Search results for: Text Processing

Authors: Irene Perez-Alfaro, Nieves Murillo, Carlos Bernal, Daniel Gil-Hernandez

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Nowadays, the increasing applicability of internet of things (IoT) systems has changed the way that the world around is perceived. The massive interconnection of systems by means of sensing, processing and communication, allows multitude of data to be at our fingertips. In this way, countless advances have been made in different fields such as personal care, predictive maintenance in industry, quality control in production processes, security, and in everything imaginable. However, all these electronic systems have in common the need to be electrically powered. In this context, batteries and wires are the most commonly used solutions, but they are not a definitive solution in some applications, because of the attainability, the serviceability, or the performance requirements. Therefore, the need arises to look for other types of solutions based on energy harvesting and long-life electronics. Energy Harvesting can be defined as the action of capturing energy from the environment and store it for an instantaneous use or later use. Among the materials capable of harvesting energy from the environment, such as thermoelectrics, electromagnetics, photovoltaics or triboelectrics, the most suitable is the piezoelectric material. The phenomenon of piezoelectricity is one of the most powerful sources for energy harvesting, ranging from a few micro wats to hundreds of wats, depending on certain factors such as material type, geometry, excitation frequency, mechanical and electrical configurations, among others. In this research work, an exhaustive study is carried out on how different types of piezoelectric materials and electrical configurations influence the maximum power that a bimorph harvester is able to extract from mechanical vibrations. A series of experiments has been carried out in which the manufactured bimorph specimens are excited under fixed inertial vibrational conditions. In addition, in order to evaluate the dependence of the maximum transferred power, different load resistors are tested. In this way, the pure active power that achieves the maximum power transfer can be approximated. In this paper, we present the design of low-cost energy harvesting solutions based on piezoelectric smart materials with tunable frequency. The results obtained show the differences in energy extraction between the PZT materials studied and their electrical configurations. The aim of this work is to gain a better understanding of the behavior of piezoelectric materials, and the design process of bimorph PZT harvesters to optimize environmental energy extraction.

Keywords: Bimorph harvesters, electrical impedance, energy harvesting, piezoelectric, smart material.

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Authors: Siddhartha Bhattacharyya, Paramartha Dutta, Ujjwal Maulik, Prashanta Kumar Nandi

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A multilayer self organizing neural neural network (MLSONN) architecture for binary object extraction, guided by a beta activation function and characterized by backpropagation of errors estimated from the linear indices of fuzziness of the network output states, is discussed. Since the MLSONN architecture is designed to operate in a single point fixed/uniform thresholding scenario, it does not take into cognizance the heterogeneity of image information in the extraction process. The performance of the MLSONN architecture with representative values of the threshold parameters of the beta activation function employed is also studied. A three layer bidirectional self organizing neural network (BDSONN) architecture comprising fully connected neurons, for the extraction of objects from a noisy background and capable of incorporating the underlying image context heterogeneity through variable and adaptive thresholding, is proposed in this article. The input layer of the network architecture represents the fuzzy membership information of the image scene to be extracted. The second layer (the intermediate layer) and the final layer (the output layer) of the network architecture deal with the self supervised object extraction task by bi-directional propagation of the network states. Each layer except the output layer is connected to the next layer following a neighborhood based topology. The output layer neurons are in turn, connected to the intermediate layer following similar topology, thus forming a counter-propagating architecture with the intermediate layer. The novelty of the proposed architecture is that the assignment/updating of the inter-layer connection weights are done using the relative fuzzy membership values at the constituent neurons in the different network layers. Another interesting feature of the network lies in the fact that the processing capabilities of the intermediate and the output layer neurons are guided by a beta activation function, which uses image context sensitive adaptive thresholding arising out of the fuzzy cardinality estimates of the different network neighborhood fuzzy subsets, rather than resorting to fixed and single point thresholding. An application of the proposed architecture for object extraction is demonstrated using a synthetic and a real life image. The extraction efficiency of the proposed network architecture is evaluated by a proposed system transfer index characteristic of the network.

