Search results for: sustainable and just food systems

Authors: Maxime A. J. Kuitche, Ruxandra M. Botez, Arthur Guillemin

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As the Unmanned Aerial Systems have found diverse utilities in both military and civil aviation, the necessity to obtain an accurate aerodynamic model has shown an enormous growth of interest. Recent modeling techniques are procedures using optimization algorithms and statistics that require many flight tests and are therefore extremely demanding in terms of costs. This paper presents a procedure to estimate the aerodynamic behavior of an unmanned aerial system from a numerical approach using computational fluid dynamic analysis. The study was performed using an unstructured mesh obtained from a grid convergence analysis at a Mach number of 0.14, and at an angle of attack of 0°. The flow around the aircraft was described using a standard k-ω turbulence model. Thus, the Reynold Averaged Navier-Stokes (RANS) equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45 designed and manufactured by Hydra Technologies in Mexico. The lift, the drag, and the pitching moment coefficients were obtained at different angles of attack for several flight conditions defined in terms of altitudes and Mach numbers. The results obtained from the Computational Fluid Dynamics analysis were compared with the results obtained by using the DATCOM semi-empirical procedure. This comparison has indicated that our approach is highly accurate and that the aerodynamic model obtained could be useful to estimate the flight dynamics of the UAS-S45.

Keywords: aerodynamic modelling, CFD Analysis, ANSYS FLUENT, UAS-S45

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Ts. Lazarova, V. Tumbalev, S. Atanacova-Vladimirova, G. Ivanov, A. Naydenov, D. Kovacheva

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Methane is a major Greenhouse Gas (GHG) that accounts for 14% of the world’s total amount of GHG emissions, originating mainly from agriculture, Coal mines, land fields, wastewater and oil and gas facilities. Nowadays the problem caused by the methane emissions has been a subject of an increased concern. One of the methods for neutralization of the methane emissions is it's complete catalytic oxidation. The efforts of the researchers are focused on the development of new types of catalysts and optimizing the existing catalytic systems in order to prevent the sintering of the palladium, providing at the same time a sufficient activity at temperatures below 500oC. The aim of the present work is to prepare mixed Cu-Ag-Pd oxide catalysts supported on alumina and to test them for methane complete catalytic oxidation. Cu-Ag-Pd/Al2O3 were prepared on a γ-Al2O3 (BET surface area = 220 m2/g) by the incipient wetness method using the corresponding metal nitrates (Cu:Ag = 90:10, Cu:Pd =97:3, Cu:Ag:Pd= 87:10:3) as precursors. A second set of samples were prepared with addition of urea to the metal nitrate solutions with the above mentioned ratios assuming increased dispersivity of the catalysts. The catalyst samples were dried at 100°C for 3 hours and calcined at 550°C for 30 minutes. Catalysts samples were characterized using X-ray diffraction (XRD), low temperature adsorption of nitrogen (BET) and scanning electron microscopy (SEM). The catalytic activity tests were carried out in a continuous flow type of reactor at atmospheric pressure. The effect of catalyst aging at 500 oC for 120 h on the methane combustion activity was also investigated. The results clearly indicate the synergetic effect of Ag and Pd on the catalytic activity.

Keywords: catalysts, XRD, BET, SEM, catalytic oxidation

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Authors: Shlomit Paz, Meir Broza

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Cholera is an acute intestinal infection caused by ingestion of food or water contaminated with the bacterium Vibrio cholerae. It has a short incubation period and produces an enterotoxin that causes copious, painless, watery diarrhoea that can quickly lead to severe dehydration and death if treatment is not promptly given. In an epidemic, the source of the contamination is usually the feces of an infected person. The disease can spread rapidly in areas with poor treatment of sewage and drinking water. Cholera remains a global threat and is one of the key indicators of social development. An estimated 3-5 million cases and over 100,000 deaths occur each year around the world. The relevance of climatic events as causative factors for cholera epidemics is well known. However, the examination of the involvement of winds in intra-continental disease distribution is new. The study explore the hypothesis that the spreading of cholera epidemics may be related to the dominant wind direction over land by presenting the influence of the wind direction on windborn dissemination by flying insects, which may serve as vectors. Chironomids ("non-biting midges“) exist in the majority of freshwater aquatic habitats, especially in estuarine and organic-rich water bodies typical to Vibrio cholerae. Chironomid adults emerge into the air for mating and dispersion. They are highly mobile, huge in number and found frequently in the air at various elevations. The huge number of chironomid egg masses attached to hard substrate on the water surface, serve as a reservoir for the free-living Vibrio bacteria. Both male and female, while emerging from the water, may carry the cholera bacteria. In experimental simulation, it was demonstrated that the cholera-bearing adult midges are carried by the wind, and transmit the bacteria from one body of water to another. In our previous study, the geographic diffusions of three cholera outbreaks were examined through their linkage with the wind direction: a) the progress of Vibrio cholerae O1 biotype El Tor in Africa during 1970–1971 and b) again in 2005–2006; and c) the rapid spread of Vibrio cholerae O139 over India during 1992–1993. Using data and map of cholera dissemination (WHO database) and mean monthly SLP and geopotential data (NOAA NCEP-NCAR database), analysis of air pressure data at sea level and at several altitudes over Africa, India and Bangladesh show a correspondence between the dominant wind direction and the intra-continental spread of cholera. The results support the hypothesis that aeroplankton (the tiny life forms that float in the air and that may be caught and carried upward by the wind, landing far from their origin) carry the cholera bacteria from one body of water to an adjacent one. In addition to these findings, the current follow-up study will present new results regarding the possible involvement of winds in the spreading of cholera in recent outbreaks (2010-2013). The findings may improve the understanding of how climatic factors are involved in the rapid distribution of new strains throughout a vast continental area. Awareness of the aerial transfer of Vibrio cholerae may assist health authorities by improving the prediction of the disease’s geographic dissemination.

