Search results for: processing schemes

Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: C. Girometta, S. Babbini, R. M. Baiguera, D. S. Branciforti, M. Cartabia, D. Dondi, M. Pellegrini, A. M. Picco, E. Savino

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Interest on wood decay fungi (WDF) has been increasing in the last year's thanks to the potentiality of this kind of fungi; research on new WDF strains has increased as well thus pointing out the key role of the culture collections. One of the most recent biotechnological application of WDF is the development of novel materials from natural or recycled resources. Based on different combinations of fungal species, substrate, and processing treatment involved (e.g. heat pressing), it is possible to achieve a wide variety of materials with different features useful for many industrial applications: from packaging to thermal and acoustic insulation. In comparison with the conventional ones, these materials represent a 100% natural and compostable alternative involving low amounts of energy in the production process. The purpose of the present work was to isolate and select WDF strains able to colonize and degrade different plant wastes thus producing a fungal biomass shapeable to achieve bio-composite materials. Strains were selected within the mycological culture collection of Pavia University (MicUNIPV, over 300 strains of WDF). The selected strains have been investigated with regards their ability to colonize and degrade plant residues from the local major cultivations (e.g. poplar, alfalfa, maize, rice, and wheat) and produce the fungal biomass. The degradation of the substrate was assessed by Thermogravimetric analysis (TGA) and Fourier Transform Infrared Spectroscopy (FTIR). Chemical characterization confirmed that TGA and FTIR are complementary techniques able to provide quality-quantitative information on compositional and structural variation that occurs during the transformation from the substrate to the bio-composite material. This pilot study provides a fundamental step to tune further applications in fungus-residues composite biomaterials.

Keywords: bio-composite material, lignocellulosic residues, sustainable materials, wood decay fungi

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Authors: Rishi Agarwal

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Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

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Authors: Gulcin Yildiz, Gokcen Izli

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Drying (dehydration) is the process of removing water from food in order to preserve the food. Drying is one of the oldest methods known for the preservation of agricultural products such as fruits and vegetables. Drying of agricultural products enhances their storage life, minimizes losses during storage, and save shipping and transportation costs. Apples are considered excellent candidates for drying. The objective of this research was to investigate the effects of microwave and oven processing on the quality of selected apple products. Red delicious and golden delicious apples were washed, peeled, and sliced. Drying experiments were performed in an oven at 50, 75 and 100 °C and in a microwave at 140 W and 210 W. Quality attributes such as color, total phenolic content and antioxidant capacity of dried samples with different methods were compared with the fresh sample. A Minolta CR-300 Chroma Meter was used to examine color changes in the apples. Total phenolic content was determined using the Folin-Ciocalteu reagent. The free radical scavenging activity of the extract was determined using 1,1-diphenyl-2-picrylhydrazyl (DPPH). It was found that the phenolic contents and antioxidant capacities of dried samples under all drying conditions were decreased compared to the fresh samples. The phenolic contents of microwave dried samples at 140 W and 210 W for both red and golden delicious apples were higher than those of the oven drying at 50, 75 and 100 °C. Similarly, the antioxidant activities of microwave dried samples at 140 W and 210 W were higher than those of the oven drying at 50, 75 and 100 °C for both types of apples. All color parameters (L*, a*, b*) were changed significantly depending on the drying methods and temperatures. The closest color values to the fresh sample were found for the microwave dried samples at 140 W. Microwave drying was proven to be more effective than oven drying.

Keywords: antioxidant capacity, color, golden delicious, microwave, red delicious, total phenolic content

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Authors: Jorge Prada, Christina Cordes, Carsten Harms, Walter Lang

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The development of microfluidic-based biosensors over the last years has shown an increasing employ of thermoplastic polymers as constitutive material. Their low-cost production, high replication fidelity, biocompatibility and optical-mechanical properties are sought after for the implementation of disposable albeit functional lab-on-chip solutions. Among the range of thermoplastic materials on use, the Cyclo-Olefin Copolymer (COC) stands out due to its optical transparency, which makes it a frequent choice as manufacturing material for fluorescence-based biosensors. Moreover, several processing techniques to complete a closed COC microfluidic biosensor have been discussed in the literature. The reported techniques differ however in their implementation, and therefore potentially add more or less complexity when using it in a mass production process. This work introduces and reports results on the application of a purely thermal bonding process between COC substrates, which were produced by the hot-embossing process, and COC foils containing screen-printed circuits. The proposed procedure takes advantage of the transition temperature difference between two COC grades foils to accomplish the sealing of the microfluidic channels. Patterned heat injection to the COC foil through the COC substrate is applied, resulting in consistent channel geometry uniformity. Measurements on bond strength and bursting pressure are shown, suggesting that this purely thermal bonding process potentially renders a technique which can be easily adapted into the thermoplastic microfluidic chip production workflow, while enables a low-cost as well as high-quality COC biosensor manufacturing process.

