Search results for: grey clustering

Authors: Chuan Yang, Jing Bie, Zhong Wang, Panagiotis Psimoulis

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Contemporary studies investigating the association between the spatial configuration of the urban network and economic activities at the street level were mostly conducted within space syntax conceptual framework. These findings supported the theory of 'movement economy' and demonstrated the impact of street configuration on the distribution of pedestrian movement and land-use shaping, especially retail activities. However, the effects varied between different urban contexts. In this paper, the relationship between economic activity distribution and the urban configurational characters was examined at the segment level. In the study area, three kinds of neighbourhood types, urban, suburban, and rural neighbourhood, were included. And among all neighbourhoods, three kinds of urban network form, 'tree-like', grid, and organic pattern, were recognised. To investigate the nested effects of urban configuration measured by space syntax approach and urban context, multilevel zero-inflated negative binomial (ZINB) regression models were constructed. Additionally, considering the spatial autocorrelation, spatial lag was also concluded in the model as an independent variable. The random effect ZINB model shows superiority over the ZINB model or multilevel linear (ML) model in the explanation of economic activities pattern shaping over the urban environment. And after adjusting for the neighbourhood type and network form effects, connectivity and syntax centrality significantly affect economic activities clustering. The comparison between accumulative and new established economic activities illustrated the different preferences for economic activity location choice.

Keywords: space syntax, economic activities, multilevel model, Chinese city

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Authors: Faisal Nouroz, Mukaramin Mukaramin

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Of the transposable elements (TEs), the retrotransposons are the most copious elements identified from many sequenced genomes. They have played a major role in genome evolution, rearrangement, and expansions based on their copy and paste mode of proliferation. They are further divided into LTR and Non-LTR retrotransposons. The purpose of the current study was to identify the LTR REs in sequenced Phoenix dactylifera genome and to study their structural diversity. A total of 150 P. dactylifera BAC sequences with > 60kb sizes were randomly retrieved from National Center for Biotechnology Information (NCBI) database and screened for the presence of LTR retrotransposons. Seven bacterial artificial chromosomes (BAC) sequences showed full-length LTR Retrotransposons with 4 Copia and 3 Gypsy families having variable copy numbers in respective families. Reverse transcriptase (RT) domain was found as the most conserved domain among Copia and Gypsy superfamilies and was used to deduce evolutionary analysis. The amino acid residues among various RT sequences showed variability in their percentages indicating post divergence evolution. Amino acid Leucine was found in highest proportions followed by Lysine, while Methionine and Tryptophan were in lowest percentages. The phylogenetic analysis based on RT domains confirmed that although having most conserved RT regions, several evolutionary events occurred causing nucleotide polymorphisms and hence clustering of Gypsy and Copia superfamilies into their respective lineages. The study will be helpful in identification and annotation of these elements in other species and genera and their distribution patterns on chromosomes by fluorescent in situ hybridization techniques.

Keywords: transposable elements, Phoenix dactylifera, retrotransposons, phylogenetic analysis

Procedia PDF Downloads 128

Authors: S. S. Sharma, P. R. Prabhu, K. Jagannath, Achutha Kini U., Gowri Shankar M. C.

