Search results for: climatic classification

Authors: Khasim Munir Bhasha Shaik

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Veligonda and adjoining hill range spread along about 170 Km North to South in Kadapa and Nellore Districts stretching a little further into Prakasam District. The latitude in general ranges up to 1000m. The forests are generally dry deciduous type. Veligonda and adjoining hill ranges comprise of Palakonda, Seshachalam, Lankamala and the terminal part of Nallamalais from mid-region of Southern Eastern Ghats. The Veligonda range which separates the Nellore district from Kadapa and Kurnool is the backbone of the Eastern Ghats, starting from Nagari promontory in Chittoor district. It runs in a northerly direction along the western border of the Nellore district, with a raising elevation of 3,626 ft at Penchalakona in Raipur thaluk. Veligonda hill ranges are high in altitude and have deep valleys. Among the Veligondas range of hills the Durgam in Venkatagiri range and Penchalakona are the most prominent and are situated 914 meters above mean sea level. It has more than 3000 species of plants along with 500 animal species. The unique specialty of this region is the presence of Pterocarpus santalinus(endangered) and Santalum album (vulnerable). In the present study, an attempt is made to assess the efforts that are going on to conserve the biodiversity of flora and fauna of this region. Various conservation strategies were suggested to protect the biodiversity and richness of Veligonda forest, hill region of Eastern Ghats of Andhra Pradesh. The major threats and the reasons for the dwindling species richness are poor rainfall, adverse climatic conditions, robbery of Red sanders and poaching of animals by the local tribals. Efforts are to be made to conserve some of the animals by both in situ and ex-situ methods. More awareness is to be developed among the local communities who are dwelling in the vicinity and importance of conservation is to be emphasized to them. Anthropogenic attachments are to be made by introducing more numbers of sacred groves. Gross enforcement of law is to be made to protect the various forest resources in this area. The important species with the medicinal values are to be identified. It was found that two important wildlife sanctuaries named Sri Lankamalleswarawildlife sanctuary and Sripenusila Narasimha wildlife sanctuary are working for the comprehensive conservation of the environment in this area. Apart from this more than 38 important sacred grooves are there where the plants and animals are protected by local Yanadi and other communities.

Keywords: biodiversity, wild life sanctuary, habitat destruction, eastern Ghats

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Ki-Yeo Kim

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Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Keywords: oral squamous cell carcinoma, combined biomarker, microarray dataset, correlated genes

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: J. C. Hoyos, J. Zambrano Nájera

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Increasing urbanization has led to changes in the natural dynamics of watersheds, causing problems such as increases in volumes of runoff, peak flow rates, and flow rates so that the risk of storm flooding increases. Sewerage networks designed 30 – 40 years ago don’t account for these increases in flow volumes and velocities. Besides, Andean cities with high slopes worsen the problem because velocities are even higher not allowing sewerage network work and causing cities less resilient to landscape changes and climatic change. In Latin America, especially Colombia, this is a major problem because urban population at late XX century was more than 70% is in urban areas increasing approximately in 790% in 1940-1990 period. Thus, it becomes very important to study how changes in hydrological behavior affect hydraulic capacity of sewerage networks in Andean Urban Watersheds. This research aims to determine the impact of urbanization in high-sloped urban watersheds in its hydrology. To this end it will be used as study area experimental urban watershed named Palogrande-San Luis watershed, located in the city of Manizales, Colombia. Manizales is a city in central western Colombia, located in Colombian Central Mountain Range (part of Los Andes Mountains) with an abrupt topography (average altitude is 2.153 m). The climate in Manizales is quite uniform, but due to its high altitude it presents high precipitations (1.545 mm/year average) with high humidity (83% average). Behavior of the current sewerage network will be reviewed by the hydraulic model SWMM (Storm Water Management Model). Based on SWMM the hydrological response of urban watershed selected will be evaluated under the design storm with different frequencies in the region, such as drainage effect and water-logging, overland flow on roads, etc. Cartographic information was obtained from a Geographic Information System (GIS) thematic maps of the Institute of Environmental Studies of the Universidad Nacional de Colombia and the utility Aguas de Manizales S.A. Rainfall and streamflow data is obtained from 4 rain gages and 1 stream gages. This information will allow determining critical issues on drainage systems design in urban watershed with very high slopes, and which practices will be discarded o recommended.

