possibility that the spectrum and brightness variation for any detected piece of space debris as a function of time might offer a unique identifier or fingerprint. Such a fingerprint signal from any object identified in multiple detections by different observers has the potential to determine the orbital features of the object and be used for their tracking. Modelling performed so far shows that with a 20 cm telescope located at an Astronomical observatory (e.g. La Palma, Canary Islands) we could detect sub cm objects at GEO. By considering a Lambertian sphere with a 10 % reflectivity (albedo of the Moon) we anticipate the following for a GEO object: 10 cm object imaged in a 1 second image capture; 1.2 cm object for a 70 second image integration or 0.65 cm object for a 4 minute image integration. We present details of our modelling and the potential instrument for a dedicated GEO surveillance system.

Keywords: space debris, orbital debris, detection system, observation, microwave kinetic inductance detectors, MKID

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Authors: Shuo Liu, Dan Liu

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In the field of named entity recognition, most of the research has been conducted around simple entities. However, for nested named entities, which still contain entities within entities, it has been difficult to identify them accurately due to their boundary ambiguity. In this paper, a hierarchical recognition model is constructed based on the grammatical structure and semantic features of Chinese text for boundary calculation based on lexical features. The analysis is carried out at different levels in terms of granularity, semantics, and lexicality, respectively, avoiding repetitive work to reduce computational effort and using the semantic features of words to calculate the boundaries of entities to improve the accuracy of the recognition work. The results of the experiments carried out on web-based microblogging data show that the model achieves an accuracy of 86.33% and an F1 value of 89.27% in recognizing nested named entities, making up for the shortcomings of some previous recognition models and improving the efficiency of recognition of nested named entities.

Keywords: coarse-grained, nested named entity, Chinese natural language processing, word embedding, T-SNE dimensionality reduction algorithm

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Authors: Hadas Sopher, Davide Schaumann, Yehuda E. Kalay

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This paper addresses the shortcomings of architectural computation tools in representing human behavior in built environments, prior to construction and occupancy of those environments. Evaluating whether a design fits the needs of its future users is currently done solely post construction, or is based on the knowledge and intuition of the designer. This issue is of high importance when designing complex buildings such as hospitals, where the quality of treatment as well as patient and staff satisfaction are of major concern. Existing computational pre-occupancy human behavior evaluation methods are geared mainly to test ergonomic issues, such as wheelchair accessibility, emergency egress, etc. As such, they rely on Agent Based Modeling (ABM) techniques, which emphasize the individual user. Yet we know that most human activities are social, and involve a number of actors working together, which ABM methods cannot handle. Therefore, we present an event-based model that manages the interaction between multiple Actors, Spaces, and Activities, to describe dynamically how people use spaces. This approach requires expanding the computational representation of Actors beyond their physical description, to include psychological, social, cultural, and other parameters. The model presented in this paper includes cognitive abilities and rules that describe the response of actors to their physical and social surroundings, based on the actors’ internal status. The model has been applied in a simulation of hospital wards, and showed adaptability to a wide variety of situated behaviors and interactions.

Keywords: agent based modeling, architectural design evaluation, event modeling, human behavior simulation, spatial cognition

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Authors: Rowane May A. Fesalbon, Greyland C. Agno, Jodel L. Cuasay, Dindo A. Malonzo, Ma. Rosario Concepcion O. Ang

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The Department of Energy of the Republic of the Philippines estimates that the country’s energy reserves for 2015 are dwindling– observed in the rotating power outages in several localities. To aid in the energy crisis, a national hydropower resource assessment scheme is developed. Hydropower is a resource that is derived from flowing water and difference in elevation. It is a renewable energy resource that is deemed abundant in the Philippines – being an archipelagic country that is rich in bodies of water and water resources. The objectives of this study is to develop a methodology for a national hydropower resource assessment using hydrologic modeling and geospatial techniques in order to generate resource maps for future reference and use of the government and other stakeholders. The methodology developed for this purpose is focused on two models – the implementation of the Soil and Water Assessment Tool (SWAT) for the river discharge and the use of geospatial techniques to analyze the topography and obtain the head, and generate the theoretical hydropower potential sites. The methodology is highly coupled with Geographic Information Systems to maximize the use of geodatabases and the spatial significance of the determined sites. The hydrologic model used in this workflow is SWAT integrated in the GIS software ArcGIS. The head is determined by a developed algorithm that utilizes a Synthetic Aperture Radar (SAR)-derived digital elevation model (DEM) which has a resolution of 10-meters. The initial results of the developed workflow indicate hydropower potential in the river reaches ranging from pico (less than 5 kW) to mini (1-3 MW) theoretical potential.