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In present global scenario, aluminum alloys are coining the attention of many innovators as competing structural materials for automotive and space applications. Comparing to other challenging alloys, especially, 7xxx series aluminum alloys have been studied seriously because of their benefits such as moderate strength; better deforming characteristics, excellent chemical decay resistance, and affordable cost. 7075 Al-alloys have been used in the transportation industry for the fabrication of several types of automobile parts, such as wheel covers, panels and structures. It is expected that substitution of such aluminum alloys for steels will result in great improvements in energy economy, durability and recyclability. However, it is necessary to improve the strength and the formability levels at low temperatures in aluminium alloys for still better applications. Aluminum–Zinc–Magnesium with or without other wetting agent denoted as 7XXX series alloys are medium strength heat treatable alloys. Cu, Mn and Si are the other solute elements which contribute for the improvement in mechanical properties achievable by selecting and tailoring the suitable heat treatment process. On subjecting to suitable treatments like age hardening or cold deformation assisted heat treatments, known as low temperature thermomechanical treatments (LTMT) the challenging properties might be incorporated. T6 is the age hardening or precipitation hardening process with artificial aging cycle whereas T8 comprises of LTMT treatment aged artificially with X% cold deformation. When the cold deformation is provided after solution treatment, there is increase in hardness related properties such as wear resistance, yield and ultimate strength, toughness with the expense of ductility. During precipitation hardening both hardness and strength of the samples are increasing. Decreasing peak hardness value with increasing aging temperature is the well-known behavior of age hardenable alloys. The peak hardness value is further increasing when room temperature deformation is positively supported with age hardening known as thermomechanical treatment. Considering these aspects, it is intended to perform heat treatment and evaluate hardness, tensile strength, wear resistance and distribution pattern of reinforcement in the matrix. 2 to 2.5 and 3 to 3.5 times increase in hardness is reported in age hardening and LTMT treatments respectively as compared to as-cast composite. There was better distribution of reinforcements in the matrix, nearly two fold increase in strength levels and upto 5 times increase in wear resistance are also observed in the present study.

Keywords: reinforcement, precipitation, thermomechanical, dislocation, strain hardening

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni

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In this research, we analyzed the groundwater quality index in the El Milia plain, Kebir Rhumel Basin, Algeria. Thirty-three groundwater samples were collected from wells in the El Milia plain during April 2015. In this study, pH and electrical conductivity (EC) were conducted at each sampling well. Eight hydrochemical parameters such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chlorid (Cl), sulfate (SO4), bicarbonate (HCO3), and Nnitrate (NO3) were analysed. The entropy water quality index (EWQI) method was employed to evaluate the groundwater quality in the study area. Moran’s I and the ordinary kriging (OK) interpolation technique were used to examine the spatial distribution pattern of the hydrochemical parameters in the groundwater. It was found that the hydrochemical parameters Ca, Cl, and HCO3 showed strong spatial autocorrelation in the El Milia plain, indicating a spatial dependence and clustering of these parameters in the groundwater. The groundwater quality was evaluated using the entropy water quality index (EWQI). The results showed that approximately 86% of the total groundwater samples in the study area fall within the moderate groundwater quality category. The spatial map of the EWQI values indicated an increasing trend from the south-west to the northeast, following the direction of groundwater flow. The highest EWQI values were observed near El Milia city in the center of the plain. This spatial pattern suggests variations in groundwater quality across the study area, with potentially higher risks near the city center. Therefore, the results obtained in this research provide very useful information to decision-makers.

Keywords: entropy water quality index (EWQI), moran’s i, ordinary kriging interpolation, el milia plain

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: H. Hanine, H. H'ssaini, M. Ibno Alaoui, A. Nablousi, H. Zahir, S. Ennahli, H. Latrache, H. Zine Abidine

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Biochemical and pomological variability among 14 cultivars of Prunus dulcis planted in a germoplasm collection site in Morocco were evaluated. Almond samples from six local and eight foreign cultivars (France, Italy, Spain, and USA) were characterized. Biochemical and pomological data revealed significant genetic variability among the 14 cultivars; local cultivars exhibited higher total polyphenol content. Oil content ranged from 35 to 57% among cultivars; both Texas and Toundout genotypes recorded the highest oil content. Total protein concentration from select cultivars ranged from 50 mg/g in Ferraduel to 105 mg/g in Rizlane1 cultivars. Antioxidant activity of almond samples was examined by a DPPH (1,1-diphenyl-2-picrylhydrazyl) radical-scavenging assay; the antioxidant activity varied significantly within the cultivars, with IC50 (the half-maximal inhibitory concentration) values ranging from 2.25 to 20 mg/ml. Autochthonous cultivars originated from the Oujda region exhibited higher tegument total polyphenol and amino acid content compared to others. The genotype Rizlane2 recorded the highest flavonoid content. Pomological traits revealed a large variability within the almond germplasms. The hierarchical clustering analysis of all the data regarding pomological traits distinguished two groups with some particular genotypes as distinct cultivars, and groups of cultivars as polyclone varieties. These results strongly exhibit a potential use of Moroccan-originated almonds as potential clones for future selection due to their nutritional values and pomological traits compared to well-established cultivars.