Keywords: biosensor, cyclo-olefin copolymer, hot embossing, thermal bonding, thermoplastics

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Sandra H. Fukurozaki, Andre L. N. Silva, Joao B. F. Neto, Fernando J. G. Landgraf

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Nowadays, the rare earth (RE) metals play an important role in emerging technologies that are crucial for the decarbonisation of the energy sector. Their unique properties have led to increasing clean energy applications, such as wind turbine generators, and hybrid and electric vehicles. Despite the substantial media coverage that has recently surrounded the mining and processing of rare earth metals, very little quantitative information is available concerning their subsequent life stages, especially related to the metallic production of didymium (Nd-Pr) in fluoride molten salt system. Here we investigate a gate to gate scale life cycle assessment (LCA) of the didymium electrolysis based on three different scenarios of operational conditions. The product system is modeled with SimaPro Analyst 8.0.2 software, and IMPACT 2002+ was applied as an impact assessment tool. In order to develop a life cycle inventories built in software databases, patents, and other published sources together with energy/mass balance were utilized. Analysis indicates that from the 14 midpoint impact categories evaluated, the global warming potential (GWP) is the main contributors to the total environmental burden, ranging from 2.7E2 to 3.2E2 kg CO2eq/kg Nd-Pr. At the damage step assessment, the results suggest that slight changes in materials flows associated with enhancement of current efficiency (between 2.5% and 5%), could lead a reduction up to 12% and 15% of human health and climate change damage, respectively. Additionally, this paper highlights the knowledge gaps and future research efforts needing to understand the environmental impacts of Nd-Pr electrolysis process from the life cycle perspective.

Keywords: didymium electrolysis, environmental impacts, life cycle assessment, rare earth metals

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Authors: S. O. Ayansina, R. A. Oyeyinka, K. K. Bolarinwa

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There is an increasing demand for a functional extension delivery services in Nigeria with a view to meet up with the food and fiber needs of the ever growing population of human and animal respectively. This study was designed to examine farmers’ involvement in public and private extension services in southwestern Nigeria, specifically to explore the farmers’ participation in the two types of organizations involved. It also evaluates the performances of personnel in the organizations. A multi-stage random sampling technique was used to select 30 respondents from each of the three selected organizations in Ogun, Osun and Oyo states in Southwestern Nigeria. Data was collected with interview schedule and analyzed both at descriptive and inferential levels. Kruskal Wallis one-way Analysis of variance was used to test the differences between the participation of beneficiaries who are farmers under the public and private extension services and the level of benefit accrued to them from the various extension organizations involved in the study. Results revealed that private extension organizations were performing better and were more preferred by the beneficiaries. Results of the tested hypotheses as shown by Kruskal Wallis test of difference (x2 = 0.709) S no significant difference between farmers’ participation in the extension services of public and private organizations but however showed significant difference (X2 =12.074) in the benefits achieved by respondents in the two organizations. These include: increased quantity of crop produced, farm income, skill acquisition, and improved education in private extension organizations. Based on this result, it could be inferred that beneficiaries generally preferred private extension organizations because of their effectiveness and vibrancy in programme administration. Public extension is therefore recommended for general overhauling and possibly privatization in order to cater for teeming population of farmers demanding for efficient and functional extension services to better their lots in production, processing and marketing of agricultural produce.

Keywords: public and private involvement, extension services, farmers' participation

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Authors: Muhammad Nasar-u-Minallah, Dagmar Haase, Salman Qureshi

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Rapid urbanization significantly change land use, urban nature, land surface vegetation cover, and heat distribution, leading to the formation of urban heat island (UHI) effect and affecting the healthy growth of cities and the comfort of human living style. Past information and present changes in Land Surface Temperature (LST) and urban landscapes could be useful to geographers, environmentalists, and urban planners in an attempt to shape the urban development process and mitigate the effects of urban heat islands (UHI). This study aims at using Satellite Remote Sensing (SRS) and GIS techniques to develop an approach for assessing the urban nature and UHI effects in Lahore, Pakistan. The study employed the Radiative Transfer Method (RTM) in estimating LST to assess the SUHI effect during the interval of 20 years (2000-2020). The assessment was performed by the available Landsat 7/ETM+ and Landsat 8/OIL_TIRs data for the years 2000, 2010, and 2020 respectively. Pearson’s correlation and normalized mutual information were applied to investigate the relationship between green space characteristics and LST. The result of this work revealed that the influence of urban heat island is not always at the city centers but sometimes in the outskirt where a lot of development activities were going on towards the direction of expansion of Lahore, Pakistan. The present study explores the usage of image processing and spatial analysis in the drive towards achieving urban greening of Lahore and a sustainable urban environment in terms of urban planning, policy, and decision making and promoting the healthy and sustainable urban environment of the city.