Keywords: ArcSWAT, renewable energy, hydrologic model, hydropower, GIS

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Authors: Freya Harding, Anne Gatuguta, Chi Eziefula

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Introduction Research shows the quality of experiences of pregnancy, birth, and postpartum impacts the health and well-being of the mother and baby. This is recognised by the WHO in their recommendations ‘Intrapartum care for a positive childbirth experience’. The COVID-19 pandemic saw the transformation of the NHS Maternity services to prevent the transmission of COVID-19. Physical and social isolation may have affected women’s experiences of pregnancy, birth and postpartum; especially those of healthcare. Examples of such changes made to the NHS include both the reduction in volume of face-to-face consultations and restrictions to visitor time in hospitals. One notable detriment due to these changes was the absence of a partner during certain stages of birth. The aim of this study was to explore women’s experiences of pregnancy, birth, and postnatal period during the COVID-19 pandemic in the UK. Methods We collected qualitative data from women who had given birth during the COVID-19 pandemic. In-depth, semi-structured interviews were conducted with twelve participants recruited from mother and baby groups in Southeast England. Data were audio-recorded, transcribed verbatim, and analysed thematically using both inductive and deductive approaches. Ethics permission was granted from Brighton and Sussex Medical School (ER/BSMS9A83/1). Results Interviews were conducted with 12 women who gave birth between May 2020 and February 2021. Ages of the participants ranged between 28 and 42 years, most of which were white British, with one being Asian British. All participants were heterosexual and either married or co-habiting with their partner. Five participants worked in the NHS, and all participants had professional occupations. Women felt inadequately supported both socially and medically. An appropriate sense of control over their own birthing experience was lacking. Safety mechanisms, such as in-person visits from the midwife, had no suitable alternatives in place. Serious health issues were able to “slip through the net.” Mental health conditions in some of those interviewed worsened or developed. Similarly, reduced support from partners during birth and during the immediate postpartum period at the hospital, coupled with reduced ward staffing, resulted in some traumatic experiences; particularly for women who had undergone caesarean section. However, some unexpected positive effects were reported; one example being that partners were able to spend more time with their baby due to furlough schemes and working from home. Similarly, emergency care was not felt to have been compromised. Overall, six themes emerged: (1) Self-reported traumatic experiences, (2) Challenges of caring for a baby with reduced medical and social support, (3) Unexpected benefits to the parenting experience, (4) The effects of a sudden change in medical management (5) Poor communication from healthcare professionals (6) Social change; with subthemes of support accessing medical care, the workplace, family and friends, and antenatal & baby groups. Conclusions The results indicate that the healthcare system was unable to adequately deliver maternity care to facilitate positive pregnancy, birth, and postnatal experiences during the heights of the pandemic. The poor quality of such experiences has been linked an increased risk of long-term health complications in both the mother and child.

Keywords: pregnancy, birth, postpartum, postnatal, COVID-19, maternity, social support, qualitative, pandemic

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Authors: Halima Kilungu, Pantaleo, K. T. Munishi

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Climate and land-cover change affect nature-based tourism (NBT) due to its attractions' close connection to natural environments and climate. Thus, knowledge of how each attraction reacts to the changing environments and devising simple yet science based approaches to respond to these changes from a tourism perspective in space and time is timely. Nevertheless, no specific approaches exist to address the knowledge gap. The eco-parcel approach is devised to address the gap and operationalized in Serengeti and Kilimanjaro National Parks: the most climate-sensitive NBT destinations in Africa. The approach is partly descriptive and has three simple steps: (1) to identify and define tourist attractions (i.e. biotic and abiotic attractions). This creates an important database of the most poorly kept information on attractions' types in NBT destinations. (2) To create a spatial and temporal link of each attraction and describe its characteristic environments (e.g. vegetation, soil, water and rock outcrops). This is the most limited attractions' information yet important as a proxy of changes in attractions. (3) To assess the importance of individual attractions for tourism based on tourists' preferences. This information enables an accurate assessment of the value of individual attractions for tourism. The importance of the eco-parcel approach is that it describes how each attraction emerges from and is connected to specific environments, which define its attractiveness in space and time. This information allows accurate assessment of the likely losses or gains of individual attractions when climate or environment changes in specific destinations and equips tourism stakeholders with informed responses.

