Search results for: hybrid fuzzy weighted k-nearest neighbor

Authors: Amir Shariffar, Haider Salman, Tanveer Siddique, Omar Manasreh

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According to the recent studies on metal-oxide memristors, researchers tend to improve the stability, endurance, and uniformity of resistive switching (RS) behavior in memristors. Specifically, the main challenge is to prevent abrupt ruptures in the memristor’s filament during the RS process. To address this problem, we are proposing a low-cost hybrid structure of metal oxide quantum dots (QDs) and thin films to control the formation of filaments in memristors. We aim to use metal oxide quantum dots because of their unique electronic properties and quantum confinement, which may improve the resistive switching behavior. QDs have discrete energy spectra due to electron confinement in three-dimensional space. Because of Coulomb repulsion between electrons, only a few free electrons are contained in a quantum dot. This fact might guide the growth direction for the conducting filaments in the metal oxide memristor. As a result, it is expected that QDs can improve the endurance and uniformity of RS behavior in memristors. Moreover, we use a hybrid structure of intrinsic n-type quantum dots and p-type thin films to introduce a potential barrier at the junction that can smooth the transition between high and low resistance states. A bottom-up approach is used for fabricating the proposed memristor using different types of metal-oxide QDs and thin films. We synthesize QDs including, zinc oxide, molybdenum trioxide, and nickel oxide combined with spin-coated thin films of titanium dioxide, copper oxide, and hafnium dioxide. We employ fluorine-doped tin oxide (FTO) coated glass as the substrate for deposition and bottom electrode. Then, the active layer composed of one type of quantum dots, and the opposite type of thin films is spin-coated onto the FTO. Lastly, circular gold electrodes are deposited with a shadow mask by using electron-beam (e-beam) evaporation at room temperature. The fabricated devices are characterized using a probe station with a semiconductor parameter analyzer. The current-voltage (I-V) characterization is analyzed for each device to determine the conduction mechanism. We evaluate the memristor’s performance in terms of stability, endurance, and retention time to identify the optimal memristive structure. Finally, we assess the proposed hypothesis before we proceed to the optimization process for fabricating the memristor.

Keywords: memristor, quantum dot, resistive switching, thin film

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Authors: Ayman El Sabagh, Celaleddin Barutçular, Hirofumi Saneoka

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Drought is one of the most serious problems posing a grave threat to cereals production including maize. Maize improvement in drought-stress tolerance poses a great challenge as the global need for food and bio-enegry increases. Thus, the current study was planned to explore the variations and determine the performance of target traits of maize hybrids at grain growth stage under drought conditions during 2014 under Adana, Mediterranean climate conditions, Turkey. Maize hybrids (Sancia, Indaco, 71May69, Aaccel, Calgary, 70May82, 72May80) were evaluated under (irrigated and water stress). Results revealed that, grain yield and yield traits had a negative effects because of water stress conditions compared with the normal irrigation. As well as, based on the result under normal irrigation, the maximum biological yield and harvest index were recorded. According to the differences among hybrids were found that, significant differences were observed among hybrids with respect to yield and yield traits under current research. Based on the results, grain weight had more effect on grain yield than grain number during grain filling growth stage under water stress conditions. In this concern, according to low drought susceptibility index (less grain yield losses), the hybrid (Indaco) was more stable in grain number and grain weight. Consequently, it may be concluded that this hybrid would be recommended for use in the future breeding programs for production of drought tolerant hybrids.

Keywords: drought susceptibility index, grain growth, grain yield, maize, water stress

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Authors: Aries Nawel, Bentarzi Mohamed

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This paper deals with the study of some probabilistic and statistical properties of a Periodic Integer-Valued Moving Average Model (PINMA_{S}(q)). The closed forms of the mean, the second moment and the periodic autocovariance function are obtained. Furthermore, the time reversibility of the model is discussed in details. Moreover, the estimation of the underlying parameters are obtained by the Yule-Walker method, the Conditional Least Square method (CLS) and the Weighted Conditional Least Square method (WCLS). A simulation study is carried out to evaluate the performance of the estimation method. Moreover, an application on real data set is provided.