Keywords: urban nature, urban heat islands, urban green space, land use, Lahore

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Authors: Karolina Kowalczyk-Skoczylas

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The hybrid SPD process called DRECE (Dual Rolls Equal Channel Extrusion) combines the concepts of ECAP method and CONFORM extrusion, and is intended for processing sheet-metal workpieces. The material in the fоrm оf a metal strip is subjected tо plastic defоrmation by passing thrоugh the shaping tоol at a given angle α. Importantly, in this process the dimensions of the metal strip dо nоt change after the pass is cоmpleted. Subsequent DRECE passes allоw fоr increasing the effective strain in the tested material. The methоd has a significant effect оn the micrоstructure and mechanical prоperties оf the strip. The experimental tests have been conducted on the unconventional DRECE device in VŠB Ostrava, the Czech Republic. The DC03 steel strips have been processed in several passes - up to six. Then, both Erichsen cupping tests as well as static tensile tests have been performed to evaluate the effect of DRECE process on drawability, plastic anisotropy and mechanical properties of the investigated steel. Both yield strength and ultimate tensile strength increase significantly after consecutive passes. Drawability decreases slightly after the first and second pass. Then it stabilizes on a reasonably high level, which means that the steel is characterized by useful drawability for technological processes. It was investigated in the material is characterized by a normal anisotropy. In the microstructure, an intensification of the development of microshear bands and their mutual intersection is observed, which leads to the fragmentation of the grain into smaller volumes and, consequently, to the formation of an ultrafine grained structure. "The project was co-financed by the European Union within the programme "The European Funds for Śląsk (Silesia) 2021-2027".

Keywords: SPD process, low carbon steel, mechanical properties, plastic deformation, microstructure evolution

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Authors: Akintunde Alo

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The incessant encroachments into the forest ecosystem by the farmers and local contractors constitute a major threat to the conservation of genetic resources and biodiversity in Nigeria. To propose a viable monitoring system, this study employed Geographic Information System (GIS) technology to assess the changes that occurred for a period of five years (between 2011 and 2016) in Ikere forest reserve. Landsat imagery of the forest reserve was obtained. For the purpose of geo-referencing the acquired satellite imagery, ground-truth coordinates of some benchmark places within the forest reserve was relied on. Supervised classification algorithm, image processing, vectorization and map production were realized using ArcGIS. Various land use systems within the forest ecosystem were digitized into polygons of different types and colours for 2011 and 2016, roads were represented with lines of different thickness and colours. Of the six land-use delineated, the grassland increased from 26.50 % in 2011 to 45.53% in 2016 of the total land area with a percentage change of 71.81 %. Plantations of Gmelina arborea and Tectona grandis on the other hand reduced from 62.16 % in 2011 to 27.41% in 2016. The farmland and degraded land recorded percentage change of about 176.80 % and 8.70 % respectively from 2011 to 2016. Overall, the rate of deforestation in the study area is on the increase and becoming severe. About 72.59% of the total land area has been converted to non-forestry uses while the remnant 27.41% is occupied by plantations of Gmelina arborea and Tectona grandis. Interestingly, over 55 % of the plantation area in 2011 has changed to grassland, or converted to farmland and degraded land in 2016. The rate of change over time was about 9.79 % annually. Based on the results, rapid actions to prevail on the encroachers to stop deforestation and encouraged re-afforestation in the study area are recommended.

Keywords: land use change, forest reserve, satellite imagery, geographical information system

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Authors: Muhammad Hakim Bin Mat Tasir, Erwan Shahfizad Hasidan, Hamidah Makmor Bakry, M. Hafiz B. Izhar

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Every key performance indicator used to monitor a project’s construction progress emphasizes trade productivity or specific commodity run-down curves. Examples include the productivity of welding by the number of joints completed per day, quantity of NDT (Non-Destructive Tests) inspection per day, etc. This perspective is based on progress and productivity; however, it does not enable a system perspective of how we produce. This paper uses a project production system perspective by which projects are a collection of production systems comprising the interconnected network of processes and operations that represent all the work activities to execute a project from start to finish. Furthermore, it also uses the 5 Levels of production system optimization as a frame. The goal of the paper is to describe the application of Project Production Control (PPC) to control and improve the performance of several production processes associated with the fabrication and assembly of a Central Processing Platform (CPP) Jacket, part of an offshore mega project. More specifically, the fabrication and assembly of buoyancy tanks as they were identified as part of the critical path and required the highest demand for capacity. In total, seven buoyancy tanks were built, with a total estimated weight of 2,200 metric tons. These huge buoyancy tanks were designed to be reversed launching and self-upending of the jacket, easily retractable, and reusable for the next project, ensuring sustainability. Results showed that an effective application of PPC not only positively impacted construction progress and productivity but also exposed sources of detrimental variability as the focus of continuous improvement practices. This approach augmented conventional project management practices, and the results had a high impact on construction scheduling, planning, and control.

