Search results for: land cover classification

Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Guo Rui, Cao Xinhu

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In recent decades, the world has endured severe challenges in light of climate change, epidemics, geopolitics, terrorism, economic uncertainties, as well as regional conflicts and rivalries. The appropriate use of critical infrastructures (Cis) is confronted. Ports, as typical Cis cover more than 80% of the global freight movement. Within this context, even the minimal disruption of port operations could cause malfunction of the holistic supply chain network and substantial economic losses. Hence, it is crucial to evaluate port performance from the perspective of resilience. Research on resilience and risk/safety management has been increasing, however, it needs more attention, as it could prevent potential socio-economic losses and inspire decision-makers to make resilience-based decisions to answer the challenges, such as COVID-19. To facilitate better moves from decision-makers, ports need to identify proper factors influencing port resilience. Inappropriately influenced factor selection could have a cascading effect on undesirable port performances. Thus, a systematic evaluation of factors is essential to stimulate the improvement process of port resilience investigation. This study zooms into container ports considering their critical role in international trade and global supply chains. 440 articles are selected after relevance ranking, and consequently, 62 articles are scrutinized after the title and abstract screening. Forty-one articles are included for bibliographic analysis in the end. It is found that there is no standardized index system to measure port resilience. And most studies evaluate port resilience merely in the recovery phase. Only two articles cover absorption, adaption and recovery state. However, no literature involves the prevention state. Hence, a uniform resilience index system is expected with a clear resilience definition. And port safety and security should also be considered while evaluating port resilience.

Keywords: port resilience, port safety and security, literature review, index system, port performance

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Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Kristin Murray

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The proposed paper exams prologues in 20th and 21st century American literature in order to show how Native American writer Tommy Orange’s Prologue in his 2018 novel There, Thereis different. In an interview about his 2018 novel There, There, explains he feels “a kind of burden to catch the general reader up with what really happened, because history has got it so wrong and still continue to” (Laubernds). Orange, thus, includes a “Prologue” in his novel to do this work, catching readers upon Native Americans and their history. Prologues are usually from the narrator’s voice, a character’s voice, or even from a fictionalized version of the author, but the tone of Orange’s “Prologue” is that of a non-fictional first-person essayist. Examining prologues in American literature posits Orange’s prologue outside the norm. This paper also examines other introductory sections, the preface, in particular. The research and examination reveal that Orange is adding his personal voice in the Prologue to the multiple narratorsof the novel, and his is the voice of a writer who knows that his audience comes to his novel with a plethora of misinformation. The truths he tells are horrifying and hopeful. He tells of Thanksgiving as a “land deal” and a “successful massacre,” but he also tellsreaders how urban Indians have found a sense of the land, even through concrete. Native American writers contributed and still contribute to the genre of autobiography in ways that have changed our understanding of this genre. This examination of Orange’s Prologue reveals the new and unexpected way to view this often under-examined introductory section, the prologue.

Keywords: native american literature, prologues, prefaces, 20th century american literature

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Ait Hamlet Smina, Bensoltane Samira, Djekoun Mohamed, Berrebbah Houria

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In Algeria, the use of insecticides and other phytosanitary products are considerably spreading with the development of agriculture. But, the analyses of the residues of pesticides are not systematically made. In this context, we estimated through an experimental study, the effect of a neonicotinoid insecticide, the thiamethoxam which is used as a commercial preparation on the land snail Helix aspersa. This snail is one of the most abundant gastropod in North-East Algeria. Little information is available in the literature concerning the study of the biochemical markers of mollusks which are exposed to insecticides and especially, thiamethoxam.In this work, adult snails Helix aspersa were used to estimate the effect of a neonicotinoid insecticide (thiamethoxam) on the acetylcholinesterase (AChE), glutathione S-transferase (GST) and catalase (CAT) activities in this gastropod after a treatment of 6 weeks. During this period, snails were exposed by ingestion and contact to fresh lettuce leaves which were soaked with an insecticide solution. The thiamethoxam test solutions were 0, 25, 50, 100 and 200 mg/L, which are lower or equal to the concentrations that are applied in field. The results showed that the enzymatic activities of AChE and GST and CAT increased significantly with a dose-dependent manner. These results confirmed the toxic effect of thiamethoxam on snails exposed to the lettuce contaminated with this neonicotinoid insecticide, likely to be used as biomarker of exposure, at first to thiamethoxam then to other insecticides belonging to the same chemical family, currently present in the environment.