Keywords: periodic integer-valued moving average, periodically correlated process, time reversibility, count data

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Authors: Gintarė Sauliutė, Gintaras Svecevičius

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Landfill leachates contain a number of persistent pollutants, including heavy metals. They have the ability to spread in ecosystems and accumulate in fish which most of them are classified as top-consumers of trophic chains. Fish are freely swimming organisms; but perhaps, due to their species-specific ecological and behavioral properties, they often prefer the most suitable biotopes and therefore, did not avoid harmful substances or environments. That is why it is necessary to evaluate the persistent pollutant dispersion in hydroecosystem using fish tissue metal concentration. In hydroecosystems of hybrid type (e.g. river-pond-river) the distance from the pollution source could be a perfect indicator of such a kind of metal distribution. The studies were carried out in the Kairiai landfill neighboring hybrid-type ecosystem which is located 5 km east of the Šiauliai City. Fish tissue (gills, liver, and muscle) metal concentration measurements were performed on two types of ecologically-different fishes according to their feeding characteristics: benthophagous (Gibel carp, roach) and predatory (Northern pike, perch). A number of mathematical models (linear, non-linear, using log and other transformations) have been applied in order to identify the most satisfactorily description of the interdependence between fish tissue metal concentration and the distance from the pollution source. However, the only one log-multiple regression model revealed the pattern that the distance from the pollution source is closely and positively correlated with metal concentration in all predatory fish tissues studied (gills, liver, and muscle).

Keywords: bioaccumulation in fish, heavy metals, hydroecosystem, landfill leachate, mathematical model

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Authors: Hexiong Liu

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Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Abhishek N. Srivastava, Rahul Singh, Sumedha Chakma

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The rapid urbanization in the developing countries is generating an enormous amount of waste leading to the creation of unregulated landfill sites at various places at its disposal. The liquid waste, known as leachate, produced from these landfills sites is severely affecting the surrounding water quality. The water quality in the proximity areas of the landfill is found affected by various physico-chemical parameters of leachate such as pH, alkalinity, total hardness, conductivity, chloride, total dissolved solids (TDS), total suspended solids (TSS), sulphate, nitrate, phosphate, fluoride, sodium and potassium, biological parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), Faecal coliform, and heavy metals such as cadmium (Cd), lead (Pb), iron (Fe), mercury (Hg), arsenic (As), cobalt (Co), manganese (Mn), zinc (Zn), copper (Cu), chromium (Cr), nickel (Ni). However, all these parameters are distributive in leachate that produced according to the nature of waste being dumped at various landfill sites, therefore, it becomes very difficult to predict the main responsible parameter of leachate for water quality contamination. The present study is endeavour the comparative analysis of the physical, chemical and biological parameters of various landfills in India viz. Okhla landfill, Ghazipur landfill, Bhalswa ladfill in NCR Delhi, Deonar landfill in Mumbai, Dhapa landfill in Kolkata and Kodungayaiyur landfill, Perungudi landfill in Chennai. The statistical analysis of the parameters was carried out using the Statistical Packages for the Social Sciences (SPSS) and LandSim 2.5 model to simulate the long term effect of various parameters on different time scale. Further, the uncertainties characterization of various input parameters has also been analysed using fuzzy alpha cut (FAC) technique to check the sensitivity of various water quality parameters at the proximity of numerous landfill sites. Finally, the study would help to suggest the best method for the prevention of pollution migration from the landfill sites on priority basis.

Keywords: landfill leachate, water quality, LandSim, fuzzy alpha cut

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Authors: Mohammed A. Al-Yafrsi, Fahed A. Al-Mana