Keywords: antioxidant activity, DDPH, Moroccan almonds, Prunus dulcis

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Authors: Vijay Kumar Singh, Pooja Kushwaha, Prabhat Ranjan, Krishna Kumar Ojha, Jitendra Kumar

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Present day high-throughput genomic technologies, NGS/microarrays, are producing large volume of data that require improved analysis methods to make sense of the data. The correlation between genes and samples has been regularly used to gain insight into many biological phenomena including, but not limited to, co-expression/co-regulation, gene regulatory networks, clustering and pattern identification. However, presence of outliers and violation of assumptions underlying Pearson correlation is frequent and may distort the actual correlation between the genes and lead to spurious conclusions. Here, we report a method to measure the strength of association between genes. The method assumes that the expression values of a gene are Bernoulli random variables whose outcome depends on the sample being probed. The method considers the two genes as uncorrelated if the number of sample with same outcome for both the genes (Ns) is equal to certainly expected number (Es). The extent of correlation depends on how far Ns can deviate from the Es. The method does not assume normality for the parent population, fairly unaffected by the presence of outliers, can be applied to qualitative data and it uses the binomial distribution to assess the significance of association. At this stage, we would not claim about the superiority of the method over other existing correlation methods, but our method could be another way of calculating correlation in addition to existing methods. The method uses binomial distribution, which has not been used until yet, to assess the significance of association between two variables. We are evaluating the performance of our method on NGS/microarray data, which is noisy and pierce by the outliers, to see if our method can differentiate between spurious and actual correlation. While working with the method, it has not escaped our notice that the method could also be generalized to measure the association of more than two variables which has been proven difficult with the existing methods.

Keywords: binomial distribution, correlation, microarray, outliers, transcriptome

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: E. Rama Devi Jothilingam

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Diabetes mellitus is now one of the most common non communicable diseases globally. India leads the world with largest number of diabetic subjects earning the title "diabetes capital of the world". In order to reduce the mortality rate, a fuzzy expert system is designed to predict the severity of cardiac and renal problems of diabetic patients using fuzzy logic. Since uncertainty is inherent in medicine, fuzzy logic is used in this research work to remove the inherent fuzziness of linguistic concepts and uncertain status in diabetes mellitus which is the prime cause for the cardiac arrest and renal failure. In this work, the controllable risk factors "blood sugar, insulin, ketones, lipids, obesity, blood pressure and protein/creatinine ratio" are considered as input parameters and the "the stages of cardiac" (SOC)" and the stages of renal" (SORD) are considered as the output parameters. The triangular membership functions are used to model the input and output parameters. The rule base is constructed for the proposed expert system based on the knowledge from the medical experts. Mamdani inference engine is used to infer the information based on the rule base to take major decision in diagnosis. Mean of maximum is used to get a non fuzzy control action that best represent possibility distribution of an inferred fuzzy control action. The proposed system also classifies the patients with high risk and low risk using fuzzy c means clustering techniques so that the patients with high risk are treated immediately. The system is validated with Matlab and is used as a tracking system with accuracy and robustness.

Keywords: Diabetes mellitus, fuzzy expert system, Mamdani, MATLAB

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Jianhua Zhang, Weiping Chen, Guanjie Chen, Jason Flannick, Emma Fikse, Glenda Smerin, Yanqin Yang, Yulong Li, John A. Hanover, William F. Simonds