Keywords: Beta activation function, fuzzy cardinality, multilayer self organizing neural network, object extraction,

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Authors: Zh. Karaulova, D. Baizhigitov

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At the Aktogay deposit, the oxidized ore section has been developed since 2015; by now, the reserves of easily enriched ore are decreasing, and a large number of copper-poor, difficult-to-enrich ores has been accumulated in the dumps of the KAZ Minerals Aktogay deposit, which is unprofitable to mine using the traditional mining methods. Hence, another technology needs to be implemented, which will significantly expand the raw material base of copper production in Kazakhstan and ensure the efficient use of natural resources. Heap and dump bacterial recovery are the most acceptable technologies for processing low-grade secondary copper sulfide ores. Test objects were the copper ores of Aktogay deposit and chemolithotrophic bacteria Leptospirillum ferrooxidans (L.f.), Acidithiobacillus caldus (A.c.), Sulfobacillus acidophilus (S.a.), represent mixed cultures utilized in bacterial oxidation systems. They can stay active in the 20-40 °C temperature range. Biocatalytic acceleration was achieved as a result of bacteria oxidizing iron sulfides to form iron sulfate, which subsequently underwent chemical oxidation to become sulfate oxide. The following results have been achieved at the initial stage: the goal was to grow and maintain the life activity of bacterial cultures under laboratory conditions. These bacteria grew the best within the pH 1,2-1,8 range with light stirring and in an aerated environment. The optimal growth temperature was 30-33 оC. The growth rate decreased by one-half for each 4-5 °C fall in temperature from 30 °C. At best, the number of bacteria doubled every 24 hours. Typically, the maximum concentration of cells that can be grown in ferrous solution is about 107/ml. A further step researched in this case was the adaptation of microorganisms to the environment of certain metals. This was followed by mass production of inoculum and maintenance for their further cultivation on a factory scale. This was done by adding sulfide concentrate, allowing the bacteria to convert the ferrous sulfate as indicated by the Eh (> 600 mV), then diluting to double the volume and adding concentrate to achieve the same metal level. This process was repeated until the desired metal level and volumes were achieved. The final stage of bacterial recovery was the transportation and irrigation of secondary sulfide copper ores of the oxidized ore section. In conclusion, the project was implemented at the Aktogay mine since the bioleaching process was prolonged. Besides, the method of bacterial recovery might compete well with existing non-biological methods of extraction of metals from ores.

Keywords: Bacterial recovery, copper ore, bioleaching, bacterial inoculum.

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Authors: Marine Shavlakadze

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Hydroponic greenhouses systems (production of the artificial substrate without soil) are becoming popular in the world. Mostly the system is used to grow vegetables and berries. Different countries are taking action to participate in the development of hydroponic technology and solutions such as EU members, Turkey, Australia, New Zealand, Israel, Scandinavian countries, etc. Many vegetables and berries are grown by hydroponics in Europe. As a result of our research, we have obtained material containing manganese and nitrogen. It became possible to produce this fertilizer by means of one-stage thermal processing, using industrial waste containing manganese (ores and sludges) and mineral substance (ammonium nitrate) that exist in Georgia. The received material is usable as a micro-fertilizer with economic efficiency. It became possible to turn practically water-insoluble manganese dioxide substance into the soluble condition from industrial waste in an indirect way. The ability to use the material as a fertilizer is predetermined by its chemical and phase composition, as the amount of the active component of the material in relation to manganese is 30%. At the same time, the active component elements presented non-ballast sustained action compounds. The studies implemented in Poland and in Georgia by us have shown that the manganese-containing micro-fertilizer- Mn(NO₃)₂ can provide the plant with nitrate nitrogen, which is a form that can be used for plants, providing the economy and simplicity of the application of fertilizers. Given the fact that the application of the manganese-containing micro-fertilizers significantly increases the productivity and improves the quality of the big number of agricultural products, it is necessary to mention that it is recommended to introduce the manganese containing fertilizers into the following cultures: sugar beet, corn, potato, vegetables, vine grape, fruit, berries, and other cultures. Also, as a result of the study, it was established that the material obtained is the predominant fertilizer for vegetable cultures in the soil. Based on the positive results of the research, we consider it expedient to conduct research in hydroponic systems, which will enable us to provide plants the required amount of manganese; we also introduce nitrogen in solution and regulate the solution of pH, which is one of the main problems in hydroponic production. The findings of our research will be used in hydroponic greenhouse farms to increase the fertility of vegetable crops and, consequently, to get bountiful and high-quality harvests, which will promote the development of hydroponic greenhouses in Georgia as well as abroad.