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A study was conducted to evaluate the compaction-tolerance ability of some warm season turfgrasses under shade and sunlight conditions in Riyadh, Saudi Arabia. Hybrid bermudagrass (Cynodon dactylon): 'Tifway' and 'Tifsport', seashore paspalum (Paspalum vaginatum) and its cultivar 'Sea Isle 2000' were used. The study area was divided into two sections where one was exposed to sunlight and the other one was maintained under shade using green plastic grille (shade 70%). Turfgrasses were planted by sods in beds containing a mixture of sand, silt, and peat moss (4: 1: 1, v/v). The soil compaction was applied using a locally-made cylindrical roll (weighing 250 kg), passing four times over the growing turfgrasses for 3 days/week. The results revealed that compaction treatment led to a decrease in grass height, and it was the lowest (4.0 cm) for paspalum 'Sea Isle 2000' in February. At the shaded area, paspalum turfgrasses retained its high quality degree (4.0) in April, May, and June. In the sunlight area, the grass quality degree was the greatest (4.0) in 'Sea Isle 2000' and the lowest (3.0) in 'Tifsport'. Paspalum turfgrasses gave higher color degree (4) than bermuda grasses (2.5) in April, May, and June. The compaction also led to a decline in leaf area, fresh and dry weights of all grown turfgrasses. The grass density was high for paspalum turfgrasses indicating that their resistance to compaction was greater than bermudagrasses. It can be concluded that the best compaction and shade tolerant turfgrasses are 'Sea Isle 2000' and seashore paspalum.

Keywords: hybrid bermudagrass, seashore paspalum, soil compaction, shade area, sunlight condition

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Authors: Zhu-Qing Jia, Tao Lin, Tong Zhou

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The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons.

Keywords: optical character recognition, fuzzy page identification, mutual correlation matrix, confidence self-adaptation

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Authors: Hatem Hajri, Mohamed-Cherif Rahal

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Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth.

Keywords: ground truth, Hungarian algorithm, lidar Radar data fusion, global nearest neighbor filter

Procedia PDF Downloads 171

Authors: Abbas Hani, Maryam Jassasizadeh

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The concentrations of Cd, Pb, and Ni were determined from 40 soil samples collected in surface soils of Khark Island. Geostatistic methods and GIS were used to identify heavy metal sources and their spatial pattern. Principal component analysis coupled with correlation between heavy metals showed that level of mentioned heavy metal was lower than the standard level. Then the data obtained from the soil analyzing were studied for the purposes of normal distribution. The best way of interior finding for cadmium and nickel was ordinary kriging and the best way of interpolation of lead was inverse distance weighted. The result of this study help us to understand heavy metals distribution and make decision for remediation of soil pollution.

Keywords: geostatistics, ordinary kriging, heavy metals, GIS, Khark

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Danni Cheng

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Background: Preclinical and epidemiological studies have reported potential protective effects of low-density lipoprotein cholesterol (LDL-C) lowering drugs on head and neck squamous cell cancer (HNSCC) survival, but the causality was not consistent. Genetic variants associated with LDL-C lowering drug targets can predict the effects of their therapeutic inhibition on disease outcomes. Objective: We aimed to evaluate the causal association of genetically proxied cholesterol-lowering drug targets and circulating lipid traits with cancer survival in HNSCC patients stratified by human papillomavirus (HPV) status using two-sample Mendelian randomization (MR) analyses. Method: Single-nucleotide polymorphisms (SNPs) in gene region of LDL-C lowering drug targets (HMGCR, NPC1L1, CETP, PCSK9, and LDLR) associated with LDL-C levels in genome-wide association study (GWAS) from the Global Lipids Genetics Consortium (GLGC) were used to proxy LDL-C lowering drug action. SNPs proxy circulating lipids (LDL-C, HDL-C, total cholesterol, triglycerides, apoprotein A and apoprotein B) were also derived from the GLGC data. Genetic associations of these SNPs and cancer survivals were derived from 1,120 HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) and 2,570 non-HPV-driven HNSCC patients in VOYAGER program. We estimated the causal associations of LDL-C lowering drugs and circulating lipids with HNSCC survival using the inverse-variance weighted method. Results: Genetically proxied HMGCR inhibition was significantly associated with worse overall survival (OS) in non-HPV-drive HNSCC patients (inverse variance-weighted hazard ratio (HR IVW), 2.64[95%CI,1.28-5.43]; P = 0.01) but better OS in HPV-positive OPSCC patients (HR IVW,0.11[95%CI,0.02-0.56]; P = 0.01). Estimates for NPC1L1 were strongly associated with worse OS in both total HNSCC (HR IVW,4.17[95%CI,1.06-16.36]; P = 0.04) and non-HPV-driven HNSCC patients (HR IVW,7.33[95%CI,1.63-32.97]; P = 0.01). A similar result was found that genetically proxied PSCK9 inhibitors were significantly associated with poor OS in non-HPV-driven HNSCC (HR IVW,1.56[95%CI,1.02 to 2.39]). Conclusion: Genetically proxied long-term HMGCR inhibition was significantly associated with decreased OS in non-HPV-driven HNSCC and increased OS in HPV-positive OPSCC. While genetically proxied NPC1L1 and PCSK9 had associations with worse OS in total and non-HPV-driven HNSCC patients. Further research is needed to understand whether these drugs have consistent associations with head and neck tumor outcomes.