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Ethnic disparities in many diseases are well recognized and reflect the consequences of genetic, behavior, and environmental factors. However, direct scientific evidence connecting the ethnic genetic variations and the disease disparities has been elusive, which may have led to the ethnic inequalities in large scale genetic studies. Through the genome-wide analysis of data representing 185,934 subjects, including 14,955 from our own studies of the African America Diabetes Mellitus, we discovered sets of genetic variants either unique to or conserved in all ethnicities. We further developed a quantitative gene function-based high-risk variant index (hrVI) of 20,428 genes to establish profiles that strongly correlate with the subjects' self-identified ethnicities. With respect to the ability to detect human essential and pathogenic genes, the hrVI analysis method is both comparable with and complementary to the well-known genetic analysis methods, pLI and VIRlof. Application of the ethnicity-specific hrVI analysis to the type 2 diabetes mellitus (T2DM) national repository, containing 20,791 cases and 24,440 controls, identified 114 candidate T2DM-associated genes, 8.8-fold greater than that of ethnicity-blind analysis. All the genes identified are defined as either pathogenic or likely-pathogenic in ClinVar database, with 33.3% diabetes-associated and 54.4% obesity-associated genes. These results demonstrate the utility of hrVI analysis and provide the first genetic evidence by clustering patterns of how genetic variations among ethnicities may impede the discovery of diabetes and foreseeably other disease-associated genes.

Keywords: diabetes-associated genes, ethnic health disparities, high-risk variant index, hrVI, T2DM

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Authors: Natalie Tyldesley-Marshall, Janette Parr, Anna Brown, Yen-Fu Chen, Amy Grove

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It is widely recognised in policy and research that the provision of services for children and young people (CYP) with Special Educational Needs and Disabilities (SEND) is enhanced when health and social care, and education services collaborate and interact effectively. In the UK, there have been significant changes to policy and provisions which support and improve collaboration. However, professionals responsible for implementing these changes face multiple challenges, including a lack of specific implementation guidance or framework to illustrate how effective multi-agency working could or should work. This systematic review will identify the key components of effective multi-agency working in services for CYP with SEND; and the most effective forms of partnership working in this setting. The review highlights interventions that lead to service improvements; and the conditions in the local area that support and encourage success. A protocol was written and registered with PROSPERO registration: CRD42022352194. Searches were conducted on several health, care, education, and applied social science databases from the year 2012 onwards. Citation chaining has been undertaken, as well as broader grey literature searching to enrich the findings. Qualitative, quantitative, mixed methods studies and systematic reviews were included, assessed independently, and critically appraised or assessed for risk of bias using appropriate tools based on study design. Data were extracted in NVivo software and checked by a more experienced researcher. A convergent segregated approach to synthesis and integration was used in which the quantitative and qualitative data were synthesised independently and then integrated using a joint display integration matrix. Findings demonstrate the key ingredients for effective partnership working for services delivering SEND. Interventions deemed effective are described, and lessons learned across interventions are summarised. Results will be of interest to educators and health and social care professionals that provide services to those with SEND. These will also be used to develop policy recommendations for how UK healthcare, social care, and education services for CYP with SEND aged 0-25 can most effectively collaborate and achieve service improvement. The review will also identify any gaps in the literature to recommend areas for future research. Funding for this review was provided by the Department for Education.

Keywords: collaboration, joint commissioning, service delivery, service improvement

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Authors: Devin Simmons

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At the USDOT’s Bureau of Transportation Statistics, a biannual census of ferry operators in the U.S. is conducted, with results such as route mileage used to determine federal funding levels for operators. AIS data allows for the possibility of using GIS software and geographical methods to confirm operator-reported mileage for individual ferry routes. As part of the USDOT’s work on the ferry census, an algorithm was developed that uses AIS data for ferry vessels in conjunction with known ferry terminal locations to model the average route travelled for use as both a cartographic product and confirmation of operator-reported mileage. AIS data from each vessel is first analyzed to determine individual journeys based on the vessel’s velocity, and changes in velocity over time. These trips are then converted to geographic linestring objects. Using the terminal locations, the algorithm then determines whether the trip represented a known ferry route. Given a large enough dataset, routes will be represented by multiple trip linestrings, which are then filtered by DBSCAN spatial clustering to remove outliers. Finally, these remaining trips are ready to be averaged into one route. The algorithm interpolates the point on each trip linestring that represents the start point. From these start points, a centroid is calculated, and the first point of the average route is determined. Each trip is interpolated again to find the point that represents one percent of the journey’s completion, and the centroid of those points is used as the next point in the average route, and so on until 100 points have been calculated. Routes created using this algorithm have shown demonstrable improvement over previous methods, which included the implementation of a LOESS model. Additionally, the algorithm greatly reduces the amount of manual digitizing needed to visualize ferry activity.