Keywords: helix aspersa, insecticide, thiamethoxam, AChE, GST, CAT

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Jonas Volungevicius, Kristina Amaleviciute, Rimantas Vaisvalavicius, Alvyra Slepetiene, Darijus Veteikis

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Lithuanian territory is characterized by landscape with prevailing morain hills and clayey lowlands. The largest part of it has endured agrogenization of various degrees which was the cause of changes both in the structure of landscape and soil cover, transformations of soil profile and degradation of natural background soils. These changes influence negatively geoecological potential of landscape and soil and contribute to the weakening of the sustainability of agroecosystems. Research objective: to reveal the landscape agrogenization induced alterations of catenae and their appendant soil profiles in Lithuanian morain hills and clayey lowlands. Methods: Soil cover analysis and catenae charting was conducted using landscape profiling; soil morphology detected and soil type identified following WRB 2014. Granulometric composition of soil profiles was obtained by laser diffraction method (lazer diffractometer Mastersizer 2000). pH was measured in H2O extraction using potentiometric titration; SOC was determined by the Tyurin method modified by Nikitin, measuring with spectrometer Cary 50 (VARIAN) in 590 nm wavelength using glucose standards. Results: analysis showed that the decrease of forest vegetation and the other natural landscape components following the agrogenization of the research area influenced differently but significantly the structural alterations in soil cover and vertical soil profile. The research detected that due to landscape agrogenization, the suppression of zone-specific processes and the intensification of inter-zone processes determined by agrogenic factors take place in Lithuanian agroecosystems. In forested hills historically prevailing Retisols and Histosols territorial complex is transforming into the territorial complex of Regosols, Deluvial soils and drained Histosols. Processes taking place are simplification of vertical profile structure, intensive rejuvenation of profile, disappearance of the features of zone-specific soil-forming processes (podzolization, lessivage, gley formation). Erosion and deluvial processes manifest more intensively and weakly accumulating organic material more intensively spread in a vertical soil profile. The territorial soil complex of Gleyic Luvisols and Gleysols dominating in forested clayey lowlands subjected to agrogenization is transformed into the catena of drained Luvisols and pseudo Cambisols. The best expressed are their changes in moisture regime (morphological features of gley and stagnic properties are on decline) together with alterations of pH and distribution and intensity of accumulation of organic matter in profile. A specific horizon, antraquic, uncharacteristic to natural soil formation is appearing. Important to note that due to deep ploughing and other agrotechnical measures, the natural vertical differentiation of clay particles in a soil profile is destroyed which leads not only to alterations of physical qualities of soil, but also encumbers the identification of Luvisols by creating presumptions to misidentify them as Cambisols. The latter have never developed in these ecosystems under the present climatic conditions. Acknowledgements: This work was supported by the National Science Program: The effect of long-term, different-intensity management of resources on the soils of different genesis and on other components of the agro-ecosystems [grant number SIT-9/2015] funded by the Research Council of Lithuania.

Keywords: agroecosystems, landscape agrogenization, luvisols, retisols, transformation of soil profile

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Authors: Temenuzhka Spasova, Nadya Yanakieva

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Snow modulates the hydrological cycle and influences the functioning of ecosystems and is a significant resource for many populations whose water is harvested from cold regions. Snow observations are important for validating climate models. The accumulation and rapid melt of snow are two of the most dynamical seasonal environmental changes on the Earth’s surface. The actuality of this research is related to the modern tendencies of the remote sensing application in the solution of problems of different nature in the ecological monitoring of the environment. The subject of the study is the dynamic during the different seasons on Livingstone Island, South Shetland Islands in Antarctica and on Svalbard in Arctic. The objects were analyzed and mapped according to the Еuropean Space Agency data (ESA), acquired by sensors Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel 2 MSI and GIS. Results have been obtained for changes in snow coverage during the summer-winter transition and its dynamics in the two hemispheres. The data used is of high time-spatial resolution, which is an advantage when looking at the snow cover. The MSI images are with different spatial resolution at the Earth surface range. The changes of the environmental objects are shown with the SAR images and different processing approaches. The results clearly show that snow and snow melting can be best registered by using SAR data via hh- horizontal polarization. The effect of the researcher on aerospace data and technology enables us to obtain different digital models, structuring and analyzing results excluding the subjective factor. Because of the large extent of terrestrial snow coverage and the difficulties in obtaining ground measurements over cold regions, remote sensing and GIS represent an important tool for studying snow areas and properties from regional to global scales.