Keywords: offshore, construction, project management, sustainability

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Authors: Mohammed Islam, Jungyong Wang, Dong Cheol Seo

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The Smoothed Particle Hydrodynamics (SPH) method is a novel, meshless, and Lagrangian technique based numerical method that has shown promises to accurately predict the hydrodynamics of water and structure interactions in violent flow conditions. The main goal of this study is to build confidence on the versatility of the Smoothed Particle Hydrodynamics (SPH) based tool, to use it as a complementary tool to the physical model testing capabilities and support research need for the performance evaluation of ships and offshore platforms exposed to an extreme and harsh environment. In the current endeavor, an open-sourced SPH-based tool was used and validated for modeling and predictions of the hydrodynamic interactions of a 6-DOF ship and bergy bits. The study involved the modeling of a modern generic drillship and simplified bergy bits in floating and towing scenarios and in regular and irregular wave conditions. The predictions were validated using the model-scale measurements on a moored ship towed at multiple oblique angles approaching a floating bergy bit in waves. Overall, this study results in a thorough comparison between the model scale measurements and the prediction outcomes from the SPH tool for performance and accuracy. The SPH predicted ship motions and forces were primarily within ±5% of the measurements. The velocity and pressure distribution and wave characteristics over the free surface depicts realistic interactions of the wave, ship, and the bergy bit. This work identifies and presents several challenges in preparing the input file, particularly while defining the mass properties of complex geometry, the computational requirements, and the post-processing of the outcomes.

Keywords: SPH, ship and bergy bit, hydrodynamic interactions, model validation, physical model testing

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Authors: Ashkan Ebadi, Adam Krzyzak

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Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite the wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This paper proposes a highly accurate hotel recommender system, implemented in various layers. Using multi-aspect rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing and topic modelling techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system.

Keywords: tourism, hotel recommender system, hybrid, implicit features

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Authors: Toyosi Yewande Tunde-Akintunde, Grace Oluwatoyin Ogunlakin, Bosede Folake Olanipekun

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Peppers are excellent sources of nutrients but its high moisture content makes it susceptible to spoilage. Drying, a common processing method, results in a reduction of these nutrients in the final product. Pre-treatment of pepper before drying can be used to reduce the level of degradation of nutrients. Thus this study investigated the effect of pre-treatment (hot water blanching and soaking in brine-sodium chloride) and drying methods (oven, microwave and sun) on selected quality parameters (proximate composition, capsaicin, reducing sugar and phenolic content, pH, total solid (TS), Titratable acidity (TA), water absorption capacity (WAC) and colour) of pepper. The protein and moisture content value ranged from 9.09 to 10.23% and 5.63 to 8.48% respectively. Sun dried samples had the highest value while oven dried samples had the lowest. Brine treated samples had higher protein but lower moisture content than blanched samples. Capsaicin, reducing sugar and phenolic content values ranged from 0.68 to 0.87 mg/dm3; 3.18 to 3.79 µg/ml; and 40.67 to 84.01 mg GAE/100 g d.m respectively. The sun dried samples had higher values while the lowest values were from microwave dried samples. The brine treated samples had higher values in capsaicin while the blanched samples had higher reducing sugar and phenolic contents. The values of L, a* and b* for the dried pepper varied from 58.76 to 63.13; 7.09 to 7.34; and 11.79 to 12.36 respectively. Oven dried samples had the lowest values for a*, while its L values were the highest. The L and a* values for brine treated samples were higher than blanched samples. The pre-treatment and drying method considered resulted in different values of the quality parameters considered which indicates that drying and pre-treatment has an effect on the quality of the final dried pepper samples.