Keywords: cholera, Vibrio cholerae, wind direction, Vibrio cholerae dissemination

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Authors: Jaime E. Munoz, Jose C. Arcos, Oscar E. Bautista, Ivan E. Campos

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The influence of slippage phenomenon over the destabilization and atomization mechanisms induced via surface acoustic waves on a Newtonian, millimeter-sized, drop deposited on a hydrophilic substrate is studied theoretically. By implementing the Navier-slip model and a lubrication-type approach into the equations which govern the dynamic response of a drop exposed to acoustic stress, a highly nonlinear evolution equation for the air-liquid interface is derived in terms of the acoustic capillary number and the slip coefficient. By numerically solving such an evolution equation, the Spatio-temporal deformation of the drop's free surface is obtained; in this context, atomization of the initial drop into micron-sized droplets is predicted at our numerical model once the acoustically-driven capillary waves reach a critical value: the instability length. Our results show slippage phenomenon at systems with partial and complete wetting favors the formation of capillary waves at the free surface, which traduces in a major volume of liquid being atomized in comparison to the no-slip case for a given time interval. In consequence, slippage at the wall possesses the capability to affect and improve the atomization rate for a drop exposed to a high-frequency acoustic field.

Keywords: capillary instability, lubrication theory, navier-slip condition, SAW atomization

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Authors: Masahiro Miyaji

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When a driver happens to be involved in some traffic congestion or after traffic incidents, the driver may fall in a state of anger. State of anger may encounter decisive risk resulting in severer traffic accidents. Preventive safety function using driver’s psychosomatic state with regard to anger may be one of solutions which would avoid that kind of risks. Identifying driver’s anger state is important to create countermeasures to prevent the risk of traffic accidents. As a first step, this research figured out root cause of traffic incidents by means of using Internet survey. From statistical analysis of the survey, dominant psychosomatic states immediately before traffic incidents were haste, distraction, drowsiness and anger. Then, we replicated anger state of a driver while driving, and then, replicated it by means of using driving simulator on bench test basis. Six types of facial expressions including anger were introduced as alternative characteristics. Kohonen neural network was adopted to classify anger state. Then, we created a methodology to detect anger state of a driver in high accuracy. We presented a driving support safety function. The function adapts driver’s anger state in cooperation with an autonomous driving unit to reduce the number of traffic accidents. Consequently, e evaluated reduction rate of driver’s anger in the traffic accident. To validate the estimation results, we referred the reduction rate of Advanced Safety Vehicle (ASV) as well as Intelligent Transportation Systems (ITS).

Keywords: Kohonen neural network, driver’s anger state, reduction of traffic accidents, driver’s state adaptive driving support safety

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Authors: Ehsan Mohseni, Waiching Tang, Shanyong Wang

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Low thermal conductivity of phase change materials (PCMs) affects the thermal performance and energy storage efficiency of latent heat thermal energy storage systems. In the current research, a structural lightweight concrete with function of indoor temperature control was developed using thermal energy storage aggregates (TESA) and nano-titanium (NT). The macro-encapsulated technique was served to incorporate the PCM into the lightweight aggregate through vacuum impregnation. The compressive strength was measured, and the thermal performance of concrete panel was evaluated by using a self-designed environmental chamber. The impact of NT on microstructure was also assessed via scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) tests. The test results indicated that NT was able to increase the compressive strength by filling the micro pores and making the microstructure denser and more homogeneous. In addition, the environmental chamber experiment showed that introduction of NT into TESA improved the heat transfer of composites noticeably. The changes were illustrated by the reduction in peak temperatures in the centre, outside and inside surfaces of concrete panels by the inclusion of NT. It can be concluded that NT particles had the capability to decrease the energy consumption and obtain higher energy storage efficiency by the reduction of indoor temperature.