Keywords: land cover changes, storm sewer system, urban hydrology, urban planning

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: A. Bouzekri, H. Benmassaud

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Aurès region is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: remote sensing, spatiotemporal, land use, Aurès

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Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

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Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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

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

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Authors: Ligia Florio, João Mauricio Castaldelli-Maia

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Background: Food addiction (FA) emerged in the 1990s as a possible contributor to the increasing prevalence of obesity and overweight, in conjunction with changing food environments and mental health conditions. However, FA is not yet listed as one of the disorders in the DSM-5 and/or the ICD-11. Although there are controversies and debates in the literature about the classification and construct of FA, the most common approach to access it is the use of a research tool - the Yale Food Addiction Scale (YFAS) - which approximates the concept of FA to the concept diagnosis of dependence on psychoactive substances. There is a need to explore the dimensional phenotypes accessed by YFAS in different population groups for a better understanding and scientific support of FA diagnoses. Methods: The primary objective of this project was to investigate the construct validity of the FA concept by mYFAS 2.0 in individuals who underwent bariatric surgery (n = 100) at the Hospital Estadual Mário Covas since 2011. Statistical analyzes were conducted using the STATA software. In this sense, structural or factor validity was the type of construct validity investigated using exploratory factor analysis (EFA) and item response theory (IRT) techniques. Results: EFA showed that the one-dimensional model was the most parsimonious. The IRT showed that all criteria contributed to the latent structure, presenting discrimination values greater than 0.5, with most presenting values greater than 2. Conclusion: This study reinforces a FA dimension in patients who underwent bariatric surgery. Within this dimension, we identified the most severe and discriminating criteria for the diagnosis of FA.

Keywords: obesity, food addiction, bariatric surgery, regain

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Authors: Kei Okubo, Yosuke Kurihara, Takashi Kaburagi, Kajiro Watanabe

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Many of the ever-growing elderly population require exercise, such as running, for health management. One important element of a runner’s training is the choice of shoes for exercise; shoes are important because they provide the interface between the feet and road. When we purchase shoes, we may instinctively choose a pair after trying on many different pairs of shoes. Selecting the shoes instinctively may work, but it does not guarantee a suitable fit for running activities. Therefore, if we could select suitable shoes for each runner from the viewpoint of brain activities, it would be helpful for validating shoe selection. In this paper, we describe how brain activities show different characteristics during particular task, corresponding to different properties of shoes. Using five subjects, we performed a verification experiment, applying weight, softness, and flexibility as shoe properties. In order to affect the shoe property’s differences to the brain, subjects run for ten min. Before and after running, subjects conducted a paced auditory serial addition task (PASAT) as the particular task; and the subjects’ brain activities during the PASAT are evaluated based on oxyhemoglobin and deoxyhemoglobin relative concentration changes, measured by near-infrared spectroscopy (NIRS). When the brain works actively, oxihemoglobin and deoxyhemoglobin concentration drastically changes; therefore, we calculate the maximum values of concentration changes. In order to normalize relative concentration changes after running, the maximum value are divided by before running maximum value as evaluation parameters. The classification of the groups of shoes is expressed on a self-organizing map (SOM). As a result, deoxyhemoglobin can make clusters for two of the three types of shoes.

