Search results for: climatic classification

Authors: Iris Maria Velez Osorio

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There has been in the last decade a concern about the environment, particularly about clean and enough water for human consumption but, some enterprises had some trouble to understand the limited resources in the environment. This research tries to understand how some industries are better oriented to the preservation of the environment through investment for strategic management of scarce resources and try in the best way possible, the contaminants. It was made an industry classification since four different group of theories for Corporate Social Responsibility agree with variables of: investment in environmental care, water protection, and residues treatment finding different levels of commitment with CSR.

Keywords: corporate social responsibility, environment, strategic management, water

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Authors: Slim Amami

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A model is developed to prevent the risks related to climate conditions in the agricultural sector. It will determine the yearly optimum premium to be paid by a farmer in order to reach his required turnover. The model is mainly based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, main ones of which are daily average sunlight, rainfall and temperature. By a simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is deduced from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. Optimal premium is then deduced, and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect their harvest. The application to wheat production in the French Oise department illustrates the reliability of the present model with as low as 6% difference between predicted and real data. The model can be adapted to almost every agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, database, meteorological factors, production model, optimal price

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Authors: Kyle Saltmarsh

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Acoustic testing possesses many benefits due to its non-destructive nature and practicality. There hence exists many scenarios in which using acoustic testing for condition testing shows powerful feasibility. A wealth of information is contained within the acoustic and vibration characteristics of structures, allowing the development meaningful features for the classification of their respective condition. In this paper, methods, results, and discussions are presented on the use of non-destructive acoustic testing coupled with acoustic feature extraction and machine learning techniques for the condition testing of manufactured circular steel plates subjected to varied levels of damage.

Keywords: plates, deformation, acoustic features, machine learning

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Authors: Saroj Bijarnia, Preety Singh

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We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

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Authors: Messay Mulugeta, Degefa Tolossa

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The purpose of this paper is to scrutinize trends of climate risks in eastern and southeastern parts of Ethiopia. This part of the country appears severely affected by recurrent droughts, erratic rainfall, and increasing temperature condition. Particularly, erratic rains and moisture stresses have been forcibly threatening and shoving the people over many decades coupled with unproductive policy frameworks and weak institutional setups. These menaces have been more severe in dry lowlands where rainfall is more erratic and scarce. Long-term climate data of nine weather stations in eastern and southeastern parts of Ethiopia were obtained from National Meteorological Agency of Ethiopia (NMA). As issues related to climate risks are very intricate, different techniques and indices were applied to deal with the objectives of the study. It is concluded that erratic rainfall, moisture scarcity, and increasing temperature conditions have been the main challenges in eastern and southeastern Ethiopia. In fact, these risks can be eased by putting in place efficient and integrated rural development strategies, environmental rehabilitation plans of action in overworked areas, proper irrigation and water harvesting practices and well thought-out and genuine resettlement schemes.

Keywords: rainfall variability, erratic rains, precipitation concentration index (PCI), climatic pattern, Ethiopia

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Sara Patricia Ibarra-Zavaleta, Rabindranarth Romero-Lopez, Rosario Langrave, Annie Poulin, Gerald Corzo, Mathias Glaus, Ricardo Vega-Azamar, Norma Angelica Oropeza

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The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHE). Mexico is an example; this has been affected by the presence of EHE leaving economic, social and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to predict flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process (NSE=0.83, RSR=0.021 and BIAS=-4.3) and validation: temporal was assessed at two points: point one (NSE=0.78, RSR=0.113 and BIAS=0.054) and point two (NSE=0.825, RSR=0.103 and BIAS=0.063) are satisfactory. The DHM showed its applicability in tropical environments and its ability to characterize the rainfall-runoff relationship in the study area. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Keywords: HYDROTEL, hydraulic power, extreme hydrometeorological events, streamflow

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Authors: Rabia Munsaf Khan, Eshrat Fatima

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The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016.

Keywords: Landsat, normalized difference vegetation index (NDVI), sentinel 2, Greenland monitoring

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Nabil Saad, David Morgan, Manish Gupta

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Visibility has become a key component of air quality and is regulated in many areas by environmental laws such as the EPA Clean Air Act and Regional Haze Rule. Typically, visibility is calculated by estimating the optical absorption and scattering of both gases and aerosols. A major component of the aerosols’ climatic effect is due to their scattering and absorption of solar radiation, which are governed by their optical and physical properties. However, the accurate assessment of this effect on global warming, climate change, and air quality is made difficult due to uncertainties in the calculation of single scattering albedo (SSA). Experimental complications arise in the determination of the single scattering albedo of an aerosol particle since it requires the simultaneous measurement of both scattering and extinction. In fact, aerosol optical absorption, in particular, is a difficult measurement to perform, and it’s often associated with large uncertainties when using filter methods or difference methods. In this presentation, we demonstrate the use of a new open-path Optical Extinction Analyzer (OEA) in conjunction with a nephelometer and two particle sizers, emphasizing the benefits that co-employment of the OEA offers to derive the complex refractive index of aerosols and their single scattering albedo parameter. Various use cases, data reproducibility, and instrument calibration will also be presented to highlight the value proposition of this novel Open-Path OEA.