Keywords: Mendelian randomization analysis, head and neck cancer, cancer survival, cholesterol, statin

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Authors: Stefan Peters, Phillip Roetman

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The global loss of biodiversity is threatening the benefits nature provides to human populations and has become a more pressing issue than climate change and requires immediate attention. While there have been successful global agreements for environmental protection, such as the Montreal Protocol, these are rare, and we cannot rely on them solely. Thus, it is crucial to take national and local actions to support biodiversity. Australia is one of the 17 countries in the world with a high level of biodiversity, and its cities are vital habitats for endangered species, with more of them found in urban areas than in non-urban ones. However, the protection of biodiversity in metropolitan Adelaide has been inadequate, with over 130 species disappearing since European colonization in 1836. In this research project we conceptualized, developed and implemented a framework for wildlife Habitat Hotspots and Habitat Corridor modelling in an urban context using geographic data and GIS modelling and analysis. We used detailed topographic and other geographic data provided by a local council, including spatial and attributive properties of trees, parcels, water features, vegetated areas, roads, verges, traffic, and census data. Weighted factors considered in our raster-based Habitat Hotspot model include parcel size, parcel shape, population density, canopy cover, habitat quality and proximity to habitats and water features. Weighted factors considered in our raster-based Habitat Corridor model include habitat potential (resulting from the Habitat Hotspot model), verge size, road hierarchy, road widths, human density, and presence of remnant indigenous vegetation species. We developed a GIS model, using Python scripting and ArcGIS-Pro Model-Builder, to establish an automated reproducible and adjustable geoprocessing workflow, adaptable to any study area of interest. Our habitat hotspot and corridor modelling framework allow to determine and map existing habitat hotspots and wildlife habitat corridors. Our research had been applied to the study case of Burnside, a local council in Adelaide, Australia, which encompass an area of 30 km2. We applied end-user expertise-based category weightings to refine our models and optimize the use of our habitat map outputs towards informing local strategic decision-making.

Keywords: biodiversity, GIS modeling, habitat hotspot, wildlife corridor

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Authors: Jukka Heinonen

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Higher density reduces distances, private car dependency and thus reduces greenhouse gas emissions (GHGs). As a result, increased density has been given a central role among urban development targets. However, it is not just travel behavior that changes along with density. Rather, the consumption patterns, or overall lifestyles, change along with changing urban structure, particularly with changing housing types and consumption opportunities. Furthermore, elevated consumption of services, more frequent flying and less intra-household sharing have been shown to potentially outweigh the gains from reduced driving in more dense urban settlements. In this study, the geography of carbon footprints (CFs) in California is analyzed paying close attention to the household size differences and the resulting economies-of-scale advantages and disadvantages. A hybrid life cycle assessment (LCA) framework is employed together with consumer expenditure data to assess the CFs. According to the study, small urban households have the highest CFs in California. Their transport related emissions are significantly lower than those of the residents of less urbanized areas, but higher emissions from other consumption categories, together with the low degree of sharing of goods, overweigh the gains. Two functional units, per capita and per household, are used to analyze the CFs and to demonstrate the importance of household size. The lifestyle impacts visible through the consumption data are also discussed. The study suggests that there are still significant gaps in our understanding of the premises of low-carbon human settlements.