Keywords: Bell pepper, microwave drying, oven drying, quality, sun drying

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Lican Guzmán, José Manuel Lattus, Mariana Cervetto, Mauricio Calderón

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Today the estimation of effects produced by climate change in high Andean wetland environments is confronted by big challenges. This study provides a way to an analysis by remote sensing how some Ambiental aspects have evolved on the Maricunga salt flat in the last 30 years, divided into the summer and winter seasons, and if global warming is conditioning these changes. The first step to achieve this goal was the recompilation of geological, hydrological, and morphometric antecedents to ensure an adequate contextualization of its environmental parameters. After this, software processing and analysis of Landsat 5,7 and 8 satellite imagery was required to get the vegetation, water, surface temperature, and soil moisture indexes (NDVI, NDWI, LST, and SMI) in order to see how their spatial-temporal conditions have evolved in the area of study during recent decades. Results show a tendency of regular increase in surface temperature and disponibility of water during both seasons but with slight drought periods during summer. Soil moisture factor behaves as a constant during the dry season and with a tendency to increase during wintertime. Vegetation analysis shows an areal and quality increase of its surface sustained through time that is consistent with the increase of water supply and temperature in the basin mentioned before. Roughly, the effects of climate change can be described as positive for the Maricunga salt flat; however, the lack of exact correlation in dates of the imagery available to remote sensing analysis could be a factor for misleading in the interpretation of results.

Keywords: global warming, geology, SIG, Atacama Desert, Salar de Maricunga, environmental geology, NDVI, SMI, LST, NDWI, Landsat

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Authors: Auezhan Amanov, Inho Cho, Young-Sik Pyun

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Control element drive mechanism (CEDM) nozzle is manufactured as welded on the reactor vessel and currently uses Alloy 690 material. The top of the reactor is equipped with about 100 CEDM nozzles with an internal diameter of about 70 mm. Relatively large Inlet/Outlet nozzles are equipped with two outlet nozzles and four inlet nozzles on the reactor wall. The inner diameter of the nozzle is vulnerable to stress corrosion cracking (SCC), and in order to solve this problem, an ultrasonic nanocrystal surface modification (UNSM) treatment is performed on the inner diameter of the nozzle and the weld surface. The ultimate goal is to improve the service life of parts by applying compressive residual stress and suppressing primary water stress corrosion cracking (PWSCC). The main purpose is to design and fabricate a UNSM treatment device for the internal diameter processing of CEDM nozzles and inlet/outlet nozzles. In order to develop the system, the basic technology such as the development of UNSM tooling is developed and the mechanical properties and fatigue performance of before and after UNSM treatment of reactor nozzle material made of Ni-based alloys using the specimen are compared and evaluated. The inner diameter of the nozzle was treated by a newly developed UNSM treatment under the optimized treatment parameters. It was found that the mechanical properties and fatigue performance of nozzle were improved in comparison with the untreated nozzle, which may be attributed to the increase in hardness, induced compressive residual stress.

Keywords: control element drive mechanism nozzle, fatigue, Ni-based alloy, ultrasonic nanocrystal surface modification, UNSM

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Authors: Basanti Ekka, Talis Juhna

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Dairy industrial effluents originated by the typical processing activities are composed of various organic and inorganic constituents, and these include proteins, fats, inorganic salts, antibiotics, detergents, sanitizers, pathogenic viruses, bacteria, etc. These contaminants are harmful to not only human beings but also aquatic flora and fauna. Because consisting of large classes of contaminants, the specific targeted removal methods available in the literature are not viable solutions on the industrial scale. Therefore, in this on-going research, a series of coagulation, electrochemical, and catalytic methods will be employed. The bulk coagulation and electrochemical methods can wash off most of the contaminants, but some of the harmful chemicals may slip in; therefore, specific catalysts designed and synthesized will be employed for the removal of targeted chemicals. In the context of Latvian dairy industries, presently, work is under progress on the characterization of dairy effluents by total organic carbon (TOC), Inductively Coupled Plasma Mass Spectrometry (ICP-MS)/ Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), High-Performance Liquid Chromatography (HPLC), Gas Chromatography-Mass Spectrometry (GC-MS), and Mass Spectrometry. After careful evaluation of the dairy effluents, a cost-effective natural coagulant will be employed prior to advanced electrochemical technology such as electrocoagulation and electro-oxidation as a secondary treatment process. Finally, graphene oxide (GO) based hybrid materials will be used for post-treatment of dairy wastewater as graphene oxide has been widely applied in various fields such as environmental remediation and energy production due to the presence of various oxygen-containing groups. Modified GO will be used as a catalyst for the removal of remaining contaminants after the electrochemical process.