Keywords: climate change, environmental change, nature-based tourism, Serengeti National Park, Kilimanjaro National Park

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Authors: Jerzy Banski

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Small towns represent a key element of settlement structure and serve a number of important functions associated with the servicing of rural areas that surround them. It is in light of this that scientific studies have paid considerable attention to the functional structure of centers of this kind, as well as the relationships with both surrounding rural areas and other urban centers. But a preliminary to such research has typically involved attempts at classifying the urban centers themselves, with this also assisting with the planning and shaping of development policy on different spatial scales. The purpose of the work is to test out the methods underpinning three different classifications of small urban centers, as well as to offer a preliminary interpretation of the outcomes obtained. Research took in 722 settlement units in Poland, granted town rights and populated by fewer than 20,000 inhabitants. A morphologically-based classification making reference to the database of topographic objects as regards land cover within the administrative boundaries of towns and cities was carried out, and it proved possible to distinguish the categories of “housing-estate”, industrial and R&R towns, as well as towns characterized by dichotomy. Equally, a functional/morphological approach taken with the same database allowed for the identification – via an alternative method – of three main categories of small towns (i.e., the monofunctional, multifunctional or oligo functional), which could then be described in far greater detail. A third, multi-criterion classification made simultaneous reference to the conditioning of a structural, a location-related, and an administrative hierarchy-related nature, allowing for distinctions to be drawn between small towns in 9 different categories. The results obtained allow for multifaceted analysis and interpretation of the geographical differentiation characterizing the distribution of Poland’s urban centers across space in the country.

Keywords: small towns, classification, local planning, Poland

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Andy Zhang, Awnik Roy, Trevor B. Chen, Bibik Oleksandar, Subodh Adhikari, Paul S. Hsu

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The durability of a combustor in gas-turbine engines is a strong function of its component temperatures and requires good control of these temperatures. Since the temperature of combustion gases frequently exceeds the melting point of the combustion liner walls, an efficient air-cooling system with optimized flow rates of cooling air is significantly important to elongate the lifetime of liner walls. To determine the effectiveness of the air-cooling system, accurate two-dimensional (2D) surface temperature measurement of combustor liner walls is crucial for advanced engine development. Traditional diagnostic techniques for temperature measurement in this application include the rmocouples, thermal wall paints, pyrometry, and phosphors. They have shown some disadvantages, including being intrusive and affecting local flame/flow dynamics, potential flame quenching, and physical damages to instrumentation due to harsh environments inside the combustor and strong optical interference from strong combustion emission in UV-Mid IR wavelength. To overcome these drawbacks, a compact and small borescope long-wave-infrared (LWIR) sensor is developed to achieve 2D high-spatial resolution, high-fidelity thermal imaging of 2D surface temperature in gas-turbine engines, providing the desired engine component temperature distribution. The compactLWIRborescope sensor makes it feasible to promote the durability of a combustor in gas-turbine engines and, furthermore, to develop more advanced gas-turbine engines.

Keywords: borescope, engine, low-wave-infrared, sensor

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Authors: Dimitrie Marinceu, Alan Murchison

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The Canadian Used Fuel Container (UFC) is a mid-size hemispherical headed copper coated steel container measuring 2.5 meters in length and 0.5 meters in diameter containing 48 used fuel bundles. The contained used fuel produces significant gamma radiation requiring automated assembly processes to complete the assembly. The design throughput of 2,500 UFCs per year places constraints on equipment and hot cell design for repeatability, speed of processing, robustness and recovery from upset conditions. After UFC assembly, the UFC is inserted into a Buffer Box (BB). The BB is made from adequately pre-shaped blocks (lower and upper block) and Highly Compacted Bentonite (HCB) material. The blocks are practically ‘sandwiching’ the UFC between them after assembly. This paper identifies one possible approach for the BB automatic assembly cell and processes. Automation of the BB assembly will have a significant positive impact on nuclear safety, quality, productivity, and reliability.