Keywords: heat transfer, macro-encapsulation, microstructure properties, nanoparticles, phase change material

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Lucas Nascimento, Ana Rita, Margarida Soares, André Ribeiro, Zlatina Genisheva, Hugo Silva, Joana Carvalho

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The footwear industry is an essential fashion industry, focusing on producing various types of footwear, such as shoes, boots, sandals, sneakers, and slippers. Global footwear consumption has doubled every 20 years since the 1950s. It is estimated that in 1950, each person consumed one new pair of shoes yearly; by 2005, over 20 billion pairs of shoes were consumed. To meet global footwear demand, production reached $24.2 billion, equivalent to about $74 per person in the United States. This means three new pairs of shoes per person worldwide. The issue of footwear waste is related to the fact that shoe production can generate a large amount of waste, much of which is difficult to recycle or reuse. This waste includes scraps of leather, fabric, rubber, plastics, toxic chemicals, and other materials. The search for alternative solutions for waste treatment and valorization is increasingly relevant in the current context, mainly when focused on utilizing waste as a source of substitute materials. From the perspective of the new circular economy paradigm, this approach is of utmost importance as it aims to preserve natural resources and minimize the environmental impact associated with sending waste to landfills. In this sense, the incorporation of waste into industrial sectors that allow for the recovery of large volumes, such as road construction, becomes an urgent and necessary solution from an environmental standpoint. This study explores the use of plastic waste from the footwear industry as a substitute for virgin polymers in bitumen modification, a solution that presents a more sustainable future. Replacing conventional polymers with plastic waste in asphalt composition reduces the amount of waste sent to landfills and offers an opportunity to extend the lifespan of road infrastructures. By incorporating waste into construction materials, reducing the consumption of natural resources and the emission of pollutants is possible, promoting a more circular and efficient economy. In the initial phase of this study, waste materials from end-of-life footwear were selected, and plastic waste with the highest potential for application was separated. Based on a literature review, EVA (ethylene vinyl acetate) and PU (polyurethane) were identified as the polymers suitable for modifying 50/70 classification bitumen. Each polymer was analysed at concentrations of 3% and 5%. The production process involved the polymer's fragmentation to a size of 4 millimetres after heating the materials to 180 ºC and mixing for 10 minutes at low speed. After was mixed for 30 minutes in a high-speed mixer. The tests included penetration, softening point, viscosity, and rheological assessments. With the results obtained from the tests, the mixtures with EVA demonstrated better results than those with PU, as EVA had more resistance to temperature, a better viscosity curve and a greater elastic recovery in rheology.

Keywords: footwear waste, hot asphalt pavement, modified bitumen, polymers

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Authors: Surena V.

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Background and Aim: Periodontal diseases are among the most common infectious diseases all around the world, even in developed countries. Considering the increased rate of microbial resistance to antibiotics and the chemical side effects of antibiotics and antiseptics used for the treatment of periodontal disease, there is a need for an alternative antimicrobial agent with fewer complications. Medicinal herbs have recently become popular as antimicrobial and preventive agents. This study aimed to assess the antibacterial effects of hydroalcoholic extracts of Lawsonia inermis, Malva sylvestris and Boswellia serrata on Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans). Materials and Methods: Hydroalcoholic extracts of the three medicinal plants were obtained by the maceration technique and A. actinomycetemcomitans was cultured. The antimicrobial efficacy of the three medicinal plants was compared with that of 0.2% chlorhexidine (CHX) according to the CLSI protocol using agar disc diffusion and broth microdilution techniques. All tests were repeated three times. Results: Hydroalcoholic extracts of all three plants had antimicrobial activity against A. actinomycetemcomitans. The minimum inhibitory concentration (MIC) of Lawsonia inermis, Malva sylvestris, and Boswellia serrata was 78.1, 156.2, and 1666 µg/mL with no significant difference between them. The MIC of CHX was 3.33 µg/mL, which was significantly higher than that of Boswellia serrata extract. Conclusion: Given that, further in vivo studies confirm other properties of these extracts and their safety in terms of cytotoxicity and mutagenicity, hydroalcoholic extracts of Lawsonia inermis and Malva sylvestris may be used in mouthwashes or local delivery systems to affect periodontal biofilm.

Keywords: actinobacilus actinomycetem commitans, lawsonia inermis, malva sylvestris, boswellia serrata

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Authors: Olga A. Krysiak, Grzegorz Cichowicz, Wojciech Hyk, Michal Cyranski, Jan Augustynski

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Metal oxides are known electrocatalyst in water oxidation reaction. Due to the fact that it is desirable for efficient oxygen evolution catalyst to contain numerous redox-active metal ions to guard four electron water oxidation reaction, mixed metal oxides exhibit enhanced catalytic activity towards oxygen evolution reaction compared to single metal oxide systems. On the surface of fluorine doped tin oxide coated glass slide (FTO) deposited (doctor blade technique) mixed metal oxide layer composed of nickel, iron, and chromium. Oxide coating was acquired by heat treatment of the aqueous precursors' solutions of the corresponding salts. As-prepared electrodes were photosensitive and acted as an efficient oxygen evolution catalyst. Our results showed that obtained by this method electrodes can be activated which leads to achieving of higher current densities. The recorded current and photocurrent associated with oxygen evolution process were at least two orders of magnitude higher in the presence of oxide layer compared to bare FTO electrode. The overpotential of the process is low (ca. 0,2 V). We have also checked the activity of the catalyst at different known photoanodes used in sun-driven water splitting. Herein, we demonstrate that we were able to achieve efficient oxygen evolution catalysts using relatively cheap precursor consisting of earth abundant metals and simple method of preparation.