Keywords: catalysis, dairy wastewater, electrochemical method, graphene oxide

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Emily Rabe, Stephanie Candelaria, Rachel Malone, Olivia Lenz, Greg Newbloom

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Electrodialysis (ED) is a well-developed technology for ion removal in a variety of applications. However, many industries generate harsh wastewater streams that are incompatible with traditional ion exchange membranes. Membrion® has developed novel ceramic-based ion exchange membranes (IEMs) offering several advantages over traditional polymer membranes: high performance in low pH, chemical resistance to oxidizers, and a rigid structure that minimizes swelling. These membranes are synthesized with our patented silane-based sol-gel techniques. The pore size, shape, and network structure are engineered through a molecular self-assembly process where thermodynamic driving forces are used to direct where and how pores form. Either cationic or anionic groups can be added within the membrane nanopore structure to create cation- and anion-exchange membranes. The ceramic IEMs are produced on a roll-to-roll manufacturing line with low-temperature processing. Membrane performance testing is conducted using in-house permselectivity, area-specific resistance, and ED stack testing setups. Ceramic-based IEMs show comparable performance to traditional IEMs and offer some unique advantages. Long exposure to highly acidic solutions has a negligible impact on ED performance. Additionally, we have observed stable performance in the presence of strong oxidizing agents such as hydrogen peroxide. This stability is expected, as the ceramic backbone of these materials is already in a fully oxidized state. This data suggests ceramic membranes, made using sol-gel chemistry, could be an ideal solution for acidic and/or oxidizing wastewater streams from processes such as semiconductor manufacturing and mining.

Keywords: ion exchange, membrane, silane chemistry, nanostructure, wastewater

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Authors: Bhavani Veasuvalingam

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Case-based learning (CBL) is a popular instructional method aimed to bridge theory to clinical practice. This study aims to explore CBL mixed modality curriculum in influencing students’ learning styles and strategies that support learning. An explanatory sequential mixed method study was employed with initial phase, 44-itemed Felderman’s Index of Learning Style (ILS) questionnaire employed across year one medical students (n=142) using convenience sampling to describe the preferred learning styles. The qualitative phase utilised three focus group discussions (FGD) to explore in depth on the multimodal learning style exhibited by the students. Most students preferred combination of learning stylesthat is reflective, sensing, visual and sequential i.e.: RSVISeq style (24.64%) from the ILS analysis. The frequency of learning preference from processing to understanding were well balanced, with sequential-global domain (66.2%); sensing-intuitive (59.86%), active- reflective (57%), and visual-verbal (51.41%). The qualitative data reported three major themes, namely Theme 1: CBL mixed modalities navigates learners’ learning style; Theme 2: Multimodal learners active learning strategies supports learning. Theme 3: CBL modalities facilitating theory into clinical knowledge. Both quantitative and qualitative study strongly reports the multimodal learning style of the year one medical students. Medical students utilise multimodal learning styles to attain the clinical knowledge when learning with CBL mixed modalities. Educators’ awareness of the multimodal learning style is crucial in delivering the CBL mixed modalities effectively, considering strategic pedagogical support students to engage and learn CBL in bridging the theoretical knowledge into clinical practice.

Keywords: case-based learning, learnign style, medical students, learning

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Authors: Gulnara Galeeva

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The Analytic Hierarchy Process is widely used in the economic and financial studies, including the formation of investment portfolios. In this study, a generalized method of obtaining a vector of priorities for the case with separate pairwise comparisons of the expert opinion being presented as a set of several equal evaluations on a ratio scale is examined. The author claims that this method allows solving an important and up-to-date problem of excluding vagueness and ambiguity of the expert opinion in the decision making theory. The study describes the authentic wide pairwise comparison matrix. Its application in the formation of the efficient investment portfolio of intangible assets of a small business enterprise with limited funding is considered. The proposed method has been successfully approbated on the practical example of a functioning dental clinic. The result of the study confirms that the wide pairwise comparison matrix can be used as a simple and reliable method for forming the enterprise investment policy. Moreover, a comparison between the method based on the wide pairwise comparison matrix and the classical analytic hierarchy process was conducted. The results of the comparative analysis confirm the correctness of the method based on the wide matrix. The application of a wide pairwise comparison matrix also allows to widely use the statistical methods of experimental data processing for obtaining the vector of priorities. A new method is available for simple users. Its application gives about the same accuracy result as that of the classical hierarchy process. Financial directors of small and medium business enterprises get an opportunity to solve the problem of companies’ investments without resorting to services of analytical agencies specializing in such studies.