Keywords: used fuel packing plant, automatic assembly cell, used fuel container, buffer box, deep geological repository

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: K. S. Rao, Ankesh Kumar

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The search for alternative energy sources to petroleum has increased these days because of increase in need and depletion of petroleum reserves. Therefore the importance of oil shales as an economically viable substitute has increased many folds in last 20 years. The technologies like hydro-fracturing have opened the field of oil extraction from these unconventional rocks. Oil shale is a compact laminated rock of sedimentary origin containing organic matter known as kerogen which yields oil when distilled. Oil shales are formed from the contemporaneous deposition of fine grained mineral debris and organic degradation products derived from the breakdown of biota. Conditions required for the formation of oil shales include abundant organic productivity, early development of anaerobic conditions, and a lack of destructive organisms. These rocks are not gown through the high temperature and high pressure conditions in Mother Nature. The most common approach for oil extraction is drastically breaking the bond of the organics which involves retorting process. The two approaches for retorting are surface retorting and in-situ processing. The most environmental friendly approach for extraction is In-situ processing. The three steps involved in this process are fracturing, injection to achieve communication, and fluid migration at the underground location. Upon heating (retorting) oil shale at temperatures in the range of 300 to 400°C, the kerogen decomposes into oil, gas and residual carbon in a process referred to as pyrolysis. Therefore it is very important to understand the physico-mechenical behavior of such rocks, to improve the technology for in-situ extraction. It is clear from the past research and the physical observations that these rocks will behave as an anisotropic rock so it is very important to understand the mechanical behavior under high pressure at different orientation angles for the economical use of these resources. By knowing the engineering behavior under above conditions will allow us to simulate the deep ground retorting conditions numerically and experimentally. Many researchers have investigate the effect of organic content on the engineering behavior of oil shale but the coupled effect of organic and inorganic matrix is yet to be analyzed. The favourable characteristics of Assam coal for conversion to liquid fuels have been known for a long time. Studies have indicated that these coals and carbonaceous shale constitute the principal source rocks that have generated the hydrocarbons produced from the region. Rock cores of the representative samples are collected by performing on site drilling, as coring in laboratory is very difficult due to its highly anisotropic nature. Different tests are performed to understand the petrology of these samples, further the chemical analyses are also done to exactly quantify the organic content in these rocks. The mechanical properties of these rocks are investigated by considering different anisotropic angles. Now the results obtained from petrology and chemical analysis are correlated with the mechanical properties. These properties and correlations will further help in increasing the producibility of these rocks. It is well established that the organic content is negatively correlated to tensile strength, compressive strength and modulus of elasticity.

Keywords: oil shale, producibility, hydro-fracturing, kerogen, petrology, mechanical behavior

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Constantin Haita

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The chalcolithic tell settlements from south-eastern Romania, attributed to Gumelnița culture, are characterised by a well-defined surface, marked often by delimitation structures, a succession of many layers of construction, destruction, and rebuilding, and a well-structured area of occupation: built spaces, passage areas, waste zones. Settlements of tell type are located in the river valleys –on erosion remnants, alluvial bars or small islands, at the border of the valleys– on edges or prominences of Pleistocene terraces, lower Holocene terraces, and banks of lakes. This study integrates data on the geographical position, the morphological background, and the general stratigraphy of these important settlements. The correlation of the spatial distribution with the geomorphological units of each area of evolution creates an image of the natural landscape in which they occurred. The sedimentological researches achieved in the floodplain area of Balta Ialomiței showed important changes in the alluvial activity of Danube, after the Chalcolithic period (ca. 6500 - 6000 BP), to Iron Age and Middle Ages. The micromorphological analysis, consisting in thin section interpretation, at the microscopic scale, of sediments and soils in an undisturbed state, allowed the interpretation of the identified sedimentary facies, in terms of mode of formation and anthropic activities. Our studied cases reflect some distinct situations, correlating either with the geomorphological background or with the vertical development, the presence of delimiting structures and the internal organization. The characteristics of tells from this area bring significant information about the human habitation of Lower Danube in Prehistory.