Keywords: chromium, electrocatalysis, iron, metal oxides, nickel, oxygen evolution

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Authors: Bulcha Belay Etana

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Research shows that several challenges are to be resolved for textile sensors and wearable smart textiles systems to make it accurate and reproducible minimizing variability issues when tested. To achieve this, we developed stimulating embroidery electrode with three different filling textiles such as 3Dknit, microfiber, and nonwoven fabric, and tested with FTT for high recoverability on compression. Hence The impedance characteristics of wetted electrodes were caried out after 1hr of wetting under normal environmental conditions. The wetted 3D knit (W-3D knit), Wetted nonwoven (W-nonwoven), and wetted microfiber (W-microfiber) developed using Satin stitch performed better than a dry standard stitch or dry Satin stitch electrodes. Its performance was almost the same as that of the gel electrode (Ag/AgCl) as shown by the impedance result in figure 2 .The impedance characteristics of Dry and wetted 3D knit based Embroidered electrodes are better than that of the microfiber, and nonwoven filling textile. This is due to the fact that 3D knit fabric has high recoverability on compression to retain electrolyte gel than microfiber, and nonwoven. However,The non-woven fabric held the electrolyte for longer time without releasing it to the skin when needed, thus making its impedance characteristics poor as observed from the results. Whereas the dry Satin stitch performs better than the standard stitch based developed electrode. The inter electrode distance of all types of the electrode was 25mm, with the area of the electrode being 20mm by 20mm. Detail evaluation and further analysis is in progress for EMG monitoring application

Keywords: impedance, moisture retention, 3D knit fabric, microfiber, nonwoven

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Hamda M. Al-Ali

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The ultimate aim of space exploration has been centralized around the possibility of life on other planets in the solar system. This aim is driven by the detrimental effects that climate change could potentially have on human survival on Earth in the future. This drives humans to search for feasible solutions to increase environmental and economical sustainability on Earth and to evaluate and explore the ability of human survival on other planets such as Mars. To do that, frequent space missions are required to meet the ambitious human goals. This means that reliable and affordable access to space is required, which could be largely achieved through the use of reusable spacecrafts. Therefore, materials and resources must be used wisely to meet the increasing demand. Space missions are currently extremely expensive to operate. However, reusing materials hence spacecrafts, can potentially reduce overall mission costs as well as the negative impact on both space and Earth environments. This is because reusing materials leads to less waste generated per mission, and therefore fewer landfill sites are required. Reusing materials reduces resource consumption, material production, and the need for processing new and replacement spacecraft and launch vehicle parts. Consequently, this will ease and facilitate human access to outer space as it will reduce the demand for scarce resources, which will boost material efficiency in the space industry. Material efficiency expresses the extent to which resources are consumed in the production cycle and how the waste produced by the industrial process is minimized. The strategies proposed in this paper to boost material efficiency in the space sector are the introduction of key performance indicators that are able to measure material efficiency as well as the introduction of clearly defined policies and legislation that can be easily implemented within the general practices in the space industry. Another strategy to improve material efficiency is by amplifying energy and resource efficiency through reusing materials. The circularity of various spacecraft materials such as Kevlar, steel, and aluminum alloys could be maximized through reusing them directly or after galvanizing them with another layer of material to act as a protective coat. This research paper has an aim to investigate and discuss how to improve material efficiency in space missions considering circular economy concepts so that space and Earth become more economically and environmentally sustainable. The circular economy is a transition from a make-use-waste linear model to a closed-loop socio-economic model, which is regenerative and restorative in nature. The implementation of a circular economy will reduce waste and pollution through maximizing material efficiency, ensuring that businesses can thrive and sustain. Further research into the extent to which reusable launch vehicles reduce space mission costs have been discussed, along with the environmental and economic implications it could have on the space sector and the environment. This has been examined through research and in-depth literature review of published reports, books, scientific articles, and journals. Keywords such as material efficiency, circular economy, reusable launch vehicles and spacecraft materials were used to search for relevant literature.