Keywords: Hydroponics, micro-fertilizers, manganese ore, chemical amelioration.

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Authors: Jamalodin Alvani, Fardin Boustani, Omid Tabiee, Masoud Hashemi

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The Yasuj city stream named the Beshar supply water for different usages such as aquaculture farms , drinking, agricultural and industrial usages. Fish processing plants ,Agricultural farms, waste water of industrial zones and hospitals waste water which they are generate by human activity produce a considerable volume of effluent and when they are released in to the stream they can effect on the water quality and down stream aquatic systems. This study was conducted to evaluate the effects of outflow effluent from different human activity and point and non point pollution sources on the water quality and health of the Beshar river next to Yasuj. Yasuj is the biggest and most important city in the Kohkiloye and Boyerahmad province . The Beshar River is one of the most important aquatic ecosystems in the upstream of the Karun watershed in south of Iran which is affected by point and non point pollutant sources . This study was done in order to evaluate the effects of human activities on the water quality and health of the Beshar river. This river is approximately 190 km in length and situated at the geographical positions of 51° 20' to 51° 48' E and 30° 18' to 30° 52' N it is one of the most important aquatic ecosystems of Kohkiloye and Boyerahmad province in south-west Iran. In this research project, five study stations were selected to examine water pollution in the Beshar River systems. Human activity is now one of the most important factors affecting on hydrology and water quality of the Beshar river. Humans use large amounts of resources to sustain various standards of living, although measures of sustainability are highly variable depending on how sustainability is defined. The Beshar river ecosystems are particularly sensitive and vulnerable to human activities. The water samples were analyzed, then some important water quality parameters such as pH, dissolve oxygen (DO), Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Total Suspended Solids (TDS),Turbidity, Temperature, Nitrates (NO3) and Phosphates (PO4) were estimated at the two stations. The results show a downward trend in the water quality at the down stream of the city. The amounts of BOD5,COD,TSS,T,Turbidity, NO3 and PO4 in the down stream stations were considerably more than the station 1. By contrast the amounts of DO in the down stream stations were less than to the station 1. However when effluent discharge consequence of human activities are released into the Beshar river near the city, the quality of river are decreases and the environmental problems of the river during the next years are predicted to rise.

Keywords: Health, Human activities, Water pollution, Yasuj , Iran

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Authors: Elif Gencturk, Ekin Yurdakul, Ahmet Y. Celik, Senol Mutlu, Kutlu O. Ulgen