Keywords: brain activities, NIRS, PASAT, running shoes

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: actionable pattern discovery, education, emotion, data mining

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Authors: Emily Rivera, Toni DiDona

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The Big Five personality traits is a hierarchical classification of personality traits that applies factor analysis to a personality survey data in order to describe human personality using five broad dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness (Fetvadjiev & Van de Vijer, 2015). Research shows that personality constructs underline individual differences in processing conflict and interpersonal relations. (Graziano et al., 1996). This research explores the understudied correlation between the Big Five personality traits and perceived interpersonal conflict in the workplace. It revises social psychological literature on Big Five personality traits within a social context and discusses organizational development journal articles on the perceived efficacy of conflict tactics and approach to interpersonal relationships. The study also presents research undertaken on a survey group of 867 subjects over the age of 18 that were recruited by means of convenience sampling through social media, email, and text messaging. The central finding of this study is that only two of the Big Five personality traits had a significant correlation with perceiving interpersonal conflict in the workplace. Individuals who score higher on agreeableness and neuroticism, perceive more interpersonal conflict in the workplace compared to those that score lower on each dimension. The relationship between both constructs is worthy of research due to its everyday frequency and unique individual psycho-social consequences. This multimethod research associated the Big Five personality dimensions to interpersonal conflict. Its findings that can be utilized to further understand social cognition, person perception, complex social behavior and social relationships in the work environment.

Keywords: five-factor model, interpersonal conflict, personality, The Big Five personality traits

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Authors: Zeyu Zhou, Xiaojun Bi

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Due to the limited environmental parameters and the limited resolution of the sensor, the universal existence of the mixed pixels in the process of remote sensing images restricts the spatial resolution of the remote sensing images. Sub-pixel mapping technology can effectively improve the spatial resolution. As the bilinear interpolation algorithm inevitably produces the edge blur effect, which leads to the inaccurate sub-pixel mapping results. In order to avoid the edge blur effect that affects the sub-pixel mapping results in the interpolation process, this paper presents a new edge-directed interpolation algorithm which uses the covariance adaptive interpolation algorithm on the edge of the low-resolution image and uses bilinear interpolation algorithm in the low-resolution image smooth area. By using the edge-directed interpolation algorithm, the super-resolution of the image with low resolution is obtained, and we get the percentage of each sub-pixel under a certain type of high-resolution image. Then we rely on the probability value as a soft attribute estimate and carry out sub-pixel scale under the ‘hard classification’. Finally, we get the result of sub-pixel mapping. Through the experiment, we compare the algorithm and the bilinear algorithm given in this paper to the results of the sub-pixel mapping method. It is found that the sub-pixel mapping method based on the edge-directed interpolation algorithm has better edge effect and higher mapping accuracy. The results of the paper meet our original intention of the question. At the same time, the method does not require iterative computation and training of samples, making it easier to implement.

Keywords: remote sensing images, sub-pixel mapping, bilinear interpolation, edge-directed interpolation

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Authors: Nana Bolashvili, Vakhtang Geladze, Tamazi Karalashvili, Nino Machavariani, George Geladze, Davit Kartvelishvili, Ana Karalashvili