Keywords: climate changes, GIS, remote sensing, SAR images, snow coverage

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Jeron Maheswaran, Sudath C. Siriwardane

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In fatigue analysis, the structural detail of tubular joint has taken great attention among engineers. The DNV-RP-C203 is covering this topic quite well for simple and clear joint cases. For complex joint and geometry, where joint classification isn’t available and limitation on validity range of non-dimensional geometric parameters, the challenges become a fact among engineers. The classification of joint is important to carry out through the fatigue analysis. These joint configurations are identified by the connectivity and the load distribution of tubular joints. To overcome these problems to some extent, this paper compare the fatigue life of tubular joints in offshore jacket according to the stress concentration factors (SCF) in DNV-RP-C203 and finite element method employed Abaqus/CAE. The paper presents the geometric details, material properties and considered load history of the jacket structure. Describe the global structural analysis and identification of critical tubular joints for fatigue life estimation. Hence fatigue life is determined based on the guidelines provided by design codes. Fatigue analysis of tubular joints is conducted using finite element employed Abaqus/CAE [4] as next major step. Finally, obtained SCFs and fatigue lives are compared and their significances are discussed.

Keywords: fatigue life, stress-concentration factor, finite element analysis, offshore jacket structure

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Authors: Omid Kalatpour, Mohammadreza Ajdari

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This article studied the possible causes of human error-induced incidents in the petrochemical industry maintenance activities by using Human Factors Analysis and Classification System (HFACS). The purpose of the study was anticipating and identifying these causes and proposing corrective and preventive actions. Maintenance department in a petrochemical company was selected for research. A checklist of human error-induced incidents was developed based on four HFACS main levels and nineteen sub-groups. Hierarchical task analysis (HTA) technique was used to identify maintenance activities and tasks. The main causes of possible incidents were identified by checklist and recorded. Corrective and preventive actions were defined depending on priority. Analyzing the worksheets of 444 activities in four levels of HFACS showed 37.6% of the causes were at the level of unsafe actions, 27.5% at the level of unsafe supervision, 20.9% at the level of preconditions for unsafe acts and 14% of the causes were at the level of organizational effects. The HFACS sub-groups showed errors (24.36%) inadequate supervision (14.89%) and violations (13.26%) with the most frequency. According to findings of this study, increasing the training effectiveness of operators and supervision improvement respectively are the most important measures in decreasing the human error-induced incidents in petrochemical industry maintenance.

Keywords: human error, petrochemical industry, maintenance, HFACS

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Authors: Gerald Steyn

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Sub-Saharan Africa is the world's fastest urbanizing region, but approximately 60 to 70 percent of urban African households are poor and living in slums. Although influential global institutions such as the World Bank propagate a new approach to housing and land policies, sustainable African urban concepts have yet to be applied significantly or even convincingly conceptualized. Most African city planners, urban designers, architects, policymakers, and developers have been trained in Western curriculums and continue to practice and plan according to such formal paradigms. Only a few activists promote Post-Colonial Afrocentric urbanism, recognizing the imperative of foregrounding the needs of low-income people. There is a vast body of authoritative literature on analyzing poverty and slums in sub-Saharan Africa and on promoting the need for land and city planning reform. However, of the latter, only a few venture beyond advising and sometimes outlining policy changes. The current study moves beyond a purely theoretical discourse into the realm of practice by designing replicable diagrammatic concepts at different urban scales. The guiding philosophy was that land-use concepts and urban requirements favoring low-income households must be fully integrated into the larger conurbation. Information was derived from intensive research over two decades, involving literature surveys and observations during regular travels into East and Southern Africa. Appropriate existing urban patterns, particularly vernacular and informal, were subsequently analyzed and reimagined as precedents to inform and underpin the represented design concepts. Five interrelated concepts are proposed, ranging in scale from (1) regional to (2) cities and (3) urban villages to (4) neighborhoods and (5) streets. Each concept is described, first in terms of its context and associated issues of concern, followed by a discussion of the patterns available to inform a possible solution, and finally, an explanation and graphic illustration of the proposal. Since each of the five concepts is unfolded from existing informal and vernacular practices studied in situ, the approach is entirely bottom-up. Contrary to an idealized vision of the African city, this study proposes actual concepts for critical assessment by peers in the tradition of architectural research in design.