Keywords: carbon footprint, life cycle assessment, lifestyle, household size, consumption, economies-of-scale

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Authors: Younis Jabarzadeh, Rahim Sarvari, Negar Ahmadi Alghalandis

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Entrepreneurship at both individual and organizational level is one of the most driving forces in economic development and leads to growth and competition, job generation and social development. Especially in developing countries, the role of entrepreneurship in economic and social prosperity is more emphasized. But the effect of global economic development on the environment is undeniable, especially in negative ways, and there is a need to rethink current business models and the way entrepreneurs act to introduce new businesses to address and embed environmental issues in order to achieve sustainable development. In this paper, green or sustainable entrepreneurship is addressed in Iran to identify challenges and barriers entrepreneurs in the economic and social sectors face in developing green business solutions. Sustainable or green entrepreneurship has been gaining interest among scholars in recent years and addressing its challenges and barriers need much more attention to fill the gap in the literature and facilitate the way those entrepreneurs are pursuing. This research comprised of two main phases: qualitative and quantitative. At qualitative phase, after a thorough literature review, fuzzy Delphi method is utilized to verify those challenges and barriers by gathering a panel of experts and surveying them. In this phase, several other contextually related factors were added to the list of identified barriers and challenges mentioned in the literature. Then, at the quantitative phase, Interpretive Structural Modeling is applied to construct a network of interactions among those barriers identified at the previous phase. Again, a panel of subject matter experts comprised of academic and industry experts was surveyed. The results of this study can be used by policymakers in both the public and industry sector, to introduce more systematic solutions to eliminate those barriers and help entrepreneurs overcome challenges of sustainable entrepreneurship. It also contributes to the literature as the first research in this type which deals with the barriers of sustainable entrepreneurship and explores their interaction.

Keywords: green entrepreneurship, barriers, fuzzy Delphi method, interpretive structural modeling

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

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Authors: Natalie Rinfret, Francine Tougas, Ann Beaton

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Conventions No. 100 and 111 of the International Labor Organization, passed in 1951 and 1958 respectively, established the principles of equal pay for men and women for work of equal value and freedom from discrimination in employment. Governments of different countries followed suit. For example, in 1964, the Civil Rights Act was passed in the United States and in 1972, Canada ratified Convention 100. Thus, laws were enacted and programs were implemented to combat discrimination in the workplace and, over time, more than 90% of the member countries of the International Labour Organization have ratified these conventions by implementing programs such as employment equity in Canada aimed at groups recognized as being discriminated against in the labor market, including women. Although legislation has been in place for several decades, employment discrimination has not gone away. In this study, we pay particular attention to the hidden side of the effects of employment discrimination. This is the emergence of subtle forms of discrimination that often fly under the radar but nevertheless, have adverse effects on the attitudes and behaviors of members of targeted groups. Researchers have identified two forms of racial and gender bias. On the one hand, there are traditional prejudices referring to beliefs about the inferiority and innate differences of women and racial minorities compared to White men. They have the effect of confining these two groups to job categories suited to their perceived limited abilities and can result in degrading, if not violent and hateful, language and actions. On the other hand, more subtle prejudices are more suited to current social norms. However, this subtlety harbors a conflict between values of equality and remnants of negative beliefs and feelings toward women and racial minorities. Our literature review also takes into account an overlooked part of the groups targeted by the programs in place, senior workers, and highlights the quantifiable and observable effects of prejudice and discriminatory behaviors in employment. The study proposes a hybrid model of interventions, taking into account the organizational system (employment equity practices), discriminatory attitudes and behaviors, and the type of leadership to be advocated. This hybrid model includes, in the first instance, the implementation of initiatives aimed at both promoting employment equity and combating discrimination and, in the second instance, the establishment of practices that foster inclusion, the full and complete participation of all, including seniors, in the mission of their organization.