Keywords: aerosols, extinction, visibility, albedo

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Authors: Allen A. Espinosa, Arlyne C. Marasigan, Janir T. Datukan

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The study explored how students visualize the states and classifications of matter using scientific models. It also identified misconceptions of students in using scientific models. In general, high percentage of students was able to use scientific models correctly and only a little misconception was identified. From the result of the study, a teaching framework was formulated wherein scientific models should be employed in classroom instruction to visualize abstract concepts in chemistry and for better conceptual understanding.

Keywords: visual conception, scientific models, mental models, states of matter, classification of matter

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Authors: Ana Gavardashvili

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The field-and-scientific studies to evaluate the modern ecological state in the water area of the estuaries of the major water-abundant rivers in the coastal line of the Black Sea (Chorokhi, Kintrishi, Natanebi, Supsa, Khobistskali, Rioni and Enguri) and sea ports (Batumi, Poti) and sea terminals of the oil pipeline (Baku-Tbilisi-Supsa, Kulevi) were accomplished in the months of June and July of 2015. GPS coordinates and GIS programs were used to fix the areas of the estuaries of the above-listed rivers on a digital map, with their values varying within the limits of 0,861 and 20,390 km2. Water samples from the Black Sea were taken from the river estuaries and sea ports during the field works, with their statistical series of 125 points. The temperatures of air (t2) and water in the Black Sea (t1) were measured locally, and their relative value is (t1 /t2 ) = 0,69 – 0,92. 125 water samples taken from the study object in the Black Sea coastal line were subject to laboratory analysis, and it was established that the Black Sea acidity (pH) changes within the limits of 7,71 – 8,22 in the river estuaries and within 8,42 - 8,65 in the port water areas and at oil terminals. As for the Sea water salinity index (TDS), it changes within the limits of 6,15 – 12,67 in the river estuaries, and (TDS) = 11,80 – 13,67 in the port water areas and at oil terminals. By taking the gained data and climatic changes into account, by using the theories of reliability and risk at the following stage, the nature of the changes of the function of the Black Sea ecological parameters will be established.

Keywords: acidity, estuary, salinity, sea

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Authors: Hamza Benyahia, Mostapha Tarfaoui, Ahmed El-Moumen, Djamel Ouinas

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Composite pipes are commonly used in the oil industry, and extreme flow of hot and cold gas fluid can cause degradation of their mechanical performance and properties. Therefore, it is necessary to consider thermomechanical behavior as an important parameter in designing these tubular structures. In this paper, an experimental study is conducted on composite glass/epoxy tubes, with a thickness of 6.2 mm and 86 mm internal diameter made by filament winding of (Փ = ± 55°), to investigate the effects of extreme thermal condition on their mechanical properties b over a temperature range from -40 to 80°C. The climatic chamber is used for the thermal aging and then, combine split disk system is used to perform tensile tests on these composite pies. Thermal aging is carried out for 8hr but each specimen was subjected to various temperature ranges and then, uniaxial tensile test is conducted to evaluate their mechanical performance. Experimental results show degradation in the mechanical properties of composite pipes with an increase in temperature. The rigidity of pipes increases progressively with a decrease in thermal load and results in a radical decrease in their elongation before fracture, thus, decreasing their ductility. However, with an increase in the temperature, there is a decrease in the yield strength and an increase in yield strain, which confirmed an increase in the plasticity of composite pipes.

Keywords: composite pipes, thermal-mechanical properties, filament winding, thermal degradation

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Felipe Haleyson Ribeiro dos Santos, Edson Ronaldo Guarido Filho

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This article aims to review the literature on performance in police organizations. For that, the inOrdinatio method was adopted, which defines the form of selection and classification of articles. The search was carried out in databases, which resulted in a total of 619 documents that were cataloged and classified with the support of the Mendeley software. The theoretical scope intended here is to identify how performance in police organizations has been studied. After deepening the analysis and focusing on management, it was possible to classify the articles into three levels: individual, organizational, and institutional. However, to our best knowledge, no studies were found that addressed the performance relationship between the levels, which can be seen as a suggestion for further research.