Keywords: african urban concepts, post-colonial afrocentric urbanism, sub-saharan africa, sustainable african urban design

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Authors: Alisha Sinha, Laxmi Kant Sharma

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Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters.

Keywords: wildfire susceptibility mapping, LST, random forest, GEE, MODIS, climatic parameters

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Hande Ünlü, Yu Morishita

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The total area of forest land in Japan accounts for 67% of the national land; however, despite this wealth and hundred years history of silviculture, today Japanese forestry faces socio-economic stagnation in forestry. While the growing gap in the people-forest relationship causes the depopulation of many forest villages, this paper introduces a methodology aiming to develop a place-specific approach in revitalizing this relationship. The paper focuses on a case study from Taiki town in the Hokkaido region to analyze the place's specific socio-economic requirements through interviews and workshops with the local experts, researchers, and stakeholders. Based on the analyzed facts, a master outline of design requirements is developed to produce locally sourced architectural embodiments that aim to act as a unifying element between the forests and the people of Taiki town. In parallel, the proposed methodology aims to generate a cycle of research feed and a researcher retreat, a definition given by Memu Earth Lab to the researchers' stay at Memu in Taiki town for a defined period to analyze local resources, for the continuous improvement of the introduced methodology to revitalize the interaction between people and forest through architecture.

Keywords: architecture, Japanese forestry, local timber, people-forest relationship, research platform

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Authors: A. Merzouki, N. Haddaoui

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Polymers have a large reputation as electric insulators. These materials are characterized by weak weight, reduced price and a large domain of physical and chemical properties. They conquered new application domains that were until a recent past the exclusivity of metals. In this work, we used some composite materials (polymers/conductive fillers), as electrodes and we try to cover them with metallic lead layers in order to use them as courant collector grids in lead-acid battery plates.

Keywords: electrodeposition, polymer composites, carbon black, acetylene black

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Authors: Lana Whiskeyjack, Kyle Napier

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The Spirit of the language embodies the motivation for indigenous people to connect with the indigenous language of their lineage. While the concept of the spirit of the language is often woven into the discussion by indigenous language revitalizationists, particularly those who are indigenous, there are few tangible terms in academic research conceptually actualizing the term. Through collaborative work with indigenous language speakers, elders, and learners, this research sets out to identify the spirit of the language, the catalysts of disconnection from the spirit of the language, and the sources of reconnection to the spirit of the language. This work fundamentally addresses the terms of engagement around collaboration with indigenous communities, itself inviting a decolonial approach to community outreach and individual relationships. As indigenous researchers, this means beginning, maintain, and closing this work in the ceremony while being transparent with community members in this work and related publishing throughout the project’s duration. Decolonizing this approach also requires maintaining explicit ongoing consent by the elders, knowledge keepers, and community members when handling their ancestral and indigenous knowledge. The handling of this knowledge is regarded in this work as stewardship, both in the handling of digital materials and the handling of ancestral Indigenous knowledge. This work observes recorded conversations in both nêhiyawêwin and English, resulting from 10 semi-structured interviews with fluent nêhiyawêwin speakers as well as three structured dialogue circles with fluent and emerging speakers. The words were transcribed by a speaker fluent in both nêhiyawêwin and English. The results of those interviews were categorized thematically to conceptually actualize the spirit of the language, catalysts of disconnection to thespirit of the language, and community voices methods of reconnection to the spirit of the language. Results of these interviews vastly determine that the spirit of the language is drawn from the land. Although nêhiyawêwin is the focus of this work, Indigenous languages are by nature inherently related to the land. This is further reaffirmed by the Indigenous language learners and speakers who expressed having ancestries and lineages from multiple Indigenous communities. Several other key differences embody this spirit of the language, which include ceremony and spirituality, as well as the semantic worldviews tied to polysynthetic verb-oriented morphophonemics most often found in indigenous languages — and of focus, nêhiyawêwin. The catalysts of disconnection to the spirit of the language are those whose histories have severed connections between Indigenous Peoples and the spirit of their languages or those that have affected relationships with the land, ceremony, and ways of thinking. Results of this research and its literature review have determined the three most ubiquitously damaging interdependent factors, which are catalysts of disconnection from the spirit of the language as colonization, capitalism, and Christianity. As voiced by the Indigenous language learners, this work necessitates addressing means to reconnect to the spirit of the language. Interviewees mentioned that the process of reconnection involves a whole relationship with the land, the practice of reciprocal-relational methodologies for language learning, and indigenous-protected and -governed learning. This work concludes in support of those reconnection methodologies.