Keywords: chalcolithic, micromorphology, Romania, sedimentology, tell settlements

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: Parisa Farzaneh, Azade Harati

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Mushrooms, or macro-fungi, as an important superfood, contain many bioactive compounds, particularly bio-peptides. In this research, mushroom proteins were extracted by buffer or buffer plus salt (0.15 M), along with an ultrasound bath to extract the intercellular protein. As a result, the highest amount of proteins in mushrooms were categorized into albumin. Proteins were also hydrolyzed and changed into peptides through endogenous and exogenous proteases, including gastrointestinal enzymes. The potency of endogenous proteases was also higher in Agaricus bisporus than Terfezia claveryi, as their activity ended at 75 for 15 min. The blanching process, endogenous enzymes, the mixture of gastrointestinal enzymes (pepsin-trypsin-α-chymotrypsin or trypsin- α-chymotrypsin) produced the different antioxidant and antibacterial hydrolysates. The peptide fractions produced with different cut-off ultrafilters also had various levels of radical scavenging, lipid peroxidation inhibition, and antibacterial activities. The bio-peptides with superior bioactivities (less than 3 kD of T. claveryi) were resistant to various environmental conditions (pH and temperatures). Therefore, they are good options to be added to nutraceutical and pharmaceutical preparations or functional foods, even during processing.

Keywords: bio-peptide, mushrooms, gastrointestinal enzymes, bioactivity

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Authors: Claus Bier, Dennis Binder, Alexander Gruenberger, Dagmar Drobietz, Dietrich Kohlheyer, Anita Loeschcke, Karl Erich Jaeger, Thomas Drepper, Joerg Pietruszka

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Light absorbing chromophoric systems are important optogenetic tools for biotechnical and biophysical investigations. Processes such as fluorescence or photolysis can be triggered by light-absorption of chromophores. These play a central role in life science. Photocaged compounds belong to such chromophoric systems. The photo-labile protecting groups enable them to release biologically active substances with high temporal and spatial resolution. The properties of photocaged compounds are specified by the characteristics of the caging group as well as the characteristics of the linked effector molecule. In our research, we work with different types of photo-labile protecting groups and various effector molecules giving us possible access to a large library of caged compounds. As a function of the caged effector molecule, a nearly limitless number of biological systems can be directed. Our main interest focusses on photocaging carbohydrates (e.g. arabinose) and their derivatives as effector molecules. Based on these resulting photocaged compounds a precisely controlled photoinduced gene expression will give us access to studies of numerous biotechnological and synthetic biological applications. It could be shown, that the regulation of gene expression via light is possible with photocaged carbohydrates achieving a higher-order control over this processes. With the one-step cleavable photocaged carbohydrate, a homogeneous expression was achieved in comparison to free carbohydrates.

Keywords: bacterial gene expression, biotechnology, caged compounds, carbohydrates, optogenetics, photo-removable protecting group

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Authors: V. Tabrizi, A. Vali, R. GHasemi, V. Behnamgol

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In this article, a nonlinear model of an under actuated six degrees of freedom (6 DOF) quadrotor UAV is derived on the basis of the Newton-Euler formula. The derivation comprises determining equations of the motion of the quadrotor in three dimensions and approximating the actuation forces through the modeling of aerodynamic coefficients and electric motor dynamics. The robust nonlinear control strategy includes a smooth second order non-singular terminal sliding mode control which is applied to stabilizing this model. The control method is on the basis of super twisting algorithm for removing the chattering and producing smooth control signal. Also, nonsingular terminal sliding mode idea is used for introducing a nonlinear sliding variable that guarantees the finite time convergence in sliding phase. Simulation results show that the proposed algorithm is robust against uncertainty or disturbance and guarantees a fast and precise control signal.