Keywords: police management, performance, management, multi-level

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Authors: Sudip Kumar Kundu, Charu Singh

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The climatic condition over Indian region is highly dependent on monsoon. India receives maximum amount of rainfall during southwest monsoon. Indian economy is highly dependent on agriculture. The presence of flood and drought years influenced the total cultivation system as well as the economy of the country as Indian agricultural systems is still highly dependent on the monsoon rainfall. The present study has been planned to investigate the flood and drought years for the north-west Himalayan region from 1951 to 2014 by using area average Indian Meteorological Department (IMD) rainfall data. For this investigation the Normalized index (NI) has been utilized to find out whether the particular year is drought or flood. The data have been extracted for the north-west Himalayan (NWH) region states namely Uttarakhand (UK), Himachal Pradesh (HP) and Jammu and Kashmir (J&K) to find out the rainy season average rainfall for each year, climatological mean and the standard deviation. After calculation it has been plotted by the diagrams (or graphs) to show the results- some of the years associated with drought years, some are flood years and rest are neutral. The flood and drought years can also relate with the large-scale phenomena El-Nino and La-Lina.

Keywords: IMD, rainfall, normalized index, flood, drought, NWH

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Authors: Molakala Mourya Vardhan Reddy, Kandimala Revanth, Koduru Sumanth, Beena B. M.

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Emergency response and recovery operations are severely hampered by natural catastrophes, especially floods. Understanding post-flood scenarios is essential to disaster management because it facilitates quick evaluation and decision-making. To this end, we introduce FloodNet, a brand-new high-resolution aerial picture collection created especially for comprehending post-flood scenes. A varied collection of excellent aerial photos taken during and after flood occurrences make up FloodNet, which offers comprehensive representations of flooded landscapes, damaged infrastructure, and changed topographies. The dataset provides a thorough resource for training and assessing computer vision models designed to handle the complexity of post-flood scenarios, including a variety of environmental conditions and geographic regions. Pixel-level semantic segmentation masks are used to label the pictures in FloodNet, allowing for a more detailed examination of flood-related characteristics, including debris, water bodies, and damaged structures. Furthermore, temporal and positional metadata improve the dataset's usefulness for longitudinal research and spatiotemporal analysis. For activities like flood extent mapping, damage assessment, and infrastructure recovery projection, we provide baseline standards and evaluation metrics to promote research and development in the field of post-flood scene comprehension. By integrating FloodNet into machine learning pipelines, it will be easier to create reliable algorithms that will help politicians, urban planners, and first responders make choices both before and after floods. The goal of the FloodNet dataset is to support advances in computer vision, remote sensing, and disaster response technologies by providing a useful resource for researchers. FloodNet helps to create creative solutions for boosting communities' resilience in the face of natural catastrophes by tackling the particular problems presented by post-flood situations.

Keywords: image classification, segmentation, computer vision, nature disaster, unmanned arial vehicle(UAV), machine learning.

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Authors: Anton G. Akulichev, Avinash Tiwari, Ben Alcock, Andreas Echtermeyer

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Low temperatures are known to pose a major threat for polymers; many are prone to excessive stiffness or even brittleness. There is a technology gap between the properties of existing elastomeric sealing materials and the properties needed for service in extremely cold regions. Moreover, some aspects of low temperature behaviour of rubber are not thoroughly studied and understood. The paper presents results of laboratory testing of a conventional oilfield HNBR (hydrogenated nitrile butadiene rubber) elastomer at low climatic temperatures above and below its glass transition point, as well as the performance of some filled HNBR formulations. Particular emphasis in the experiments is put on rubber viscoelastic characteristics studied by Dynamic Mechanical Analysis (DMA) and quasi-static mechanical testing results at low temperatures. As demonstrated by the stress relaxation and DMA experiments the transition region near Tg of the studied compound has the most striking features, like rapid stress relaxation, as compared to the glassy and rubbery plateau. In addition the quasi-static experiments show that molecular movement below Tg is not completely frozen, but rather evident and manifested in a certain stress decay as well. The effect of temperature and filler additions on typical mechanical and other properties of the materials is also discussed.