Keywords: indigenous language acquisition, indigenous language reclamation, indigenous language revitalization, nêhiyawêwin, spirit of the language

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Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua

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Vegetation plays a major role in deciding the flow parameters in an open channel. It enhances the aesthetic view of the revetments. The major types of vegetation in river typically comprises of herbs, grasses, weeds, trees, etc. The vegetation in an open channel usually consists of aquatic plants with complete submergence, partial submergence, floating plants. The presence of vegetative plants can have both benefits and problems. The major benefits of aquatic plants are they reduce the soil erosion, which provides the water with a free surface to move on without hindrance. The obvious problems are they retard the flow of water and reduce the hydraulic capacity of the channel. The degree to which the flow parameters are affected depends upon the density of the vegetation, degree of submergence, pattern of vegetation, vegetation species. Vegetation in open channel tends to provide resistance to flow, which in turn provides a background to study the varying trends in flow parameters having vegetative growth in the channel surface. In this paper, an experiment has been conducted on a meandering channel having sinuosity of 1.33 with rigid vegetation cover to investigate the effect on flow parameters, variation of manning’s n with degree of the denseness of vegetation, vegetation pattern and submergence criteria. The measurements have been carried out in four different cross-sections two on trough portion of the meanders, two on the crest portion. In this study, the analytical solution of Shiono and knight (SKM) for lateral distributions of depth-averaged velocity and bed shear stress have been taken into account. Dimensionless eddy viscosity and bed friction have been incorporated to modify the SKM to provide more accurate results. A mathematical model has been formulated to have a comparative analysis with the results obtained from Shiono-Knight Method.

Keywords: bed friction, depth averaged velocity, eddy viscosity, SKM

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Girija Jha

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This paper investigates the need of cotton to integrate sustainability. The methodology used in the paper is to do secondary research to find out the various environmental implications of cotton as textile material across its life cycle and try to look at ways and possibilities of minimizing its ecological footprint. Cotton is called ‘The Fabric of Our Lives’. History is replete with examples where this fabric used to be more than a fabric of lives. It used to be a miracle fabric, a symbol India’s pride and social Movement of Swaraj, Gandhijee’s clarion call to self reliance. Cotton is grown in more than 90 countries across the globe on 2.5 percent of the world's arable land in countries like China, India, United States, etc. accounting for almost three fourth of global production. But cotton as a raw material has come under the scanner of sustainability experts because of myriad reasons a few have been discussed here. It may take more than 20,000 liters of water to produce 1kg of cotton. Cotton harvest is primarily done from irrigated land which leads to Salinization and depletion of local water reservoirs, e.g., Drying up of Aral Sea. Cotton is cultivated on 2.4% of total world’s crop land but accounts for 24% usage of insecticide and shares the blame of 11% usage of pesticides leading to health hazards and having an alarmingly dangerous impact on the ecosystem. One of the possible solutions to these problems as proposed was GM, Genetically Modified cotton crop. However, use of GM cotton is still debatable and has many ethical issues. The practice of mass production and increasing consumerism and especially fast fashion has been major culprits to disrupt this delicate balance. Disposable fashion or fast fashion is on the rise and cotton being one of the major choices adds on to the problem. Denims – made of cotton and have a strong fashion statement and the washes being an integral part of their creation they share a lot of blame. These are just a few problems listed. Today Sustainability is the need of the hour and it is inevitable to incorporate have major changes in the way we cultivate and process cotton to make it a sustainable choice. The answer lies in adopting minimalism and boycotting fast fashion, in using Khadi, in saying no to washed denims and using selvedge denims or using better methods of finishing the washed out fabric so that the environment does not bleed blue. Truly, the answer lies in integrating state of art technology with age old sustainable practices so that the synergy of the two may help us come out of the vicious circle.