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Fresh water is the major natural resource of Georgia. The average perennial sum of the rivers' runoff in Georgia is 52,77 km³, out of which 9,30 km³ inflows from abroad. The major volume of transit river runoff is ascribed to the Chorokhi river. Average perennial runoff in Western Georgia is 41,52 km³, in Eastern Georgia 11,25 km³. The indices of Eastern and Western Georgia were calculated with 50% and 90% river runoff respectively, while the same index calculation for other countries is based on a 50% river runoff. Out of total volume of resources, 133,2 m³/sec (4,21 km³) has been geologically prospected by the State Commission on Reserves and Acknowledged as reserves available for exploitation, 48% (2,02 km³) of which is in Western Georgia and 2,19 km³ in Eastern Georgia. Considering acknowledged water reserves of all categories per capita water resources accounts to 2,2 m³/day, whereas high industrial category -0. 88 m³ /day fresh drinking water. According to accepted norms, the possibility of using underground water reserves is 2,5 times higher than the long-term requirements of the country. The volume of abundant fresh-water reserves in Georgia is about 150 m³/sec (4,74 km³). Water in Georgia is consumed mostly in agriculture for irrigation purposes. It makes 66,4% around Georgia, in Eastern Georgia 72,4% and 38% in Western Georgia. According to the long-term forecast provision of population and the territory with water resources in Eastern Georgia will be quite normal. A bit different is the situation in the lower reaches of the Khrami and Iori rivers which could be easily overcome by corresponding financing. The present day irrigation system in Georgia does not meet the modern technical requirements. The overall efficiency of their majority varies between 0,4-0,6. Similar is the situation in the fresh water and public service water consumption. Organization of the mentioned systems, installation of water meters, introduction of new methods of irrigation without water loss will substantially increase efficiency of water use. Besides new irrigation norms developed from agro-climatic, geographical and hydrological angle will significantly reduce water waste. Taking all this into account we assume that for irrigation agricultural lands in Georgia is necessary 6,0 km³ water, 5,5 km³ of which goes to Eastern Georgia on irrigation arable areas. To increase water supply in Eastern Georgian territory and its population is possible by means of new water reservoirs as the runoff of every river considerably exceeds the consumption volume. In conclusion, we should say that fresh water resources by which Georgia is that rich could be significant source for barter exchange and investment attraction. Certain volume of fresh water can be exported from Western Georgia quite trouble free, without bringing any damage to population and hydroecosystems. The precise volume of exported water per region/time and method/place of water consumption should be defined after the estimation of different hydroecosystems and detailed analyses of water balance of the corresponding territories.

Keywords: GIS, management, rivers, water resources

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Authors: S. R. Shamili, C. Natarajan, J. Karthikeyan

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Rapid growth of world population and widespread urbanization has remarkably increased the development of the construction industry which caused a huge demand for sand and gravels. Environmental problems occur when the rate of extraction of sand, gravels, and other materials exceeds the rate of generation of natural resources; therefore, an alternative source is essential to replace the materials used in concrete. Now-a-days, electronic products have become an integral part of daily life which provides more comfort, security, and ease of exchange of information. These electronic waste (E-Waste) materials have serious human health concerns and require extreme care in its disposal to avoid any adverse impacts. Disposal or dumping of these E-Wastes also causes major issues because it is highly complex to handle and often contains highly toxic chemicals such as lead, cadmium, mercury, beryllium, brominates flame retardants (BFRs), polyvinyl chloride (PVC), and phosphorus compounds. Hence, E-Waste can be incorporated in concrete to make a sustainable environment. This paper deals with the composition, preparation, properties, classification of E-Waste. All these processes avoid dumping to landfills whilst conserving natural aggregate resources, and providing a better environmental option. This paper also provides a detailed literature review on the behaviour of concrete with incorporation of E-Wastes. Many research shows the strong possibility of using E-Waste as a substitute of aggregates eventually it reduces the use of natural aggregates in concrete.

Keywords: dumping, electronic waste, landfill, toxic chemicals

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Authors: T. Sadunishvili, L. Kutateladze, T. Urushadze, R. Khvedelidze, N. Zakariashvili, M. Jobava, G. Kvesitadze