Keywords: characterization, filled elastomers, HNBR, low temperature

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Authors: Yousef Almutairi, Yajue Wu

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Various large-scale trends have characterized the current century thus far, including increasing shifts towards urbanization and greater movement. It is predicted that there will be 9.3 billion people on Earth in 2050 and that over two-thirds of this population will be city dwellers. Moreover, in larger cities worldwide, mass transportation systems, including underground systems, have grown to account for the majority of travel in those settings. Underground networks are vulnerable to fires, however, endangering travellers’ safety, with various examples of fire outbreaks in this setting. This study aims to increase knowledge of the impacts of extreme climatic conditions on fires, including the role of the high ambient temperatures experienced in Middle Eastern countries and specifically in Saudi Arabia. This is an element that is not always included when assessments of fire safety are made (considering visibility, temperatures, and flows of smoke). This paper focuses on a tunnel within Riyadh’s underground system as a case study and includes simulations based on computational fluid dynamics using ANSYS Fluent, which investigates the impact of various ventilation systems while identifying smoke density, speed, pressure and temperatures within this tunnel.

Keywords: fire, subway tunnel, CFD, mechanical ventilation, smoke, temperature, harsh weather

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: G. Çetinkaya, M. Yıldız, P. Çetinkaya

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Tourism is one of the world’s largest industries and its total contribution to the global economy in 2014 was US$7.6 trillion, which equates to 9.8% of total economy GDP in 2014. Mountains are important regions for tourism industry and its second most popular tourist destinations after coastal regions. Hiking and trekking are most popular activity in mountains region and it is estimated that more than 50 million people visit mountains each year. So that hiking was come out to individual activity and it’s to be a massive event. Nowadays hiking is commercialized and mostly it’s become organized by travel agency and tour operators. Travel agency which is offering hiking activities to know the demands of the individuals involved in these activities and is required to submit to it for services. The aim of this study to determined hiking participant expectation from hiking by travel agency perspective. 34 travel agency officials participated in the study. Data were collected by questionnaire developed by the researchers. Results show that according to travel agency officials “visual quality” is the most important expectation factor for hikers. And other expectation factors are “safety”, “accessibility”, “unspoiled local service”, “walking grade”, “expert guidance service”, “popularity of trail”, “uncrowded trail”, “substructure facilities”, “relevant cost”, “guidebook” and “suitable climatic conditions”.

Keywords: expectation, hikers, travel agency, mountain tourism

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Hanan M. Taleb

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Since buildings are a major consumer of energy, their potential impact on the environment is considerable. Therefore, expanding the application of low energy architecture is of the utmost importance. Designing with nature is also one of the most attractive methods of design for many architects and designers because it creates a pathway to sustainability. One feature of designing with nature is the use of green roofing which aims to cover the roof with vegetation either partially or completely. Appreciably, green roofing in a building has many advantages including absorbing rainwater, providing thermal insulation, enhancing the ecology, creating a peaceful retreat for people and animals, improving air quality and helping to offset the air temperature and heat island effect. The aim of this paper is to monitor energy saving in the residential buildings of Dubai after applying green roofing techniques. The paper also attempts to provide a thermal analysis after the application of green roofs. A villa in Dubai was chosen as a case study. With the aid of energy simulation software, namely Design Builder, as well as manual recording and calculations, the energy savings after applying the green roofing were detected. To that extent, the paper draws some recommendations with regard to the types of green roofing that should be used in these particular climatic conditions based on this real experiment that took place over a one year period.

Keywords: residential buildings, Dubai, energy saving, green roofing, CFD, thermal comfort

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Authors: Ouerchefani Dalel, Mahdhaoui Basma

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Land use change is one of the important factors needed to evaluate later on the impact of human actions on land degradation. This work present the application of a methodology based on remote sensing for evaluation land use change in an arid region of Tunisia. This methodology uses Landsat TM and ETM+ images to produce land use maps by supervised classification based on ground truth region of interests. This study showed that it was possible to rely on radiometric values of the pixels to define each land use class in the field. It was also possible to generate 3 land use classes of the same study area between 1988 and 2011.

Keywords: land use, change, remote sensing, GIS

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

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Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

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Authors: Fereshteh Pashaei Kamali, Elham Kazemi, Shokooh Neshani Fam

Abstract:

The subject of sustainable development is a reformist revision of modernism and tradition, proposing reconciliatory strategies between these two. Sustainability in architecture cannot only be interpreted as the construction’s physical stability, but also as stability, the preserving of the continuous totality of earth and its energy resources as well, whose available resources and materials should be employed more efficiently. In other words, by referring to the building ecology, emphasizing the combinatory capacity of the building with the environmental factors (existence context), the aim of sustainability is to achieve spatial quality and comfort, as well as proper design in the architectural composition. To achieve these traditional Iranian architecture objectives, it is essential to plan on protecting the environment, maintaining aesthetic measures and responding to the needs of each climatic region. This study was conducted based on the descriptive-analytical method, and aimed to express the design patterns compatible with the climate of the Tabriz residential fabric. The present article attempts to express the techniques and patterns used in traditional Iranian architecture, especially the Tabriz Sharbatoglu houses and Ghadaki houses, which are supposed to be in accordance with modern concepts of sustainable architecture.