Keywords: cotton, sustainability, denim, Khadi

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Okunola Solomon Olufemi

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The study focused on the contribution of urban agriculture to the livelihood of 107 respondents in Kwara State of Nigeria. The study employed structured questionnaire to collect relevant data for the study while descriptive statistics such as frequency distribution and percentage were employed to analyses the objectives. Most respondents (82.9%), were one time or the other in a married state and they had formal education (80.4%) while many of the lot (58.9%) had superior education ranging from OND to Ph.D level. These farmers were either retired government workers or those trying to argument their family income or trying to be food sufficient. Most of the respondents (77.4%) had a farm size of less than or equal to 3 hectares showing that most the urban farmers were smallholders which might be as a result of stiff completion for land resource. Most of the respondents had a relatively large family size of 6 and above members. Most respondents used family labor (59.8%). Respondents in the study area also made use of the cooperatives and daily contributions for loanable funds, while few respondents utilized the formal sector. .Urban agriculture accounted for 84.4% of the Livelihood outcomes of the respondents. While non-farming activities contributed 17.6%.Most of the respondents (31.8%) participated in non-farming activities to generate extra income while their major constraints were shortage of land both in term of access and tenure (34.1%), limited access to resource and agricultural inputs (29.3%), and prohibitive urban policies and regulation (23.2%).

Keywords: poverty throes, urban agriculture, rigors of farming, non-farming, married state

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Hung-Chu Chen, Han Qi, Bauke de Vries

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Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared.

Keywords: energy demand model, geographically weighted regression, normalized difference built-up index, normalized difference vegetation index, spatial statistics

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Authors: Cornelius Michael Ekenta

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The study determined institutional and technological factors that influence the adoption of tenera oil palm production practices with a gender dimension among smallholder farmers in Edo State, Nigeria. Primary data were generated with use of questionnaire administered to 155 males and 137 female respondents. Results show that the level of adoption of tenera oil palm production practices was low for both male and females. Tobi regression result shows that land ownership structure and affordability at 1% significance influenced male adoption of tenera oil palm production practices while age and level of income at 1% significance influenced female in the adoption. The major roles of male as reported in adopting process were purchase of seedlings, clearing of bush for planting and selling of cut bunches while the major roles of female were periodic weeding, gathering of cut bunches and mulching of palm field. The major constraint faced by male in adoption process were high cost of labour while for females is drudgery nature of the work. The study recommended that the Land Use Act of 1978 should be enforced to help women and non-indigenes to have sizeable farm lands, Government should empower Agricultural Development Programme (ADP) by employing more extension personnel to increase their contacts with the farmers.

Keywords: gender, adoption, variety, oil, tenera, Edo

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Authors: Armand Okende, Benjamin Beaumont

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Introduction: Deforestation is a change in the structure and composition of flora and fauna, which leads to a loss of biodiversity, production of goods and services and an increase in fires. It concerns vast territories in tropical zones particularly; this is the case of the territory of Bolobo in the current province of Maï- Ndombe in the Democratic Republic of Congo. Indeed, through this study between 2001 and 2018, we believe that it was important to show and analyze quantitatively the important forests changes and analyze quantitatively. It’s the overall objective of this study because, in this area, we are witnessing significant deforestation. Methodology: Mapping and quantification are the methodological approaches that we have put forward to assess the deforestation or forest changes through satellite images or raster layers. These satellites data from Global Forest Watch are integrated into the GIS software (GRASS GIS and Quantum GIS) to represent the loss of forest cover that has occurred and the various changes recorded (e.g., forest gain) in the territory of Bolobo. Results: The results obtained show, in terms of quantifying deforestation for the periods 2001-2006, 2007-2012 and 2013-2018, the loss of forest area in hectares each year. The different change maps produced during different study periods mentioned above show that the loss of forest areas is gradually increasing. Conclusion: With this study, knowledge of forest management and protection is a challenge to ensure good management of forest resources. To do this, it is wise to carry out more studies that would optimize the monitoring of forests to guarantee the ecological and economic functions they provide in the Congo Basin, particularly in the Democratic Republic of Congo. In addition, the cartographic approach, coupled with the geographic information system and remote sensing proposed by Global Forest Watch using raster layers, provides interesting information to explain the loss of forest areas.

Keywords: deforestation, loss year, forest change, remote sensing, drivers of deforestation

Procedia PDF Downloads 133