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Yeast cells are generally used as a model system of eukaryotes due to their complex genetic structure, rapid growth ability in optimum conditions, easy replication and well-defined genetic system properties. Thus, yeast cells increased the knowledge of the principal pathways in humans. During fermentation, carbohydrates (hexoses and pentoses) degrade into some toxic by-products such as 5-hydroxymethylfurfural (5-HMF or HMF) and furfural. HMF influences the ethanol yield, and ethanol productivity; it interferes with microbial growth and is considered as a potent inhibitor of bioethanol production. In this study, yeast single cell behavior under HMF application was monitored by using a continuous flow single phase microfluidic platform. Microfluidic device in operation is fabricated by hot embossing and thermo-compression techniques from cyclo-olefin polymer (COP). COP is biocompatible, transparent and rigid material and it is suitable for observing fluorescence of cells considering its low auto-fluorescence characteristic. The response of yeast cells was recorded through Red Fluorescent Protein (RFP) tagged Nop56 gene product, which is an essential evolutionary-conserved nucleolar protein, and also a member of the box C/D snoRNP complexes. With the application of HMF, yeast cell proliferation continued but HMF slowed down the cell growth, and after HMF treatment the cell proliferation stopped. By the addition of fresh nutrient medium, the yeast cells recovered after 6 hours of HMF exposure. Thus, HMF application suppresses normal functioning of cell cycle but it does not cause cells to die. The monitoring of Nop56 expression phases of the individual cells shed light on the protein and ribosome synthesis cycles along with their link to growth. Further computational study revealed that the mechanisms underlying the inhibitory or inductive effects of HMF on growth are enriched in functional categories of protein degradation, protein processing, DNA repair and multidrug resistance. The present microfluidic device can successfully be used for studying the effects of inhibitory agents on growth by single cell tracking, thus capturing cell to cell variations. By metabolic engineering techniques, engineered strains can be developed, and the metabolic network of the microorganism can thus be manipulated such that chemical overproduction of target metabolite is achieved along with the maximum growth/biomass yield.  

Keywords: COP, HMF, ribosome biogenesis, thermoplastic microbioreactor, yeast.

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Authors: Jung-Hong Hong, Jing-Cen Yang, Cai-Yu Ou

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The analysis of geographic inequality heavily relies on the use of location-enabled statistical data and quantitative measures to present the spatial patterns of the selected phenomena and analyze their differences. To protect the privacy of individual instance and link to administrative units, point-based datasets are spatially aggregated to area-based statistical datasets, where only the overall status for the selected levels of spatial units is used for decision making. The partition of the spatial units thus has dominant influence on the outcomes of the analyzed results, well known as the Modifiable Areal Unit Problem (MAUP). A new spatial reference framework, the Taiwan Geographical Statistical Classification (TGSC), was recently introduced in Taiwan based on the spatial partition principles of homogeneous consideration of the number of population and households. Comparing to the outcomes of the traditional township units, TGSC provides additional levels of spatial units with finer granularity for presenting spatial phenomena and enables domain experts to select appropriate dissemination level for publishing statistical data. This paper compares the results of respectively using TGSC and township unit on the mortality data and examines the spatial characteristics of their outcomes. For the mortality data between the period of January 1^st, 2008 and December 31^st, 2010 of the Taitung County, the all-cause age-standardized death rate (ASDR) ranges from 571 to 1757 per 100,000 persons, whereas the 2^nd dissemination area (TGSC) shows greater variation, ranged from 0 to 2222 per 100,000. The finer granularity of spatial units of TGSC clearly provides better outcomes for identifying and evaluating the geographic inequality and can be further analyzed with the statistical measures from other perspectives (e.g., population, area, environment.). The management and analysis of the statistical data referring to the TGSC in this research is strongly supported by the use of Geographic Information System (GIS) technology. An integrated workflow that consists of the tasks of the processing of death certificates, the geocoding of street address, the quality assurance of geocoded results, the automatic calculation of statistic measures, the standardized encoding of measures and the geo-visualization of statistical outcomes is developed. This paper also introduces a set of auxiliary measures from a geographic distribution perspective to further examine the hidden spatial characteristics of mortality data and justify the analyzed results. With the common statistical area framework like TGSC, the preliminary results demonstrate promising potential for developing a web-based statistical service that can effectively access domain statistical data and present the analyzed outcomes in meaningful ways to avoid wrong decision making.

Keywords: Mortality map, spatial patterns, statistical area, variation.