Keywords: sustainable architecture, climate, Tabriz, Sharbatoglu house, Ghadaki house

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Authors: H. Chabalala, A. Grootboom, M. Tang

Abstract:

The innovative development, production, and clinical utilisation of African natural medicines requires frameworks from systematisation, innovation, registration. Afrika faces challenges when it comes to these sectors. The opposite is the case as is is evident in ancient Asian (Traditional Chinese Medicine and Indian Ayurveda and Siddha) medical systems, which are interfaced into their respective national health and educational systems. Afrikan Natural Medicines (ANMs) are yet to develop systematisation frameworks, i.e. disease characterisation and medicines classification. This paper explores classical medical systems drawn from Afrikan and Chinese experts in natural medicines. An Afrikological research methodology was used to conduct in-depth interviews with 20 key respondents selected through purposeful sampling technique. Data was summarised into systematisation frameworks for classical disease theories, patient categorisation, medicine classification, aetiology and pathogenesis of disease, diagnosis and prognosis techniques and treatment methods. It was discovered that ancient Afrika had systematic medical cosmologies, remnants of which are evident in most Afrikan cultural health practices. Parallels could be drawn from classical medical concepts of antiquity, like Chinese Taoist and Indian tantric health systems. Data revealed that both the ancient and contemporary ANM systems were based on living medical cosmologies. The study showed that African Natural Healing Systems have etiological systems, general pathogenesis knowledge, differential diagnostic techniques, comprehensive prognosis and holistic treatment regimes. Systematisation models were developed out of these frameworks, and this could be used for evaluation of clinical research, medical application including development of curriculum for high-education. It was envisaged that frameworks will pave way towards the development, production and commercialisation of ANMs. This was piloted in inclusive innovation, technology transfer and commercialisation of South African natural medicines, cosmeceuticals, nutraceuticals and health infusions. The central model presented here in will assist in curriculum development and establishment of Afrikan Medicines Hospitals and Pharmaceutical Industries.

Keywords: African Natural Medicines, Indigenous Knowledge Systems, Medical Cosmology, Clinical Application

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Authors: Hameed Ullah, Shujaul Mulk Khan, Zahid Ullah, Zeeshan Ahmad Sadia Jahangir, Abdullah, Amin Ur Rahman, Muhammad Suliman, Dost Muhammad

Abstract:

The interrelationship between vegetation and abiotic variables inside an ecosystem is one of the main jobs of plant scientists. This study was designed to investigate the vegetation structure and species diversity along with the environmental variables in the Yakhtangay hill district Shangla of the Himalayan Mountain series Pakistan by using multivariate statistical analysis. Quadrat’s method was used and a total of 171 Quadrats were laid down 57 for Tree, Shrubs and Herbs, respectively, to analyze the phytosociological attributes of the vegetation. The vegetation of the selected area was classified into different Life and leaf-forms according to Raunkiaer classification, while PCORD software version 5 was used to classify the vegetation into different plants communities by Two-way indicator species Analysis (TWINSPAN). The CANOCCO version 4.5 was used for DCA and CCA analysis to find out variation directories of vegetation with different environmental variables. A total of 114 plants species belonging to 45 different families was investigated inside the area. The Rosaceae (12 species) was the dominant family followed by Poaceae (10 species) and then Asteraceae (7 species). Monocots were more dominant than Dicots and Angiosperms were more dominant than Gymnosperms. Among the life forms the Hemicryptophytes and Nanophanerophytes were dominant, followed by Therophytes, while among the leaf forms Microphylls were dominant, followed by Leptophylls. It is concluded that among the edaphic factors such as soil pH, the concentration of soil organic matter, Calcium Carbonates concentration in soil, soil EC, soil TDS, and physiographic factors such as Altitude and slope are affecting the structure of vegetation, species composition and species diversity at the significant level with p-value ≤0.05. The Vegetation of the selected area was classified into four major plants communities and the indicator species for each community was recorded. Classification of plants into 4 different communities based upon edaphic gradients favors the individualistic hypothesis. Indicator Species Analysis (ISA) shows the indicators of the study area are mostly indicators to the Himalayan or moist temperate ecosystem, furthermore, these indicators could be considered for micro-habitat conservation and respective ecosystem management plans.

Keywords: species richness, edaphic gradients, canonical correspondence analysis (CCA), TWCA

Procedia PDF Downloads 153