Keywords: analytic hierarchy process, decision processes, investment portfolio, intangible assets

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Authors: Patricia M. B. Saint-Vincent, Anna Furches, Stephanie Galanie, Erica Teixeira Prates, Piet Jones, Nancy Engle, David Kainer, Wellington Muchero, Daniel Jacobson, Timothy J. Tschaplinski

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Uridine diphosphate-glycosyltransferases (UGTs) are enzymes that catalyze sugar transfer to a variety of plant metabolites. UGT substrates, which include plant secondary metabolites involved in lignification, demonstrate new activities and incorporation when glycosylated. Knowledge of UGT function, substrate specificity, and enzyme products is important for plant engineering efforts, especially related to increasing plant biomass through lignification. UGTs in Populus trichocarpa, a biofuel feedstock, and model woody plant, were selected from a pool of gene candidates using rapid prioritization strategies. A functional genomics workflow, consisting of a metabolite genome-wide association study (mGWAS), expression of synthetic codon-optimized genes, and high-throughput biochemical assays with mass spectrometry-based analysis, was developed for determining the substrates and products of previously-uncharacterized enzymes. A total of 40 UGTs from P. trichocarpa were profiled, and the biochemical assay results were compared to predicted mGWAS connections. Assay results confirmed seven of 11 leaf mGWAS associations and demonstrated varying levels of substrate specificity among candidate UGTs. P. trichocarpa UGT substrate processing confirms the role of these newly-characterized enzymes in lignan, flavonoid, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress responses.

Keywords: Populus, metabolite-gene associations, GWAS, bio feedstocks, glycosyltransferase

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Authors: Alafara A. Baba, Kuranga I. Ayinla, Folahan A. Adekola, Rafiu B. Bale

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Due to increasing demands and diverse applications of copper oxide as pigment in ceramics, cuprammonium hydroxide solution for rayon, p-type semi-conductor, dry cell batteries production and as safety disposal of hazardous materials, a study on the hydrometallurgical operations involving leaching, solvent extraction and precipitation for the recovery of copper for producing high grade copper oxide from a Nigerian chalcopyrite ore in chloride media has been examined. At a particular set of experimental parameter with respect to acid concentration, reaction temperature and particle size, the leaching investigation showed that the ore dissolution increases with increasing acid concentration, temperature and decreasing particle diameter at a moderate stirring. The kinetics data has been analyzed and was found to follow diffusion control mechanism. At optimal conditions, the extent of ore dissolution reached 94.3%. The recovery of the total copper from the hydrochloric acid-leached chalcopyrite ore was undertaken by solvent extraction and precipitation techniques, prior to the beneficiation of the purified solution as copper oxide. The purification of the leach liquor was firstly done by precipitation of total iron and manganese using Ca(OH)2 and H2O2 as oxidizer at pH 3.5 and 4.25, respectively. An extraction efficiency of 97.3% total copper was obtained by 0.2 mol/L Dithizone in kerosene at 25±2ºC within 40 minutes, from which ≈98% Cu from loaded organic phase was successfully stripped by 0.1 mol/L HCl solution. The beneficiation of the recovered pure copper solution was carried out by crystallization through alkali addition followed by calcination at 600ºC to obtain high grade copper oxide (Tenorite, CuO: 05-0661). Finally, a simple hydrometallurgical scheme for the operational extraction procedure amenable for industrial utilization and economic sustainability was provided.

Keywords: chalcopyrite ore, Nigeria, copper, copper oxide, solvent extraction

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Authors: Kateryna Zharan, Jan C. Bongaerts

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In recent years, economic attractiveness of renewable energy (RE) for the mining industry, especially for off-grid mines, and a negative environmental impact of fossil energy are stimulating to use RE for mining needs. Being that remote area mines have higher energy expenses than mines connected to a grid, integration of RE may give a mine economic benefits. Regarding the literature review, there is a lack of business models for adopting of RE at mine. The main aim of this paper is to develop an optimized model of RE integration into the German mining industry (GMI). Hereby, the GMI with amount of around 800 mill. t. annually extracted resources is included in the list of the 15 major mining country in the world. Accordingly, the mining potential of Germany is evaluated in this paper as a perspective market for RE implementation. The GMI has been classified in order to find out the location of resources, quantity and types of the mines, amount of extracted resources, and access of the mines to the energy resources. Additionally, weather conditions have been analyzed in order to figure out where wind and solar generation technologies can be integrated into a mine with the highest efficiency. Despite the fact that the electricity demand of the GMI is almost completely covered by a grid connection, the hybrid energy system (HES) based on a mix of RE and fossil energy is developed due to show environmental and economic benefits. The HES for the GMI consolidates a combination of wind turbine, solar PV, battery and diesel generation. The model has been calculated using the HOMER software. Furthermore, the demonstrated HES contains a forecasting model that predicts solar and wind generation in advance. The main result from the HES such as CO2 emission reduction is estimated in order to make the mining processing more environmental friendly.