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Multiple repeated soil-climatic zones in Georgia determines the diversity of microorganisms. Hundreds of microscopic fungi of different genera have been isolated from different ecological niches, including some extreme environments. Biosynthetic ability of microscopic fungi has been studied. Trichoderma ressei, representative of the Ascomycetes secrete cellulolytic and xylanolytic enzymes that act in synergy to hydrolyze polysaccharide polymers to glucose, xylose and arabinose, which can be fermented to biofuels. The other mesophilic strains producing cellulases are Allesheria terrestris, Chaetomium thermophile, Fusarium oxysporium, Piptoporus betulinus, Penicillium echinulatum, P. purpurogenum, Aspergillus niger, A. wentii, A. versicolor, A. fumigatus etc. In the majority of the cases the cellulases produced by strains of genus Aspergillus usually have high β-glucosidase activity and average endoglucanases levels (with some exceptions), whereas strains representing Trichoderma have high endo enzyme and low β-glucosidase, and hence has limited efficiency in cellulose hydrolysis. Six producers of stable cellulases and xylanases from mesophilic and thermophilic fungi have been selected. By optimization of submerged cultivation conditions, high activities of cellulases and xylanases were obtained. For enzymes purification, their sedimentation by organic solvents such as ethyl alcohol, acetone, isopropanol and by ammonium sulphate in different ratios have been carried out. Best results were obtained with precipitation by ethyl alcohol (1:3.5) and ammonium sulphate. The yields of enzyme according to cellulase activities were 80-85% in both cases. Cellulase activity of enzyme preparation obtained from the strain Trichoderma viride X 33 is 126 U/g, from the strain Penicillium canescence D 85–185U/g and from the strain Sporotrichum pulverulentum T 5-0 110 U/g. Cellulase activity of enzyme preparation obtained from the strain Aspergillus sp. Av10 is 120 U/g, xylanase activity of enzyme preparation obtained from the strain Aspergillus niger A 7-5–1155U/g and from the strain Aspergillus niger Aj 38-1250 U/g. Optimum pH and temperature of operation and thermostability, of the enzyme preparations, were established. The efficiency of hydrolyses of different agricultural residues by the microscopic fungi cellulases has been studied. The glucose yield from the residues as a result of enzymatic hydrolysis is highly determined by the ratio of enzyme to substrate, pH, temperature, and duration of the process. Hydrolysis efficiency was significantly increased as a result of different pretreatment of the residues by different methods. Acknowledgement: The Study was supported by the ISTC project G-2117, funded by Korea.

Keywords: cellulase, xylanase, microscopic fungi, enzymatic hydrolysis

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Helen Frialde-Dupale

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Critical thinking Index (CTI) of 150 third year college students from five State Colleges and Universities (SUCs) in Region I were determined. Only students with Grade Point Average (GPA) of at least 2.0 from four general classification of degree courses, namely: Education, Arts and Sciences, Engineering and Agriculture were included. Specific problem No.1 dealt with the profile variables, namely: age, sex, degree course, monthly family income, number of siblings, high school graduated from, grade point average, personality type, highest educational attainment of parents, and occupation of parents. Problem No. 2 determined the critical thinking index among the respondents. Problem No. 3 investigated whether or not there are significant differences in the critical thinking index among the respondents across the profile variables. While problem No.4 determined whether or not there are significant relationship between the critical thinking index and selected profile variables, namely: age, monthly family income, number of siblings, and grade point average of the respondents. Finally, on problem No. 5, the critical thinking instrument which obtained the lowest rates, were used as basis for outlining an intervention program for enhancing critical thinking index (CTI) of students. The following null hypotheses were tested at 0.05 level of significance: there are no significant differences in the critical thinking index of the third college students across the profile variables; there are no significant relationships between the critical thinking index of the respondents and selected variables, namely: age, monthly family income, number of siblings, and grade point average.

Keywords: attitude as critical thinker, critical thinking applied, critical thinking index, self-perception as critical thinker

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Sudikshya Bhandari, Jonathan K. London