Keywords: circular economy, key performance indicator, material efficiency, reusable launch vehicles, spacecraft materials

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: M. Nadeem, Y. K. Chang, C. Diallo, U. Venkatadri, P. Havard, T. Nguyen-Quang

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Uniform distribution of agro-chemicals is highly important because there is a significant loss of agro-chemicals, for example from pesticide, during spraying due to non-uniformity of droplet and off-target drift. Improving the efficiency of spray pattern for different cropping systems would reduce energy, costs and to minimize environmental pollution. In this paper, we examine the water jet patterns in order to study the performance and uniformity of water distribution during the spraying process. We present a method to quantify the water amount from a sprayer jet by using the Particle Image Velocimetry (PIV) system. The results of the study will be used to optimize sprayer or nozzles design for chemical application. For this study, ten sets of images were acquired by using the following PIV system settings: double frame mode, trigger rate is 4 Hz, and time between pulsed signals is 500 µs. Each set of images contained different numbers of double-framed images: 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 at eight different pressures 25, 50, 75, 100, 125, 150, 175 and 200 kPa. The PIV images obtained were analysed using custom-made image processing software for droplets and volume calculations. The results showed good agreement of both manual and PIV measurements and suggested that the PIV technique coupled with image processing can be used for a precise quantification of flow through nozzles. The results also revealed that the method of measuring fluid flow through PIV is reliable and accurate for sprayer patterns.

Keywords: image processing, PIV, quantifying the water volume from nozzle, spraying pattern

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Authors: Maria Pintea, Nigel Mason

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Induced chemistry is one of the newest pathways in the nanotechnology field with applications in the focused electron beam induced processes for deposition of nm scale structures. Si(OPr)₄ and Ti(OEt)₄ are two of the precursors that have not been so extensively researched, though highly sought for semiconductor and medical applications fields, the two compounds make good candidates for FEBIP and are the subject of velocity slice map imaging analysis for deposition purposes, offering information on kinetic energies, fragmentation channels, and angular distributions. The velocity slice map imaging technique is a method used for the characterization of molecular dynamics of the molecule and the fragmentation channels as a result of induced chemistry. To support the gas-phase analysis, Meso-Bio-Nano simulations of irradiation dynamics studies are employed with final results on Fe(CO)₅ deposited on various substrates. The software is capable of running large scale simulations for complex biomolecular, nano- and mesoscopic systems with applications to thermos-mechanical DNA damage, complex materials, gases, nanoparticles for cancer research and deposition applications for nanotechnology, using a large library of classical potentials, many-body force fields, molecular force fields involved in the classical molecular dynamics.

Keywords: focused electron beam induced deposition, FEBID, induced chemistry, molecular dynamics, velocity map slice imaging

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Authors: Mingxin Li, Liya Ni

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Autonomous parking is a valuable feature applicable to many robotics applications such as tour guide robots, UV sanitizing robots, food delivery robots, and warehouse robots. With auto-parking, the robot will be able to park at the charging zone and charge itself without human intervention. As compared to self-driving vehicles, auto-parking is more challenging for a small-scale mobile robot only equipped with a front camera due to the camera view limited by the robot’s height and the narrow Field of View (FOV) of the inexpensive camera. In this research, auto-parking of a small-scale mobile robot with a front camera only was achieved in a four-step process: Firstly, transfer learning was performed on the AlexNet, a popular pre-trained convolutional neural network (CNN). It was trained with 150 pictures of empty parking slots and 150 pictures of occupied parking slots from the view angle of a small-scale robot. The dataset of images was divided into a group of 70% images for training and the remaining 30% images for validation. An average success rate of 95% was achieved. Secondly, the image of detected empty parking space was processed with edge detection followed by the computation of parametric representations of the boundary lines using the Hough Transform algorithm. Thirdly, the positions of the entrance point and center of available parking space were predicted based on the robot kinematic model as the robot was driving closer to the parking space because the boundary lines disappeared partially or completely from its camera view due to the height and FOV limitations. The robot used its wheel speeds to compute the positions of the parking space with respect to its changing local frame as it moved along, based on its kinematic model. Lastly, the predicted entrance point of the parking space was used as the reference for the motion control of the robot until it was replaced by the actual center when it became visible again by the robot. The linear and angular velocities of the robot chassis center were computed based on the error between the current chassis center and the reference point. Then the left and right wheel speeds were obtained using inverse kinematics and sent to the motor driver. The above-mentioned four subtasks were all successfully accomplished, with the transformed learning, image processing, and target prediction performed in MATLAB, while the motion control and image capture conducted on a self-built small scale differential drive mobile robot. The small-scale robot employs a Raspberry Pi board, a Pi camera, an L298N dual H-bridge motor driver, a USB power module, a power bank, four wheels, and a chassis. Future research includes three areas: the integration of all four subsystems into one hardware/software platform with the upgrade to an Nvidia Jetson Nano board that provides superior performance for deep learning and image processing; more testing and validation on the identification of available parking space and its boundary lines; improvement of performance after the hardware/software integration is completed.