Keywords: employment discrimination, gender bias, the hybrid model of interventions, senior workers

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Authors: Paul Stonaha, Mariia Romashchenko, Xaio Xu

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Magnetic shape memory alloys (MSMAs) are promising technological materials for a range of fields, from biomaterials to energy harvesting. We have performed inelastic neutron scattering on two powder samples of cobalt-based high-entropy MSMAs across a range of temperatures in an effort to compare calculations of thermodynamic properties (entropy, specific heat, etc.) to the measured ones. The measurements were correct for multiphonon scattering and multiple scattering contributions. We present herein the neutron-weighted vibrational density of states. Future work will utilize DFT calculations of the disordered lattice to correct for the neutron weighting and retrieve the true thermodynamical properties.

Keywords: neutron scattering, vibrational dynamics, computational physics, material science

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Authors: T. Patcharawit, C. Kansomket, N. Wongnaree, W. Kritsrikan, T. Yingnakorn, S. Khumkoa

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Recovery of pure copper and silver from end-of-life photovoltaic panels was investigated in this paper using an effective hybrid pyro-hydrometallurgical process. In the first step of waste treatment, solar panel waste was first dismantled to obtain a PV sheet to be cut and calcined at 500°C, to separate out PV ribbon from glass cullet, ash, and volatile while the silicon wafer containing silver finger was collected for recovery. In the second step of metal recovery, copper recovery from photovoltaic ribbon was via 1-3 M HCl leaching with SnCl₂ and H₂O₂ additions in order to remove the tin-lead coating on the ribbon. The leached copper band was cleaned and subsequently melted as an anode for the next step of electrorefining. Stainless steel was set as the cathode with CuSO₄ as an electrolyte, and at a potential of 0.2 V, high purity copper of 99.93% was obtained at 96.11% recovery after 24 hours. For silver recovery, the silicon wafer containing silver finger was leached using HNO₃ at 1-4 M in an ultrasonic bath. In the next step of precipitation, silver chloride was then obtained and subsequently reduced by sucrose and NaOH to give silver powder prior to oxy-acetylene melting to finally obtain pure silver metal. The integrated recycling process is considered to be economical, providing effective recovery of high purity metals such as copper and silver while other materials such as aluminum, copper wire, glass cullet can also be recovered to be reused commercially. Compounds such as PbCl₂ and SnO₂ obtained can also be recovered to enter the market.

Keywords: electrorefining, leaching, calcination, PV ribbon, silver finger, solar panel

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Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

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Authors: S. Jalilzadeh, S. M. Mohseni Bonab

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Solar and battery energy storage systems are very useful for consumers who live in deprived areas and do not have access to electricity distribution networks. Nowadays one of the problems that photo voltaic systems (PV) have changing of output power in temperature and irradiance variations, which directly affects the load that is connected to photo voltaic systems. In this paper, with considering the fact that the solar array varies with change in temperature and solar power radiation, a voltage stabilizer system of a load connected to photo voltaic array is designed to stabilize the load voltage and to transfer surplus power of the battery. Also, in proposed hybrid system, the needed load power amount is supplemented considering the voltage stabilization in standalone operation for supplying unbalanced AC load. Electrical energy storage system for voltage control and improvement of the performance of PV by a DC/DC converter is connected to the DC bus. The load is also feed by an AC/DC converter. In this paper, when the voltage increases in its reference limit, the battery gets charged by the photo voltaic array and when it decreases in its defined limit, the power gets injected to the DC bus by this battery. The constant of DC bus Voltage is the cause for the reduced harmonics generated by the inverter. In addition, a series of filters are provided in the inverter output in to reduced harmonics. The inverter control circuit is designed that the voltage and frequency of the load remain almost constant at different load conditions. This paper has focused on controlling strategies of converters to improve their performance.

Keywords: photovoltaic array (PV), DC/DC Boost converter, battery converter, inverters control

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Authors: Velázquez Alejandro, Pradhan Sujan, Yoon Rokhyun, Sanada Yasushi