Keywords: ferry vessels, transportation, modeling, AIS data

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Authors: Santhani M. Selveindran

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Background: Natural disasters are traumatic global events that are becoming increasing more common, with significant psychosocial impact on survivors. This impact results not only in psychosocial distress but, for many, can lead to psychosocial disorders and chronic psychopathology. While there are currently available interventions that seek to prevent and treat these psychosocial sequelae, their effectiveness is uncertain. The evidence-base is emerging with more primary studies evaluating the effectiveness of various psychosocial interventions for survivors of natural disasters, which remains to be synthesized. Aim of Review: To identify, critically appraise and synthesize the current evidence-base on the effectiveness of psychosocial interventions in preventing or treating Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD) and/or Generalized Anxiety Disorder (GAD) in adults and children who are survivors of natural disasters. Methods: A protocol was developed as a guide to carry out this review. A systematic search was conducted in eight international electronic databases, three grey literature databases, one dissertation and thesis repository, websites of six humanitarian and non-governmental organizations renowned for their work on natural disasters, as well as bibliographic and citation searching for eligible articles. Papers meeting the specific inclusion criteria underwent quality assessment using the Downs and Black checklist. Data were extracted from the included papers and analysed by way of narrative synthesis. Results: Database and website searching returned 3777 papers where 31 met the criteria for inclusion. Additional 2 papers were obtained through bibliographic and citation searching. Methodological quality of most papers was fair. Twenty-five studies evaluated psychological interventions, five, social interventions whereas three studies evaluated ‘mixed’ psychological and social interventions. All studies, irrespective of methodological quality, reported post-intervention reductions in symptom scores for PTSD, depression and/or anxiety and where assessed, reduced diagnosis of PTSD and MDD, and produced improvements in self-efficacy and quality of life. Statistically significant results were seen in 27 studies. However, three studies demonstrated that the evaluated interventions may not have been very beneficial. Conclusions: The overall positive results suggest that any psychosocial interventions are favourable and should be delivered to all natural disaster survivors, irrespective of age, country, and phase of disaster. Yet, heterogeneity and methodological shortcomings of the current evidence-base makes it difficult to draw definite conclusions needed to formulate categorical guidance or frameworks. Further, rigorously conducted research is needed in this area, although the feasibility of such, given the context and nature of the problem, is also recognized.

Keywords: psychosocial interventions, natural disasters, survivors, effectiveness

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Authors: Bo Ning Lyu

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Blog assisted language learning (BALL) has been trialed by educators in language teaching with the development of Web 2.0 technology. Understanding the development trend of related research helps grasp the whole picture of the use of blog in language education. This paper reviews current research related to blogs enhanced language learning based on bibliometric analysis, aiming at (1) identifying the most frequently used keywords and their co-occurrence, (2) clustering research topics based on co-citation analysis, (3) finding the most frequently cited studies and authors and (4) constructing the co-authorship network. 330 articles were searched out in Web of Science, 225 peer-viewed journal papers were finally collected according to selection criteria. Bibexcel and VOSviewer were used to visualize the results. Studies reviewed were published between 2005 to 2016, most in the year of 2014 and 2015 (35 papers respectively). The top 10 most frequently appeared keywords are learning, language, blog, teaching, writing, social, web 2.0, technology, English, communication. 8 research themes could be clustered by co-citation analysis: blogging for collaborative learning, blogging for writing skills, blogging in higher education, feedback via blogs, blogging for self-regulated learning, implementation of using blogs in classroom, comparative studies and audio/video blogs. Early studies focused on the introduction of the classroom implementation while recent studies moved to the audio/video blogs from their traditional usage. By reviewing the research related to BALL quantitatively and objectively, this paper reveals the evolution and development trends as well as identifies influential research, helping researchers and educators quickly grasp this field overall and conducting further studies.