Keywords: quadrotor UAV, nonsingular terminal sliding mode, second order sliding mode t, electronics, control, signal processing

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Authors: Belgherbi Aicha, Bessaid Abdelhafid

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In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. The first and fundamental step in all these studies is the semi-automatic liver and spleen segmentation that is still an open problem. In this paper, a semi-automatic liver and spleen segmentation method by the mathematical morphology based on watershed algorithm has been proposed. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological to extract the liver and spleen. The second step consists to improve the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce the over-segmentation problem by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. The aim of this work is to develop a method for semi-automatic segmentation liver and spleen based on watershed algorithm, improve the accuracy and the robustness of the liver and spleen segmentation and evaluate a new semi-automatic approach with the manual for liver segmentation. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts. Liver segmentation has achieved the sensitivity and specificity; sens Liver=96% and specif Liver=99% respectively. Spleen segmentation achieves similar, promising results sens Spleen=95% and specif Spleen=99%.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Przemyslaw Nogly, Tobias Weinert, Daniel James, Sergio Carbajo, Dmitry Ozerov, Antonia Furrer, Dardan Gashi, Veniamin Borin, Petr Skopintsev, Kathrin Jaeger, Karol Nass, Petra Bath, Robert Bosman, Jason Koglin, Matthew Seaberg, Thomas Lane, Demet Kekilli, Steffen Brünle, Tomoyuki Tanaka, Wenting Wu, Christopher Milne, Thomas A. White, Anton Barty, Uwe Weierstall, Valerie Panneels, Eriko Nango, So Iwata, Mark Hunter, Igor Schapiro, Gebhard Schertler, Richard Neutze, Jörg Standfuss

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Ultrafast isomerization of retinal is the primary step in a range of photoresponsive biological functions including vision in humans and ion-transport across bacterial membranes. We studied the sub-picosecond structural dynamics of retinal isomerization in the light-driven proton pump bacteriorhodopsin using an X-ray laser. Twenty snapshots with near-atomic spatial and temporal resolution in the femtosecond regime show how the excited all-trans retinal samples conformational states within the protein binding pocket prior to passing through a highly-twisted geometry and emerging in the 13-cis conformation. The aspartic acid residues and functional water molecules in proximity of the retinal Schiff base respond collectively to formation and decay of the initial excited state and retinal isomerization. These observations reveal how the protein scaffold guides this remarkably efficient photochemical reaction.

Keywords: bacteriorhodopsin, free-electron laser, retinal isomerization mechanism, time-resolved crystallography

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Authors: Amara Rafik, Bougherara Maamar, Belhadj Aissa Mostefa

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Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques.

Keywords: ATM, GIS, radar data, air traffic simulation

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Authors: Jiahua Zhang, Qing Chang, Fengmei Yao

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Studying on the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics andthe adaptation of ecosystems to global change. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset and climate data were used to analyze the dynamics of grassland phenology as well as their correlation with climatic factors in different eco-geographic regions and elevation units across the Tibetan Plateau. The results showed that during 2003–2012, the start of the grassland greening season (SOS) appeared later while the end of the growing season (EOS) appeared earlier following the plateau’s precipitation and heat gradients from southeast to northwest. The multi-year mean value of SOS showed differences between various eco-geographic regions and was significantly impacted by average elevation and regional average precipitation during spring. Regional mean differences for EOS were mainly regulated by mean temperature during autumn. Changes in trends of SOS in the central and eastern eco-geographic regions were coupled to the mean temperature during spring, advancing by about 7d/°C. However, in the two southwestern eco-geographic regions, SOS was delayed significantly due to the impact of spring precipitation. The results also showed that the SOS occurred later with increasing elevation, as expected, with a delay rate of 0.66 d/100m. For 2003–2012, SOS showed an advancing trend in low-elevation areas, but a delayed trend in high-elevation areas, while EOS was delayed in low-elevation areas, but advanced in high-elevation areas. Grassland SOS and EOS changes may be influenced by a variety of other environmental factors in each eco-geographic region.