Keywords: autonomous parking, convolutional neural network, image processing, kinematics-based prediction, transfer learning

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Authors: Katerina Krizova, Inigo Molina

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The ongoing climate change affects various natural processes resulting in significant changes in human life. Since there is still a growing human population on the planet with more or less limited resources, agricultural production became an issue and a satisfactory amount of food has to be reassured. To achieve this, agriculture is being studied in a very wide context. The main aim here is to increase primary production on a spatial unit while consuming as low amounts of resources as possible. In Europe, nowadays, the staple issue comes from significantly changing the spatial and temporal distribution of precipitation. Recent growing seasons have been considerably affected by long drought periods that have led to quantitative as well as qualitative yield losses. To cope with such kind of conditions, new techniques and technologies are being implemented in current practices. However, behind assessing the right management, there is always a set of the necessary information about plot properties that need to be acquired. Remotely sensed data had gained attention in recent decades since they provide spatial information about the studied surface based on its spectral behavior. A number of space platforms have been launched carrying various types of sensors. Spectral indices based on calculations with reflectance in visible and NIR bands are nowadays quite commonly used to describe the crop status. However, there is still the staple limit by this kind of data - cloudiness. Relatively frequent revisit of modern satellites cannot be fully utilized since the information is hidden under the clouds. Therefore, microwave remote sensing, which can penetrate the atmosphere, is on its rise today. The scientific literature describes the potential of radar data to estimate staple soil (roughness, moisture) and vegetation (LAI, biomass, height) properties. Although all of these are highly demanded in terms of agricultural monitoring, the crop moisture content is the utmost important parameter in terms of agricultural drought monitoring. The idea behind this study was to exploit the unique combination of SAR (Sentinel-1) and optical (Sentinel-2) data from one provider (ESA) to describe potential crop water stress during dry cropping season of 2019 at six winter wheat plots in the central Czech Republic. For the period of January to August, Sentinel-1 and Sentinel-2 images were obtained and processed. Sentinel-1 imagery carries information about C-band backscatter in two polarisations (VV, VH). Sentinel-2 was used to derive vegetation properties (LAI, FCV, NDWI, and SAVI) as support for Sentinel-1 results. For each term and plot, summary statistics were performed, including precipitation data and soil moisture content obtained through data loggers. Results were presented as summary layouts of VV and VH polarisations and related plots describing other properties. All plots performed along with the principle of the basic SAR backscatter equation. Considering the needs of practical applications, the vegetation moisture content may be assessed using SAR data to predict the drought impact on the final product quality and yields independently of cloud cover over the studied scene.

Keywords: precision agriculture, remote sensing, Sentinel-1, SAR, water content

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Authors: Sumiko Anno, Keiji Imaoka, Takeo Tadono, Tamotsu Igarashi, Subramaniam Sivaganesh, Selvam Kannathasan, Vaithehi Kumaran, Sinnathamby Noble Surendran

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Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately and still require systematic clarification. The present study aimed to investigate the spatial-temporal clustering relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites were used to develop an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling temporal analysis, spatial statistical analysis and space-time clustering analysis. Our present results showed that increases in the number of the combination of ecological factor and socio-economic and demographic factors with above the average or the presence contribute to significantly high rates of space-time dengue clusters.

Keywords: ALOS/AVNIR-2, dengue, space-time clustering analysis, Sri Lanka

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Authors: Kanagarajah Ravishankar

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This paper describes a study performed for a project featuring an elevated steel bridge structure supported by various types of foundation systems. This project focused on rehabilitation or redesign of a portion of the bridge substructures founded on caisson foundations. The study that this paper focuses on is the evaluation of foundation and soil stiffnesses and interactions between the existing caissons and proposed foundations. The caisson foundations were founded on top of rock, where the depth to the top of rock varies from approximately 50 to 140 feet below ground surface. Based on a comprehensive investigation of the existing piers and caissons, the presence of ASR was suspected from observed whitish deposits on cracked surfaces as well as internal damages sustained through the entire depth of foundation structures. Reuse of existing piers and caissons was precluded and deemed unsuitable under the earthquake condition because of these defects on the structures. The proposed design of new foundations and substructures which was selected ultimately neglected the contribution from the existing caisson and pier columns. Due to the complicated configuration between the existing caisson and the proposed foundation system, three-dimensional finite element method (FEM) was employed to evaluate soil-structure interaction (SSI), to evaluate the effect of the existing caissons on the proposed foundations, and to compare the results with conventional group analysis. The FEM models include separate models for existing caissons, proposed foundations, and combining both.

Keywords: soil-structure interaction, foundation stiffness, finite element, seismic design

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Chethana V. Casiker, Jagadishakumara B., Sunil G. M., Chaithra K., M. Soubadra Devy