Keywords: blog, bibliometric analysis, language learning, literature review

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

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Authors: Hongyun Li, Zhibin Jiang

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The travel of passengers in the urban rail transit network is influenced by changes in network structure and operational status, and the response of individual travel preferences to these changes also varies. Firstly, the influence of the suspension of urban rail transit line sections on passenger travel along the line is analyzed. Secondly, passenger travel trajectories containing multi-dimensional semantics are described based on network UD data. Next, passenger panel data based on spatio-temporal sequences is constructed to achieve frequent passenger clustering. Then, the Graph Convolutional Network (GCN) is used to model and identify the changes in travel modes of different types of frequent passengers. Finally, taking Shanghai Metro Line 11 as an example, the travel behavior patterns of frequent passengers after the Huaqiao section shutdown during the COVID-19 epidemic are analyzed. The results showed that after the section shutdown, most passengers would transfer to the nearest Anting station for boarding, while some passengers would transfer to other stations for boarding or cancel their travels directly. Among the passengers who transferred to Anting station for boarding, most of passengers maintained the original normalized travel mode, a small number of passengers waited for a few days before transferring to Anting station for boarding, and only a few number of passengers stopped traveling at Anting station or transferred to other stations after a few days of boarding on Anting station. The results can provide a basis for understanding urban rail transit passenger travel patterns and improving the accuracy of passenger flow prediction in abnormal operation scenarios.

Keywords: urban rail transit, section shutdown, frequent passenger, travel behavior pattern

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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: Saeid Haghjoo, Ebrahim Reyhani, Fahimeh Kolahdouz

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The present study aimed to evaluate the understanding of the students in Tehran universities (Iran) about the numerical representation of the average rate of change based on the Structure of Observed Learning Outcomes (SOLO). In the present descriptive-survey research, the statistical population included undergraduate students (basic sciences and engineering) in the universities of Tehran. The samples were 604 students selected by random multi-stage clustering. The measurement tool was a task whose face and content validity was confirmed by math and mathematics education professors. Using Cronbach's Alpha criterion, the reliability coefficient of the task was obtained 0.95, which verified its reliability. The collected data were analyzed by descriptive statistics and inferential statistics (chi-squared and independent t-tests) under SPSS-24 software. According to the SOLO model in the prestructural, unistructural, and multistructural levels, basic science students had a higher percentage of understanding than that of engineering students, although the outcome was inverse at the relational level. However, there was no significant difference in the average understanding of both groups. The results indicated that students failed to have a proper understanding of the numerical representation of the average rate of change, in addition to missconceptions when using physics formulas in solving the problem. In addition, multiple solutions were derived along with their dominant methods during the qualitative analysis. The current research proposed to focus on the context problems with approximate calculations and numerical representation, using software and connection common relations between math and physics in the teaching process of teachers and professors.

Keywords: average rate of change, context problems, derivative, numerical representation, SOLO taxonomy

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Authors: Marziye Hadian, Alireza Jabbari