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The utilization of new materials as an alternative solution to decrease the environmental impact of the construction industry has been gaining more relevance in the architectural design and construction industry. One such material is cross-laminated timber (CLT), an engineered timber solution that excels for its faster construction times, workability, lightweight, and capacity for carbon storage. This material is usually used alone for the entire structure or combined with steel frames, but a hybrid with reinforced concrete (RC) is rarer. Since RC is one of the most used materials worldwide, a hybrid with CLT would allow further utilization of the latter, and in the process, it would help reduce the environmental impact of RC construction to achieve a sustainable society, but first, the structural performance of such hybrids must be understood. This paper focuses on proposing a model to predict the seismic performance of RC frames with CLT panels as infills. A series of static horizontal cyclic loading experiments were conducted on two 40% scale specimens of reinforced concrete frames with and without CLT panels at Osaka University, Japan. An analytical model was created to simulate the seismic performance of the RC frame with CLT infill based on the experimental results. The proposed model was verified by comparing the experimental and analytical results, showing that the load-deformation relationship and the failure mechanism agreed well with limited error. Hence, the proposed analytical model can be implemented for the seismic performance evaluation of the RC frames with CLT infill.

Keywords: analytical model, multi spring, performance evaluation, reinforced concrete, rocking mechanism, wooden wall

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Authors: Ibrahim Ilker Ozyigit, Ertugrul Filiz, Ilhan Dogan

Abstract:

An in silico analysis of Brachypodium distachyon, Triticum aestivum, Festuca arundinacea, Lolium perenne, Hordeum vulgare subsp. vulgare of the Pooideaea was performed based on complete chloroplast genomes including rbcL coding and trnH-psbA intergenic spacer regions alone to compare phylogenetic resolving power. Neighbor-joining, Minimum Evolution, and Unweighted Pair Group Method with arithmetic mean methods were used to reconstruct phylogenies with the highest bootstrap supported the obtained data from whole chloroplast genome sequence. The highest and lowest values from nucleotide diversity (π) analysis were found to be 0.315813 and 0.043495 in rbcL coding region in chloroplast genome and complete chloroplast genome, respectively. The highest transition/transversion bias (R) value was recorded as 1.384 in complete chloroplast genomes. F. arudinacea-L. perenne clade was uncovered in all phylogenies. Sequences of rbcL and trnH-psbA regions were not able to resolve the Pooideae phylogenies due to lack of genetic variation.

Keywords: chloroplast DNA, Pooideae, phylogenetic analysis, rbcL, trnH-psbA

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

Abstract:

The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Nargis Ali

Abstract:

Since the modest fashion market is growing in the UK, there is still little understanding and more concerns found among researchers and marketers about Muslim consumers. Therefore, the present study is designed to explore critical factors influencing Muslim women’s intention to purchase clothing and to identify the differences in the purchase intention of ethnic minority groups in the UK. The conceptual framework is designed using the theory of planned behavior and social identity theory. In order to satisfy the research objectives, a structured online questionnaire was published on Facebook from 20 November to 21 March. As a result, 1087 usable questionnaires were received and used to assess the proposed model fit through structural equation modeling. Results revealed that social media does influence the purchase intention of Muslim women. Muslim women search for stylish clothes that provide comfort during summer while they prefer soft and subdued colors. Furthermore, religious knowledge and religious practice, and fashion uniqueness strongly influence their purchase intention, while hybrid identity is negatively related to the purchase intention of Muslim women. This research contributes to the literature linked to Muslim consumers at a time when the UK's large retailers were seeking to attract Muslim consumers through modestly designed outfits. Besides, it will be helpful to formulate or revise product and marketing strategies according to UK’s Muslim women’s tastes and needs.

Keywords: fashion uniqueness, hybrid identity, religiosity, social media, social identity theory, structural equation modeling, theory of planned behavior

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Authors: Dimitra Alexiou, Stefanos Katsavounis, Ria Kalfakakou

Abstract:

In an urban area the determination of transportation routes should be planned so as to minimize the provoked pollution taking into account the cost of such routes. In the sequel these routes are cited as pollution routes. The transportation network is expressed by a weighted graph G= (V, E, D, P) where every vertex represents a location to be served and E contains unordered pairs (edges) of elements in V that indicate a simple road. The distances/cost and a weight that depict the provoked air pollution by a vehicle transition at every road are assigned to each road as well. These are the items of set D and P respectively. Furthermore the investigated pollution routes must not exceed predefined corresponding values concerning the route cost and the route pollution level during the vehicle transition. In this paper we present an algorithm that generates such routes in order that the decision maker selects the most appropriate one.