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A majority of the world’s food crops depends on insects for pollination, among which bees are the most dominant taxon. Bees pollinate vegetables, fruits and oilseeds which are rich in essential micronutrients. Besides being a prerequisite for a nutritionally secure diet, agrarian economies such as India depend heavily on pollination for good yield and quality of the product. As cities all over the world expand rapidly, large tracts of green spaces are being built up. This, along with high usage of agricultural chemicals has reduced floral diversity and shrunk bee habitats. Indeed, pollinator decline is being reported from various parts of the world. Further, the FAO has reported a huge increase in the area of land under cultivation of pollinator-dependent crops. In the light of increasing demand for pollination and disappearing natural habitats, it is critical to understand whether and how urban spaces can support pollinators. To this end, this study investigates the influence of landscape and local habitat quality on bee community dynamics. To capture the dynamics of expanding cityscapes, the study employs a space for time substitution, wherein a transect along the gradient of urbanization substitutes a timeframe of increasing urbanization. This will help understand how pollinators would respond to changes induced by increasing intensity of urbanization in the future. Bengaluru, one of the fastest growing cities of Southern India, is an excellent site to study impacts associated with urbanization. With sites moving away from the Bengaluru’s centre and towards its peripheries, this study captures the changes in bee species diversity and richness along a gradient of urbanization. Bees were sampled under different land use types as well as in different types of vegetation, including plantations, croplands, fallow land, parks, lake embankments, and private gardens. The relationship between bee community metrics and key drivers such as a percentage of built-up area, land use practices, and floral resources was examined. Additionally, data collected using questionnaire interviews were used to understand people’s perceptions towards and level of dependence on pollinators. Our results showed that urban areas are capable of supporting bees. In fact, a greater diversity of bees was recorded in urban sites compared to adjoining rural areas. This suggests that bees are able to seek out patchy resources and survive in small fragments of habitat. Bee abundance and species richness correlated positively with floral abundance and richness, indicating the role of vegetation in providing forage and nesting sites which are crucial to their survival. Bee numbers were seen to decrease with increase in built-up area demonstrating that impervious surfaces could act as deterrents. Findings from this study challenge the popular notion of cities being biodiversity-bare spaces. There is indeed scope for conserving bees in urban landscapes, provided that there are city-scale planning and local initiative. Bee conservation can go hand in hand with efforts such as urban gardening and terrace farming that could help cities urbanize sustainably.

Keywords: bee, landscape ecology, urbanization, urban pollination

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Authors: Mónica Catarina Soares

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Neoliberal rationality is currently changing the ways concepts like freedom or equality are framed. As an omnipresent and context-sensitive paradigm, homo oeconomicus is continuously taking place in realms of life previously insulated from economic and market-driven principles. This presentation is based on the argument that, more than ever, this paradigm is nowadays framing institutional and everyday discourses in regard to social problems. Although neoliberal rationality is based on the putative ideological basis that everyone is equal, equality seems to be reshaped by specific meanings apprehended by this rationality. In this sense, an illusion of equality seems to be relevant to legitimize different social inequalities (e.g., access to health care or to habitation). Political psychology has studied how ideology is relevant to legitimize market and unequal systems, but still the specific relation between markets, (in)equality and neoliberal languages is not widely addressed. The goal is to discuss the smithereens of the neoliberal rationality when it comes to legitimizing social inequalities by contesting the arguments of meritocracy, progressive freedom and minimal guarantees obeying to market-rules and principles. This analysis can be helpful to grasp for instance the continuously dismantlement of the welfare-state in different countries of the global north and how it is turning the regulation/emancipation tension inside out. The ultimate goal is to contribute to the breaking up of a paradigm that is still too big to capture, too depoliticized and chameleonic to fully acknowledge the biopolitics of power that is helping to create it.

Keywords: discourses, legitimacy, neoliberal rationality, social inequality

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Authors: Hamid El Qarnia

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This study investigates the impact of the eccentricity of the central tube on the thermal and fluid characteristics of a triplex tube used in latent heat energy storage technologies. Two triplex tube orientations are considered in the proposed study: vertical and horizontal. The energy storage material, which is a phase change material (PCM), is placed in the space between the inside and outside tubes. During the thermal energy storage period, a heat transfer fluid (HTF) flows inside the two tubes, transmitting the heat to the PCM through two heat exchange surfaces instead of one heat exchange surface as it is the case for double tube heat storage systems. A CFD model is developed and validated against experimental data available in the literature. The mesh independency study is carried out to select the appropriate mesh. In addition, different time steps are examined to determine a time step ensuring accuracy of the numerical results and reduction in the computational time. The numerical model is then used to conduct numerical investigations of the thermal behavior and thermal performance of the storage unit. The effects of eccentricity of the central tube and HTF mass flow rate on thermal characteristics and performance indicators are examined for two flow arrangements: co-current and counter current flows. The results are given in terms of isotherm plots, streamlines, melting time and thermal energy storage efficiency.