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Background: The World Health Organization has identified COVID-19 as a public health emergency and is urging governments to stop the virus transmission by adopting appropriate policies. In this regard, authorities have taken different approaches to cut the chain or controlling the spread of the disease. Now, the questions we are facing include what these approaches are? What tools should be used to implement each preventive protocol? In addition, what is the impact of each approach? Objective: The aim of this study was to determine the approaches to biological epidemics and related prevention tools with an emphasis on COVID-19 disease. Data sources: Databases including ISI web of science, PubMed, Scopus, Science Direct, Ovid, and ProQuest were employed for data extraction. Furthermore, authentic sources such as the WHO website, the published reports of relevant countries, as well as the Worldometer website were evaluated for gray studies. The time-frame of the study was from 1 December 2019 to 30 May 2020. Methods: The present study was a systematic study of publications related to the prevention strategies for the COVID-19 disease. The study was carried out based on the PRISMA guidelines and CASP for articles and AACODS for grey literature. Results: The study findings showed that in order to confront the COVID-19 epidemic, in general, there are three approaches of "mitigation", "active control" and "suppression" and four strategies of "quarantine", "isolation", "social distance" and "lockdown" in both individual and social dimensions to deal with epidemics. Selection and implementation of each approach requires specific strategies and has different effects when it comes to controlling and inhibiting the disease. Key finding: One possible approach to control the disease is to change individual behavior and lifestyle. In addition to prevention strategies, use of masks, observance of personal hygiene principles such as regular hand washing and non-contact of contaminated hands with the face, as well as an observance of public health principles such as sneezing and coughing etiquettes, safe extermination of personal protective equipment, must be strictly observed. Have not been included in the category of prevention tools. However, it has a great impact on controlling the epidemic, especially the new coronavirus epidemic. Conclusion: Although the use of different approaches to control and inhibit biological epidemics depends on numerous variables, however, despite these requirements, global experience suggests that some of these approaches are ineffective. The use of previous experiences in the world, along with the current experiences of countries, can be very helpful in choosing the accurate approach for each country in accordance with the characteristics of that country and lead to the reduction of possible costs at the national and international levels.

Keywords: novel corona virus, COVID-19, approaches, prevention tools, prevention strategies

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Authors: Meimei Shi

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Objectives: The identification of endangered species, especially simultaneous detection of multiple species in complex samples, plays a critical role in alleged wildlife crime incidents and prevents illegal trade. This study was to develop a multi-locus DNA metabarcoding method for endangered animal species identification. Methods: Several pairs of universal primers were designed according to the mitochondria conserved gene regions. Experimental mixtures were artificially prepared by mixing well-defined species, including endangered species, e.g., forest musk, bear, tiger, pangolin, and sika deer. The artificial samples were prepared with 1-16 well-characterized species at 1% to 100% DNA concentrations. After multiplex-PCR amplification and parameter modification, the amplified products were analyzed by capillary electrophoresis and used for NGS library preparation. The DNA metabarcoding was carried out based on Illumina MiSeq amplicon sequencing. The data was processed with quality trimming, reads filtering, and OTU clustering; representative sequences were blasted using BLASTn. Results: According to the parameter modification and multiplex-PCR amplification results, five primer sets targeting COI, Cytb, 12S, and 16S, respectively, were selected as the NGS library amplification primer panel. High-throughput sequencing data analysis showed that the established multi-locus DNA metabarcoding method was sensitive and could accurately identify all species in artificial mixtures, including endangered animal species Moschus berezovskii, Ursus thibetanus, Panthera tigris, Manis pentadactyla, Cervus nippon at 1% (DNA concentration). In conclusion, the established species identification method provides technical support for customs and forensic scientists to prevent the illegal trade of endangered animals and their products.

Keywords: DNA metabarcoding, endangered animal species, mitochondria nucleic acid, multi-locus

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Authors: Donatella Giuliani

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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Arsalan Ansari, Muhammad Dawood Idrees, Maria Hafeez

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Use of mobile phones has been increasing considerably over the past decade. Currently, it is one of the main sources of communication and information. Initially, mobile phones were limited to calls and messages, but with the advent of new technology smart phones were being used for many other purposes including video games. Despite of positive outcomes, addiction to video games on mobile phone has become a leading cause of psychological and physiological problems among many people. Several researchers examined the different aspects of behavior addiction with the use of different scales. Objective of this study is to examine any distinction between mobile game addicted and non-addicted players with the use of electroencephalography (EEG), based upon psycho-physiological indicators. The mobile players were asked to play a mobile game and EEG signals were recorded by BIOPAC equipment with AcqKnowledge as data acquisition software. Electrodes were places, following the 10-20 system. EEG was recorded at sampling rate of 200 samples/sec (12,000samples/min). EEG recordings were obtained from the frontal (Fp1, Fp2), parietal (P3, P4), and occipital (O1, O2) lobes of the brain. The frontal lobe is associated with behavioral control, personality, and emotions. The parietal lobe is involved in perception, understanding logic, and arithmetic. The occipital lobe plays a role in visual tasks. For this study, a 60 second time window was chosen for analysis. Preliminary analysis of the signals was carried out with Acqknowledge software of BIOPAC Systems. From the survey based on CGS manual study 2010, it was concluded that five participants out of fifteen were in addictive category. This was used as prior information to group the addicted and non-addicted by physiological analysis. Statistical analysis showed that by applying clustering analysis technique authors were able to categorize the addicted and non-addicted players specifically on theta frequency range of occipital area.