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Authors: Isabella Bianco, Gian Andrea Blengini

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The environmental impact related to ornamental stones (such as marbles and granites) is largely debated. Starting from the industrial revolution, continuous improvements of machineries led to a higher exploitation of this natural resource and to a more international interaction between markets. As a consequence, the environmental impact of the extraction and processing of stones has increased. Nevertheless, if compared with other building materials, ornamental stones are generally more durable, natural, and recyclable. From the scientific point of view, studies on stone life cycle sustainability have been carried out, but these are often partial or not very significant because of the high percentage of approximations and assumptions in calculations. This is due to the lack, in life cycle databases (e.g. Ecoinvent, Thinkstep, and ELCD), of datasets about the specific technologies employed in the stone production chain. For example, databases do not contain information about diamond wires, chains or explosives, materials commonly used in quarries and transformation plants. The project presented in this paper aims to populate the life cycle databases with specific data of specific stone processes. To this goal, the methodology follows the standardized approach of Life Cycle Assessment (LCA), according to the requirements of UNI 14040-14044 and to the International Reference Life Cycle Data System (ILCD) Handbook guidelines of the European Commission. The study analyses the processes of the entire production chain (from-cradle-to-gate system boundaries), including the extraction of benches, the cutting of blocks into slabs/tiles and the surface finishing. Primary data have been collected in Italian quarries and transformation plants which use technologies representative of the current state-of-the-art. Since the technologies vary according to the hardness of the stone, the case studies comprehend both soft stones (marbles) and hard stones (gneiss). In particular, data about energy, materials and emissions were collected in marble basins of Carrara and in Beola and Serizzo basins located in the province of Verbano Cusio Ossola. Data were then elaborated through an appropriate software to build a life cycle model. The model was realized setting free parameters that allow an easy adaptation to specific productions. Through this model, the study aims to boost the direct participation of stone companies and encourage the use of LCA tool to assess and improve the stone sector environmental sustainability. At the same time, the realization of accurate Life Cycle Inventory data aims at making available, to researchers and stone experts, ILCD compliant datasets of the most significant processes and technologies related to the ornamental stone sector.

Keywords: LCA datasets, life cycle assessment, ornamental stone, stone environmental impact.

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Authors: U. K. Sahin, N. Erdumlu, C. Saricam, I. Gocek, M. H. Arslan, H. Acikgoz-Tufan, B. Kalav

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Finishing is an important part of fabric processing with which a wide range of features are imparted to greige or colored fabrics for various end-uses. Especially, by the addition or impartation of nano-scaled particles to the fabric structure composite fabrics, a kind of composite materials can be acquired. Composite materials, generally shortened as composites or in other words composition materials, are engineered or naturally occurring materials made from two or more component materials with significantly different physical, mechanical or chemical characteristics remaining separate and distinctive at the macroscopic or microscopic scale within the end product structure. Therefore, the technique finishing which is one of the fundamental methods to be applied on fabrics for obtainment of composite fabrics with many functionalities was used in the current study with the same purpose. However, regardless of the finishing materials applied, the efficient life of finished product on offering desired feature is low, since the durability of finishes on the material is limited. Any increase in durability of these finishes on textiles would enhance the life of use for textiles, which will result in happier users. Therefore, in this study, since higher durability was desired for the finishing materials fixed on the fabrics, nano-scaled hollow structured cyclodextrins were chemically imparted by grafting to the structure of conventional cotton fabrics by the help of finishing technique in order to be fixed permanently. By this way, a processed and functionalized base fabric having potential to be treated in the subsequent processes with many different finishing agents and nanomaterials could be obtained. Henceforth, this fabric can be used as a multi-functional fabric due to the encapturing ability of cyclodextrins to molecules/particles via physical/chemical means. In this study, scoured and rinsed woven bleached plain weave 100% cotton fabrics were utilized because textiles made of cotton are the most demanded textile products in the textile market by the textile consumers in daily life. Cotton fabric samples were immersed in treating baths containing β-cyclodextrin and 1,2,3,4-butanetetracarboxylic acid and to reduce the curing temperature the catalyst sodium hypophosphite monohydrate was used. All impregnated fabric samples were pre-dried. The reaction of grafting was performed in dry state. The treated and cured fabric samples were rinsed with warm distilled water and dried. The samples were dried for 4 h and weighed before and after finishing and rinsing. Stability and durability of β-cyclodextrins on fabric surface against external factors such as washing as well as strength of functionalized fabric in terms of tensile and tear strength were tested. Presence and homogeneity of distribution of β-cyclodextrins on fabric surface were characterized.