Keywords: energy storage, heat transfer, melting, solidification

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Authors: Arleta Rifati Nixha, Mustafa Arslan, Adem Ergün, Nahit Gencer

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Polyphenol oxidase (PPO), sometimes referred to as phenol oxidase, catecholase, phenolase, catechol oxidase, or even tyrosinase, is considered to be an o-dipenol. PPO (EC 1.14.18.1), a multifunctional copper containing enzyme, is widely distributed in nature. It catalyzes two distinct reactions of melanin synthesis: a hydroxylation of monophenols to o-diphenols (monophenolase activity) and an oxidation of o-diphenols to o-quinones (diphenolase activity), both using molecular oxygen. Additionaly, investigation demonstrated that various dermatological disorders, such as age spots and freckle, were caused by the accumulation of an excessive level of epidermal pigmentation. Tyrosinase has also been linked to Parkinson’s and other neurodegenerative diseases. Nitrogen heterocycles have received a great deal of attention in the literature because of biological properties. Especially, among these heterocyclic systems, pyridine containing compounds have been the subject of expanding research efforts in heteroaromatic and biological chemistry. The pyrido [2,3-d] pyrimidine heterocycles, which are those annelated to a pyrimidine ring, are important because of their wide range of biological and pharmaceutical applications (i.e., bronchodilators, vasodilators) and their anti-allergic, cardiotonic, antihypertensive, and hepatoprotective activities. In this study series of 12 new carbazole substituted pyridopyrimidine urea(thiourea) derivatives were synthesized and evaluated effect on PPO. Additionally, we presented structure-activity relationship analyses and theoretical calculations of the compounds.

Keywords: carbazole, pyridopyrimidine, urea, thiourea, tyrosinase inhibitors

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Authors: Jugoslav Jokovic, Tijana Dimitrijevic, Nebojsa Doncov

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The goal of this paper is to investigate the possibilities and effectiveness of the TLM (Transmission Line Matrix) method for modelling of up-to-date microstrip antennas with circular geometry that have significant application in modern wireless communication systems. The coaxially fed microstrip antenna configurations with circular patch are analyzed by using the in-house 3DTLMcyl_cw solver based on computational electromagnetic TLM method adapted to the cylindrical grid and enhanced with the compact wire model. Opposed to the widely used rectangular TLM mesh, where a staircase approximation has to be used to describe curved boundaries, precise modelling of circular boundaries can be accomplished in the cylindrical grid irrespective of the mesh resolution. Using the compact wire model incorporated in cylindrical mesh, it is possible to model coaxial feed and include the influence of the real excitation in the antenna model. The conventional and inverted configuration of a coaxially fed circular patch antenna are considered, comparing the resonances obtained using TLM cylindrical model with results reached by the corresponding model in a rectangular grid as well as with experimental ones. Bearing in mind that accuracy of simulated results depends on a relevantly created model, besides structure geometry and dimensions, it is important to consider additional modelling issues, regarding appropriate mesh resolution and a relevant extension of a mesh around the considered structure that would provide convergence of the results.

Keywords: computational electromagnetic, coaxial feed, microstrip antenna, TLM modelling

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Ahmad Mohsin, Syda Fatima, Falak Nawaz, Aman Ullah Khan

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Selection of an appropriate architectural style is vital to the success of target web service under development. The nature of architecture design and selection for service-oriented computing applications is quite different as compared to traditional software. Web Services have complex and rigorous architectural styles to choose. Due to this, selection for accurate architectural style for web services development has become a more complex decision to be made by architects. Architectural style selection is a multi-criteria decision and demands lots of experience in service oriented computing. Decision support systems are good solutions to simplify the selection process of a particular architectural style. Our research suggests a new approach using DSS for selection of architectural styles while developing a web service to cater FRs and NFRs. Our proposed DSS helps architects to select right web service architectural pattern according to the domain and non-functional requirements. In this paper, a rule base DSS has been developed using CLIPS (C Language Integrated Production System) to support decisions using multi-criteria requirements. This DSS takes architectural characteristics, domain requirements and software architect preferences for NFRs as input for different architectural styles in use today in service-oriented computing. Weighted sum model has been applied to prioritize quality attributes and domain requirements. Scores are calculated using multiple criterions to choose the final architecture style.

Keywords: software architecture, web-service, rule-based, DSS, multi-criteria requirements, quality attributes

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Authors: Vinay Agrawal, Suyash Garg, Ravindra Nagar, Vinay Chandwani

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Open ground storey with floating columns is a typical feature in the modern multistory constructions in urban India. Such features are very much undesirable in buildings built in seismically active areas. The present study proposes a feasible solution to mitigate the effects caused due to non-uniformity of stiffness and discontinuity in load path and to simultaneously hold the functional use of the open storey particularly under the floating column, through a combination of various lateral strengthening systems. An investigation is performed on an example building with nine different analytical models to bring out the importance of recognising the presence of open ground storey and floating columns. Two separate analyses on various models of the building namely, the equivalent static analysis and the response spectrum analysis as per IS: 1893-2002 were performed. Various measures such as incorporation of Chevron bracings and shear walls, strengthening the columns in the open ground storey, and their different combinations were examined. The analysis shows that, in comparison to two short ones separated by interconnecting beams, the structural walls are most effective when placed at the periphery of the buildings and used as one long structural wall. Further, it can be shown that the force transfer from floating columns becomes less horizontal when the Chevron Bracings are placed just below them, thereby reducing the shear forces in the beams on which the floating column rests.

Keywords: equivalent static analysis, floating column, open ground storey, response spectrum analysis, shear wall, stiffness irregularity

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