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Barpak in northern Nepal represents a unique identity expressed through the local rituals, values, lifeways and the styles of vernacular architecture. The traditional residential buildings and construction practices adopted by the dominant ethnic groups: Ghales and Gurungs, reflect environmental, social, cultural and economic concerns. However, most of these buildings did not survive the Gorkha earthquake in 2015 that made many residents skeptical about their strength to resist future disasters. This led Barpak residents to prefer modern housing designs primarily for the strength but additionally for convenience and access to earthquake relief funds. Post-earthquake reconstruction has transformed the cohesive community, developed over hundreds of years into a haphazard settlement with the imposition of externally-driven building models. Housing guidelines provided for the community reconstruction and earthquake resilience have been used as a singular template, similar to other communities on different geographical locations. The design and construction of these buildings do not take into account the local, historical, environmental, social, cultural and economic context of Barpak. In addition to the physical transformation of houses and the settlement, the consequences continue to develop challenges to sustainability. This paper identifies the major challenges for environmental sustainability with the construction of new houses in post-earthquake Barpak. Mixed methods such as interviews, focus groups, site observation, and documentation, and analysis of housing and neighborhood design have been used for data collection. The discernible changing situation of this settlement due to the new housing has included reduced climatic adaptation and thermal comfort, increased consumption of agricultural land and water, minimized use of local building materials, and an increase in energy demand. The research has identified that reconstruction housing practices happening in Barpak, while responding to crucial needs for disaster recovery and resilience, are also leading this community towards an unsustainable future. This study has also integrated environmental, social, cultural and economic parameters into an assessment framework that could be used to develop place-based design guidelines in the context of other post-earthquake reconstruction efforts. This framework seeks to minimize the unintended repercussions of unsustainable reconstruction interventions, support the vitality of vernacular architecture and traditional lifeways and respond to context-based needs in coordination with residents.

Keywords: earthquake, environment, reconstruction, sustainability

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Authors: Pukhtoon Yar

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Peshawar City is experiencing a rapid spatial urban growth primarily as a result of high rate of urbanization along with economic development. This paper was designed to understand the impacts of urbanization on agriculture land use change by particularly focusing on land use change trajectories from the past (1991-2014). We used Landsat imageries (30 meters) for1991along with Spot images (2.5 meters) for year 2014. . The ground truthing of the satellite data was performed by collecting information from Peshawar Development Authority, revenue department, real estate agents and interviews with the officials of city administration. The temporal satellite images were processed by applying supervised maximum likelihood classification technique in ArcGIS 9.3. The procedure resulted into five main classes of land use i.e. built-up area, farmland, barren land, cultivable-wasteland and water bodies. The analysis revealed that, in Peshawar City the built-up environment has been doubled from 8.1 percent in 1991 to over 18.2 percent in 2014 by predominantly encroaching land producing food. Furthermore, the CA-Markov Model predicted that the area under impervious surfaces would continue to flourish during the next three decades. This rapid increase in built-up area is accredited to the lack of proper land use planning and management, which has caused chaotic urban sprawl with detrimental social and environmental consequences.

Keywords: Urban Expansion, Land use, GIS, Remote Sensing, Markov Model, Peshawar City

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Justin Lee, Siraj Shaikh, Alice Chu MD

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Children with cerebral palsy (CP) commonly present with upper extremity impairment, affecting one or both extremities, and are classified using the Manual Ability Classification Scale (MACS). The MACS defines bimanual hand abilities for children ages 4-18 years in everyday tasks and is a gradient scale, with I being nearly normal and V requiring total assistance. Children with more severe upper extremity impairment (MACS III-V) are often underrepresented, and relatively few effective therapies have been identified for these patients. Current orthoses are static and are only meant to prevent the progression of contractures in these patients. Other limitations include cost, comfort, accessibility, and longevity of the orthoses. Taking advantage of advances in 3D printing technology, we have created a highly customizable upper extremity orthotic that can be produced at a low cost. Iterations in our design have resulted in an orthotic that is custom fit to the patient based on scans of their arm, made of rigid polymer when needed to provide support, flexible material where appropriate to allow for comfort, and designed with a mechanical pulley system to allow for some functional use of the arm while in the orthotic. Preliminary data has shown that our orthotic can be built at a fraction of the cost of current orthoses and provide clinically significant improvement in assisting hand assessment (AHA) and pediatric quality of life scores (PedsQL).