Keywords: Cotton fabric, cyclodextrin, improved durability, multifunctional composite textile.

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Authors: M. I. Ribah, M. Jibir, Y. A. Bashar, S. S. Manga

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Food safety is a significant and growing public health problem in the world and Nigeria as a developing country, since food-borne diseases are important contributors to the huge burden of sickness and death of humans. In Nigeria, traditional ready-to-eat meat products (RTE-MPs) like balangu, tsire, guru and dried meat products like kilishi, dambun nama, banda, were reported to be highly appreciated because of their eating qualities. The consumption of these products was considered as safe due to the treatments that are usually involved during their production process. However, during processing and handling, the products could be contaminated by pathogens that could cause food poisoning. Therefore, a hazard identification for pathogenic bacteria on some traditional RTE-MPs was conducted in Kebbi and Sokoto States, Nigeria. A total of 116 RTE-MPs (balangu-38, kilishi-39 and tsire-39) samples were obtained from retail outlets and analyzed using standard cultural microbiological procedures in general and selective enrichment media to isolate the target pathogens. A six-fold serial dilution was prepared and using the pour plating method, colonies were counted. Serial dilutions were selected based on the prepared pre-labeled Petri dishes for each sample. A volume of 10-12 ml of molten Nutrient agar cooled to 42-45°C was poured into each Petri dish and 1 ml each from dilutions of 102, 104 and 106 for every sample was respectively poured on a pre-labeled Petri plate after which colonies were counted. The isolated pathogens were identified and confirmed after series of biochemical tests. Frequencies and percentages were used to describe the presence of pathogens. The General Linear Model was used to analyze data on pathogen presence according to RTE-MPs and means were separated using the Tukey test at 0.05 confidence level. Of the 116 RTE-MPs samples collected, 35 (30.17%) samples were found to be contaminated with some tested pathogens. Prevalence results showed that Escherichia coli, salmonella and Staphylococcus aureus were present in the samples. Mean total bacterial count was 23.82×106 cfu/g. The frequency of individual pathogens isolated was; Staphylococcus aureus 18 (15.51%), Escherichia coli 12 (10.34%) and Salmonella 5 (4.31%). Also, among the RTE-MPs tested, the total bacterial counts were found to differ significantly (P < 0.05), with 1.81, 2.41 and 2.9×104 cfu/g for tsire, kilishi, and balangu, respectively. The study concluded that the presence of pathogenic bacteria in balangu could pose grave health risks to consumers, and hence, recommended good manufacturing practices in the production of balangu to improve the products’ safety.

Keywords: Ready-to-eat meat products, retail outlets, safety assessment, public health.

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: Edge network, embedded network, MMA, matrix multiplication accelerator and semantic segmentation network.

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Authors: R. Tamborrino, F. Rinaudo