Keywords: grassland, phenology, MODIS, eco-geographic regions, elevation, climatic factors, Tibetan Plateau

Procedia PDF Downloads 322

Authors: Mehdi Moghimi

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Doctrines associated with situational prevention considers 'place of committing crime' as one of the fundamental elements of a crime. Meanwhile, with regard to causing or having effect on a crime situation, 'place' can be one of the pivotal indices in situational prevention analyses. This study aims at examining the role of place in construction of a crime situation and explaining the relationship between 'place' and situational preventive measures and procedures. Also, how to identify high-crime places, types of high-crime places and the factors influencing their creation are among the most important secondary objectives of this article. Concerning the purpose, it is a practical study whose material has been written through a documentary method using original sources (English), books and written and translated articles etc. This article is written in two main parts. In the first section, cognitive-conceptual issues about 'place' as one of the main causes of crime situation, and its effective interaction with situational preventive measures will be reviewed. The second part of this paper will focus on criminological examination of places and critical locations of crime and provide situational preventive measures to deal with the situation. 'Crime displacement' and 'geographical distribution of benefits'are also considered as the possible consequences of implementing preventive strategies. The results of the study suggest that the inventory of offenses is distributed according to the spatial characteristics. Moreover, according to the criminological characteristics governing region or location, offenders choose the place of crime based on a logical calculation. Therefore, some locations, regions or neighborhoods are permanent places of occurring lots of crimes. As a result, considering "place", preventive measures and procedures can be systematically directed, and using the most effective ways, limited preventive resources are utilized in the most critical places. Finally, some suggestions for further research and application are provided in line with more favorable promotion of situational preventive measures.

Keywords: crime prevention, place, police crime, situational crime prevention

Procedia PDF Downloads 516

Authors: Sung-Hyeon Park, Jin-Wook Jang, Hak-Sung Kim

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In recent years, Terahertz (THz) time-domain spectroscopy (TDS) imaging method has been received considerable interest as a promising non-destructive technique for detection of internal defects. In comparison to other non-destructive techniques such as x-ray inspection method, scanning acoustic tomograph (SAT) and microwave inspection method, THz-TDS imaging method has many advantages: First, it can measure the exact thickness and location of defects. Second, it doesn’t require the liquid couplant while it is very crucial to deliver that power of ultrasonic wave in SAT method. Third, it didn’t damage to materials and be harmful to human bodies while x-ray inspection method does. Finally, it exhibits better spatial resolution than microwave inspection method. However, this technology couldn’t be applied to IC package because THz radiation can penetrate through a wide variety of materials including polymers and ceramics except of metals. Therefore, it is difficult to detect the defects in IC package which are composed of not only epoxy and semiconductor materials but also various metals such as copper, aluminum and gold. In this work, we proposed a special method for detecting the void in the IC package using THz-TDS imaging system. The IC package specimens for this study are prepared by Packaging Engineering Team in Samsung Electronics. Our THz-TDS imaging system has a special reflection mode called pitch-catch mode which can change the incidence angle in the reflection mode from 10 o to 70 o while the others have transmission and the normal reflection mode or the reflection mode fixed at certain angle. Therefore, to find the voids in the IC package, we investigated the appropriate angle as changing the incidence angle of THz wave emitter and detector. As the results, the voids in the IC packages were successfully detected using our THz-TDS imaging system.

Keywords: terahertz, non-destructive technique, void, IC package

Procedia PDF Downloads 473

Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

Procedia PDF Downloads 164

Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

Procedia PDF Downloads 252

Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

Procedia PDF Downloads 173

Authors: Prafulla Kumar Choubey, Krishna Kishor Jha, S. B. Vaisakh Punnekkattu Chirayil

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In Graphics-display subsystem, frame buffers are shared between producer i.e. content rendering and consumer i.e. display. If a common buffer is operated by both producer and consumer simultaneously, their processing rates mismatch can cause tearing effect in displayed content. Therefore, Android OS employs triple buffered system, taking in to account an additional composition stage. Three stages-rendering, composition and display refresh, operate synchronously on three different buffers, which is achieved by using vsync pulses. This synchronization, however, brings in to the pipeline an additional latency of up to 26ms. The present study details about the existing synchronization mechanism of android graphics-display pipeline and discusses a new adaptive architecture which reduces the wait time to 5ms-16ms in all the use-cases. The proposed method uses two adaptive software vsyncs (PLL) for achieving the same result.

Keywords: Android graphics system, vertical synchronization, atrace, adaptive system

Procedia PDF Downloads 314

Authors: Regita P. Permata, Ulfa S. Nuraini

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Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies.

Keywords: Euclidean, heuristics, simulation, TSP

Procedia PDF Downloads 127