Keywords: upper extremity orthosis, upper extremity, orthosis, 3-D printing, cerebral palsy, occupational therapy, spasticity, customizable

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Authors: Dasom Kim, Chen Liu, Myungsu Lim, Su-Hyeon Jeon, ByeoungKug Jeon, Kee-Young Kwahk, Namgyu Kim

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Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we previously proposed a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. In this paper, we design a survey-based verification scenario for estimating the accuracy of our automatic categorization methodology.

Keywords: big data analysis, document classification, multi-category, text mining, topic analysis

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Authors: Fatemeh Deldarabdolmaleki, Mohamad Fakri Zaky Bin Ja'afar

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Having a daylit space together with view results in a pleasant and productive environment for office employees. A daylit space is a space which utilizes daylight as a basic source of illumination to fulfill user’s visual demands and minimizes the electric energy consumption. Malaysian weather is hot and humid all over the year because of its location in the equatorial belt. however, because most of the commercial buildings in Malaysia are air-conditioned, huge glass windows are normally installed in order to keep the physical and visual relation between inside and outside. As a result of climatic situation and mentioned new trend, an ordinary office has huge heat gain, glare, and discomfort for occupants. Balancing occupant’s comfort and energy conservation in a tropical climate is a real challenge. This study concentrates on evaluating a venetian blind system using per pixel analyzing tools based on the suggested cut-out metrics by the literature. Workplace area in a private office room has been selected as a case study. Eight-day measurement experiment was conducted to investigate the effect of different venetian blind angles in an office area under daylight conditions in Serdang, Malaysia. The study goal was to explore daylight comfort of a commercially available venetian blind system, its’ daylight sufficiency and excess (8:00 AM to 5 PM) as well as Glare examination. Recently developed software, analyzing High Dynamic Range Images (HDRI captured by CCD camera), such as radiance based Evalglare and hdrscope help to investigate luminance-based metrics. The main key factors are illuminance and luminance levels, mean and maximum luminance, daylight glare probability (DGP) and luminance ratio of the selected mask regions. The findings show that in most cases, morning session needs artificial lighting in order to achieve daylight comfort. However, in some conditions (e.g. 10° and 40° slat angles) in the second half of day the workplane illuminance level exceeds the maximum of 2000 lx. Generally, a rising trend is discovered toward mean window luminance and the most unpleasant cases occur after 2 P.M. Considering the luminance criteria rating, the uncomfortable conditions occur in the afternoon session. Surprisingly in no blind condition, extreme case of window/task ratio is not common. Studying the daylight glare probability, there is not any DGP value higher than 0.35 in this experiment.

Keywords: daylighting, energy simulation, office environment, Venetian blind

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Authors: Pooja Ahuja, Tejal Bhutani, M. S. Dahiya

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Cheiloscopy, the study of lip prints, is a tool in forensic investigation technique that deals with identification of individuals based on lips patterns. The objective of this study is to determine predominant lip print pattern found among the Gujarati population, to evaluate whether any sex difference exists and to study the permanence of the pattern over six months duration. The study comprised of 100 healthy individuals (50 males and 50 females), in the age group of 18 to 25 years of Gujarati population of the Gandhinagar region of the Gujarat state, India. By using Suzuki and Tsuchihashi classification, Lip prints were then divided into four quadrants and also classified on the basis of peripheral shape of the lips. Materials used to record the lip prints were dark brown colored lipstick, cellophane tape, and white bond paper. Lipstick was applied uniformly, and lip prints were taken on the glued portion of cellophane tape and then stuck on to a white bond paper. These lip prints were analyzed with magnifying lens and virtually with stereo microscope. On the analysis of the subject population, results showed Branched pattern Type II (29.57 percentage) to be most predominant in the Gujarati population. Branched pattern Type II (35.60 percentage) and long vertical Type I (28.28 percentage) were most prevalent in males and females respectively and large full lips were most predominantly present in both the sexes. The study concludes that lip prints in any form can be an effective tool for identification of an individual in a closed or open group forms.

Keywords: cheiloscopy, lip pattern, predomianant, Gujarati population

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