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Digital technologies offer possibilities to effectively convert historical archives into instruments of knowledge able to provide a guide for the interpretation of historical phenomena. Digital conversion and management of those documents allow the possibility to add other sources in a unique and coherent model that permits the intersection of different data able to open new interpretations and understandings. Urban history uses, among other sources, the inventories that register human activities in a specific space (e.g. cadastres, censuses, etc.). The geographic localisation of that information inside cartographic supports allows for the comprehension and visualisation of specific relationships between different historical realities registering both the urban space and the peoples living there. These links that merge the different nature of data and documentation through a new organisation of the information can suggest a new interpretation of other related events. In all these kinds of analysis, the use of GIS platforms today represents the most appropriate answer. The design of the related databases is the key to realise the ad-hoc instrument to facilitate the analysis and the intersection of data of different origins. Moreover, GIS has become the digital platform where it is possible to add other kinds of data visualisation. This research deals with the industrial development of Turin at the beginning of the 20th century. A census of factories realized just prior to WWI provides the opportunity to test the potentialities of GIS platforms for the analysis of urban landscape modifications during the first industrial development of the town. The inventory includes data about location, activities, and people. GIS is shaped in a creative way linking different sources and digital systems aiming to create a new type of platform conceived as an interface integrating different kinds of data visualisation. The data processing allows linking this information to an urban space, and also visualising the growth of the city at that time. The sources, related to the urban landscape development in that period, are of a different nature. The emerging necessity to build, enlarge, modify and join different buildings to boost the industrial activities, according to their fast development, is recorded by different official permissions delivered by the municipality and now stored in the Historical Archive of the Municipality of Turin. Those documents, which are reports and drawings, contain numerous data on the buildings themselves, including the block where the plot is located, the district, and the people involved such as the owner, the investor, and the engineer or architect designing the industrial building. All these collected data offer the possibility to firstly re-build the process of change of the urban landscape by using GIS and 3D modelling technologies thanks to the access to the drawings (2D plans, sections and elevations) that show the previous and the planned situation. Furthermore, they access information for different queries of the linked dataset that could be useful for different research and targets such as economics, biographical, architectural, or demographical. By superimposing a layer of the present city, the past meets to the present-industrial heritage, and people meet urban history.

Keywords: Digital urban history, census, digitalisation, GIS, modelling, digital humanities.

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Authors: Dinara F. Gaynutdinova, Vladimir Ya Modorsky, Nikolay A. Shevelev

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The problem under research is that of unpredictable modes occurring in two-stage centrifugal hydraulic pump as a result of hydraulic processes caused by vibrations of structural components. Numerical, analytical and experimental approaches are considered. A hypothesis was developed that the problem of unpredictable pressure decrease at the second stage of centrifugal pumps is caused by cavitation effects occurring upon vibration. The problem has been studied experimentally and theoretically as of today. The theoretical study was conducted numerically and analytically. Hydroelastic processes in dynamic “liquid – deformed structure” system were numerically modelled and analysed. Using ANSYS CFX program engineering analysis complex and computing capacity of a supercomputer the cavitation parameters were established to depend on vibration parameters. An influence domain of amplitudes and vibration frequencies on concentration of cavitation bubbles was formulated. The obtained numerical solution was verified using CFM program package developed in PNRPU. The package is based on a differential equation system in hyperbolic and elliptic partial derivatives. The system is solved by using one of finite-difference method options – the particle-in-cell method. The method defines the problem solution algorithm. The obtained numerical solution was verified analytically by model problem calculations with the use of known analytical solutions of in-pipe piston movement and cantilever rod end face impact. An infrastructure consisting of an experimental fast hydro-dynamic processes research installation and a supercomputer connected by a high-speed network, was created to verify the obtained numerical solutions. Physical experiments included measurement, record, processing and analysis of data for fast processes research by using National Instrument signals measurement system and Lab View software. The model chamber end face oscillated during physical experiments and, thus, loaded the hydraulic volume. The loading frequency varied from 0 to 5 kHz. The length of the operating chamber varied from 0.4 to 1.0 m. Additional loads weighed from 2 to 10 kg. The liquid column varied from 0.4 to 1 m high. Liquid pressure history was registered. The experiment showed dependence of forced system oscillation amplitude on loading frequency at various values: operating chamber geometrical dimensions, liquid column height and structure weight. Maximum pressure oscillation (in the basic variant) amplitudes were discovered at loading frequencies of approximately 1,5 kHz. These results match the analytical and numerical solutions in ANSYS and CFM.

Keywords: Computing experiment, hydroelasticity, physical experiment, vibration.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

Abstract:

We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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