Keywords: mobile game, addiction, psycho-physiology, EEG analysis

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Authors: Enrique Claver Cortés, Bartolomé Marco Lajara, Eduardo Sánchez García, Pedro Seva Larrosa, Encarnación Manresa Marhuenda, Lorena Ruiz Fernández, Esther Poveda Pareja

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In recent decades, the analysis of the effects of clustering as an essential factor for the development of innovations and the competitiveness of enterprises has raised great interest in different areas. Nowadays, companies have access to almost all tangible and intangible resources located and/or developed in any country in the world. However, despite the obvious advantages that this situation entails for companies, their geographical location has shown itself, increasingly clearly, to be a fundamental factor that positively influences their innovative performance and competitiveness. Industrial clusters could represent a unique level of analysis, positioned between the individual company and the industry, which makes them an ideal unit of analysis to determine the effects derived from company membership of a cluster. Also, the absorptive capacity (hereinafter 'AC') can mediate the process of innovation development by companies located in a cluster. The transformation and exploitation of knowledge could have a mediating effect between knowledge acquisition and innovative performance. The main objective of this work is to determine the key factors that affect the degree of generation and use of knowledge from the environment by companies and, consequently, their innovative performance and competitiveness. The elements analyzed are the companies' membership of a cluster and the AC. To this end, 30 most relevant papers published on this subject in the "Web of Science" database have been reviewed. Our findings show that, within a cluster, the knowledge coming from the companies' environment can significantly influence their innovative performance and competitiveness, although in this relationship, the degree of access and exploitation of the companies to this knowledge plays a fundamental role, which depends on a series of elements both internal and external to the company.

Keywords: absorptive capacity, clusters, innovation, knowledge

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Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

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Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

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Authors: Garima Anand, Rupam Kapoor

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Alternaria leaf spot caused by Alternaria carthami is one of the most devastating diseases of safflower. It has resulted in huge losses in crop production and cultivation leading to a fall out of India’s rank as the leading producer of safflower in the world. Understanding the diversity of any pathogen is essential for its management and for the development of disease control strategies. The diversity of A. carthami was therefore analysed on the basis of biochemical, pathogenicity and genetic lines using ISSR markers. Collections and isolations of 95 isolates of A. carthami were made from major safflower producing states of India. Virulence was analysed to evaluate the pathogenic potential of these isolates. The isolates from Bijapur, Dharwad districts (Karnataka), and Parbhani and Solapur districts (Maharashtra) were found to be highly virulent. The virulence assays showed low virulence levels (42%) for the largest part of the population. Biochemical characterization to assess aggressiveness of these isolates was done by estimating the activity of cell wall degrading enzymes where isolates from districts Dharwad, Bijapur of Karnataka and districts Parbhani and Latur of Maharashtra were found to be most aggressive. Genetic diversity among isolates of A. carthami was determined using eighteen ISSR markers. Distance analysis using neighbour joining method and PCoA analysis of the ISSR profiles divided the isolates into three sub-populations. The most virulent isolates clustered in one group in the dendrogram. The study provided no evidence for geographical clustering indicating that isolates are randomly spread across the states, signifying the high potential of the fungus to adapt to diverse regions. The study can, therefore, aid in the breeding and deployment of A. carthami resistant safflower varieties and in the management of Alternaria leaf spot disease.

Keywords: alternaria leaf spot, genetic diversity, pathogenic potential, virulence

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