Keywords: diesel generation, German mining industry, hybrid energy system, hybrid optimization model for electric renewables, optimized model, renewable energy

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Authors: Shang-Yu Chiang, Yu-Shan Tsai, Shih-Hsien Sung, Chung-Ming Lo

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Mitral regurgitation (MR) is a heart disorder which the mitral valve does not close properly when the heart pumps out blood. MR is the most common form of valvular heart disease in the adult population. The diagnostic echocardiographic finding of MR is straightforward due to the well-known clinical evidence. In the determination of MR severity, quantification of sonographic findings would be useful for clinical decision making. Clinically, the vena contracta is a standard for MR evaluation. Vena contracta is the point in a blood stream where the diameter of the stream is the least, and the velocity is the maximum. The quantification of vena contracta, i.e. the vena contracta width (VCW) at mitral valve, can be a numeric measurement for severity assessment. However, manually delineating the VCW may not accurate enough. The result highly depends on the operator experience. Therefore, this study proposed an automatic method to quantify VCW to evaluate MR severity. Based on color Doppler ultrasound, VCW can be observed from the blood flows to the probe as the appearance of red or yellow area. The corresponding brightness represents the value of the flow rate. In the experiment, colors were firstly transformed into HSV (hue, saturation and value) to be closely align with the way human vision perceives red and yellow. Using ellipse to fit the high flow rate area in left atrium, the angle between the mitral valve and the ultrasound probe was calculated to get the vertical shortest diameter as the VCW. Taking the manual measurement as the standard, the method achieved only 0.02 (0.38 vs. 0.36) to 0.03 (0.42 vs. 0.45) cm differences. The result showed that the proposed automatic VCW extraction can be efficient and accurate for clinical use. The process also has the potential to reduce intra- or inter-observer variability at measuring subtle distances.

Keywords: mitral regurgitation, vena contracta, color doppler, image processing

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Othman Maklouf, Abdunnaser Tresh

Abstract:

Land vehicle navigation is subject of great interest today. Global Positioning System (GPS) is the main navigation system for positioning in such systems. GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation (INS) is the implementation of inertial sensors to determine the position and orientation of a vehicle. The availability of low-cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop INS using an inertial measurement unit (IMU). INS has unbounded error growth since the error accumulates at each step. Usually, GPS and INS are integrated with a loosely coupled scheme. With the development of low-cost, MEMS inertial sensors and GPS technology, integrated INS/GPS systems are beginning to meet the growing demands of lower cost, smaller size, and seamless navigation solutions for land vehicles. Although MEMS inertial sensors are very inexpensive compared to conventional sensors, their cost (especially MEMS gyros) is still not acceptable for many low-end civilian applications (for example, commercial car navigation or personal location systems). An efficient way to reduce the expense of these systems is to reduce the number of gyros and accelerometers, therefore, to use a partial IMU (ParIMU) configuration. For land vehicular use, the most important gyroscope is the vertical gyro that senses the heading of the vehicle and two horizontal accelerometers for determining the velocity of the vehicle. This paper presents a field experiment for a low-cost strap down (ParIMU)\GPS combination, with data post processing for the determination of 2-D components of position (trajectory), velocity and heading. In the present approach, we have neglected earth rotation and gravity variations, because of the poor gyroscope sensitivities of our low-cost IMU (Inertial Measurement Unit) and because of the relatively small area of the trajectory.

Keywords: GPS, IMU, Kalman filter, materials engineering

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Authors: Arijit Saha, Hassan Kassem, Leo Hoening

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Ongoing climate change demands the increasing use of renewable energies. Wind energy plays an important role in this context since it can be applied almost everywhere in the world. To reduce the costs of wind turbines and to make them more competitive, simulations are very important since experiments are often too costly if at all possible. The wind turbine on a vast open area experiences the turbulence generated due to the atmosphere, so it was of utmost interest from this research point of view to generate the turbulence through various Inlet Turbulence Generation methods like Precursor cyclic and Kaimal Spectrum Exponential Coherence (KSEC) in the computational simulation domain. To be able to validate computational fluid dynamic simulations of wind turbines with the experimental data, it is crucial to set up the conditions in the simulation as close to reality as possible. This present work, therefore, aims at investigating the turbulent inflow strategy and boundary conditions of KSEC and providing a comparative analysis alongside the Precursor cyclic method for Large Eddy Simulation within the context of wind energy applications. For the generation of the turbulent box through KSEC method, firstly, the constrained data were collected from an auxiliary channel flow, and later processing was performed with the open-source tool PyconTurb, whereas for the precursor cyclic, only the data from the auxiliary channel were sufficient. The functionality of these methods was studied through various statistical properties such as variance, turbulent intensity, etc with respect to different Bulk Reynolds numbers, and a conclusion was drawn on the feasibility of KSEC method. Furthermore, it was found necessary to verify the obtained data with DNS case setup for its applicability to use it as a real field CFD simulation.

Keywords: Inlet Turbulence Generation, CFD, precursor cyclic, KSEC, large Eddy simulation, PyconTurb

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