Keywords: bi-criteria, pollution, shortest paths, computation

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Authors: Mohammad Mahdi Hajiali

Abstract:

Many of construction projects estimated in Iran have been influenced by the limitations of financial resources. As for Iran, a country that is developing, and to follow this development-oriented approach which many numbers of projects each year run in, if we can reduce the cost of projects by applying a method we will help greatly to minimize the cost of major construction projects and therefore projects will finish faster and more efficiently. One of the components of transportation infrastructure are roads that are considered to have a considerable share of the country budget. In addition, major budget of the related ministry is spending to repair, improve and maintain roads. Value Engineering is a simple and powerful methodology over the past six decades that has been successful in reducing the cost of many projects. Specific solution for using value engineering in the stage of project implementation is called value engineering change proposal (VECP). It was tried in this research to apply VECP in one of the road-building projects in Iran in order to enhance the value of this kind of projects and reduce their cost. In this case study after applying VECP, an idea was raised. It was about use of concrete pavement instead of hot mixed asphalt (HMA) and also using fiber in order to improve concrete pavement performance. VE group team made a decision that for choosing the best alternatives, get expert’s opinions in pavement systems and use Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for ranking opinions of the experts. Finally, Jointed Plain Concrete Pavement (JPCP) was selected. Group also experimented concrete samples with available fibers in Iran and the results of experiments showed a significant increment in concrete specifications such as flexural strength. In the end, it was shown that by using of fiber-reinforced concrete pavement instead of asphalt pavement, we can achieve a significant saving in cost, time and also increment in quality, durability, and longevity.

Keywords: road-building projects, value engineering change proposal (VECP), Jointed Plain Concrete Pavement (JPCP), Fuzzy TOPSIS, fiber-reinforced concrete

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Authors: Yuzhi Cai

Abstract:

In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible.

Keywords: combining forecasts, MCMC, predictive density functions, quantile forecasting, quantile modelling

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Authors: Ren Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

Abstract:

Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption

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Authors: Raouf Alizadeh, Kadijeh Hemmati

Abstract:

The aim of this study is to synthesize several series of hydrogels from combination of a natural based polymer (Quince seed mucilage QSD), a synthetic copolymer contained methoxy poly ethylene glycol -polycaprolactone (mPEG-PCL) in the presence of different amount of multi-walled carbon nanotube (f-MWNT). Mono epoxide functionalized mPEG (mP EG-EP) was synthesized and reacted with sodium azide in the presence of NH4Cl to afford mPEG- N3(-OH). Then ring opening polymerization (ROP) of ε–caprolactone (CL) in the presence of mPEG- N3(-OH) as initiator and Sn(Oct)2 as catalyst led to preparation of mPEG-PCL- N3(-OH ) which was grafted onto propagylated f-MWNT by the click reaction to obtain mPEG-PCL- f-MWNT (-OH ). In the presence of mPEG- N3(-Br) and mixture of NHS/DCC/ QSD, hybrid hydrogels were successfully synthesized. The copolymers and hydrogels were characterized using different techniques such as, scanning electron microscope (SEM) and thermogravimetric analysis (TGA). The gel content of hydrogels showed dependence on the weight ratio of QSD:mPEG-PCL:f-MWNT. The swelling behavior of the prepared hydrogels was also studied under variation of pH, immersion time, and temperature. According to the results, the swelling behavior of the prepared hydrogels showed significant dependence in the gel content, pH, immersion time and temperature. The highest swelling was observed at room temperature, in 60 min and at pH 8. The loading and in-vitro release of quercetin as a model drug were investigated at pH of 2.2 and 7.4, and the results showed that release rate at pH 7.4 was faster than that at pH 2.2. The total loading and release showed dependence on the network structure of hydrogels and were in the range of 65- 91%. In addition, the cytotoxicity and release kinetics of the prepared hydrogels were also investigated.

Keywords: antioxidant, drug delivery, Quince Seed Mucilage(QSD), swelling behavior

Procedia PDF Downloads 320