Search results for: pixel-based change detection

Authors: Orkan Ozcan, Nebiye Musaoglu, Murat Turkes

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Climate change is largely recognized as one of the real, pressing and significant global problems. The concept of ‘climate change vulnerability’ helps us to better comprehend the cause/effect relationships behind climate change and its impact on human societies, socioeconomic sectors, physiographical and ecological systems. In this study, multifactorial spatial modeling was applied to evaluate the vulnerability of a Mediterranean forest ecosystem to climate change. As a result, the geographical distribution of the final Environmental Vulnerability Areas (EVAs) of the forest ecosystem is based on the estimated final Environmental Vulnerability Index (EVI) values. This revealed that at current levels of environmental degradation, physical, geographical, policy enforcement and socioeconomic conditions, the area with a ‘very low’ vulnerability degree covered mainly the town, its surrounding settlements and the agricultural lands found mainly over the low and flat travertine plateau and the plains at the east and southeast of the district. The spatial magnitude of the EVAs over the forest ecosystem under the current environmental degradation was also determined. This revealed that the EVAs classed as ‘very low’ account for 21% of the total area of the forest ecosystem, those classed as ‘low’ account for 36%, those classed as ‘medium’ account for 20%, and those classed as ‘high’ account for 24%. Based on regionally averaged future climate assessments and projected future climate indicators, both the study site and the western Mediterranean sub-region of Turkey will probably become associated with a drier, hotter, more continental and more water-deficient climate. This analysis holds true for all future scenarios, with the exception of RCP4.5 for the period from 2015 to 2030. However, the present dry-sub humid climate dominating this sub-region and the study area shows a potential for change towards more dry climatology and for it to become a semiarid climate in the period between 2031 and 2050 according to the RCP8.5 high emission scenario. All the observed and estimated results and assessments summarized in the study show clearly that the densest forest ecosystem in the southern part of the study site, which is characterized by mainly Mediterranean coniferous and some mixed forest and the maquis vegetation, will very likely be influenced by medium and high degrees of vulnerability to future environmental degradation, climate change and variability.

Keywords: forest ecosystem, Mediterranean climate, RCP scenarios, vulnerability analysis

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Authors: Alsnosy Balbaa, Mohamed Nabil Elnabawi, Yasmin El Meladi

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With the ever-growing concerns of climate change, Mediterranean ports, as vital economic and transport hubs face unique challenges in maintaining operations and infrastructure. This empirical study seeks to understand the current adaptations and preparedness levels of Egyptian Mediterranean ports against climate-induced disruptions. Drawing from a structured questionnaire, the research gathers insights on observed climate impacts, infrastructure adaptations, operational changes, and stakeholder engagement, aiming to shed light on the resilience of these ports in the face of a changing climate.

Keywords: climate, infrastructures, port, mediterranean

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Fateme Aysin Anka, Farzad Kiani

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Forced migration is a situation in which people are forced to leave their homes against their will due to political conflicts, wars and conflicts, natural disasters, climate change, economic crises, or other emergencies. This type of migration takes place under conditions where people cannot lead a sustainable life due to reasons such as security, shelter and meeting their basic needs. This type of migration may occur in connection with different factors that affect people's living conditions. In addition to these general and widespread reasons, water security and resources will be one that is starting now and will be encountered more and more in the future. Forced migration may occur due to insufficient or depleted water resources in the areas where people live. In this case, people's living conditions become unsustainable, and they may have to go elsewhere, as they cannot obtain their basic needs, such as drinking water, water used for agriculture and industry. To cope with these situations, it is important to minimize the causes, as international organizations and societies must provide assistance (for example, humanitarian aid, shelter, medical support and education) and protection to address (or mitigate) this problem. From the international perspective, plans such as the Green New Deal (GND) and the European Green Deal (EGD) draw attention to the need for people to live equally in a cleaner and greener world. Especially recently, with the advancement of technology, science and methods have become more efficient. In this regard, in this article, a multidisciplinary case model is presented by reinforcing the water problem with an engineering approach within the framework of the social dimension. It is worth emphasizing that this problem is largely linked to climate change and the lack of a sustainable water management perspective. As a matter of fact, the United Nations Development Agency (UNDA) draws attention to this problem in its universally accepted sustainable development goals. Therefore, an artificial intelligence-based approach has been applied to solve this problem by focusing on the water management problem. The most general but also important aspect in the management of water resources is its correct consumption. In this context, the artificial intelligence-based system undertakes tasks such as water demand forecasting and distribution management, emergency and crisis management, water pollution detection and prevention, and maintenance and repair control and forecasting.

Keywords: water resource management, forced migration, multidisciplinary studies, artificial intelligence

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Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

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Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

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Authors: Guilherme Henrique Gabriel, Lucí Hidalgo Nunes

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An important feature of rainfall regimes is the variability, which is subject to the atmosphere’s general and regional dynamics, geographical position and relief. Despite being inherent to the climate system, it can harshly impact virtually all human activities. In turn, global climate change has the ability to significantly affect smaller-scale rainfall regimes by altering their current variability patterns. In this regard, it is useful to know if regional climates are changing over time and whether it is possible to link these variations to climate change trends observed globally. This study is part of an international project (Metropole-FAPESP, Proc. 2012/51876-0 and Proc. 2015/11035-5) and the objective was to identify and evaluate possible changes in rainfall behavior in the state of São Paulo, southeastern Brazil, using rainfall data from 79 rain gauges for the last forty years. Cluster analysis and gamma distribution parameters were used for evaluating spatial and temporal trends, and the outcomes are presented by means of geographic information systems tools. Results show remarkable changes in rainfall distribution patterns in São Paulo over the years: changes in shape and scale parameters of gamma distribution indicate both an increase in the irregularity of rainfall distribution and the probability of occurrence of extreme events. Additionally, the spatial outcome of cluster analysis along with the gamma distribution parameters suggest that changes occurred simultaneously over the whole area, indicating that they could be related to remote causes beyond the local and regional ones, especially in a current global climate change scenario.

Keywords: climate change, cluster analysis, gamma distribution, rainfall

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Authors: I. Filikhin, B. Vlahovic

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We focus on a novel type of detection based on electron tunneling properties of double nanoscale structures in semiconductor materials. Semiconductor heterostructures as quantum wells (QWs), quantum dots (QDs), and quantum rings (QRs) may have energy level structure of several hundred of electron confinement states. The single electron spectra of the double quantum objects (DQW, DQD, and DQR) were studied in our previous works with relation to the electron localization and tunneling between the objects. The wave function of electron may be localized in one of the QDs or be delocalized when it is spread over the whole system. The localizing-delocalizing tunneling occurs when an electron transition between both states is possible. The tunneling properties of spectra differ strongly for “regular” and “chaotic” systems. We have shown that a small violation of the geometry drastically affects localization of electron. In particular, such violations lead to the elimination of the delocalized states of the system. The same symmetry violation effect happens if electrical or magnetic fields are applied. These phenomena could be used to propose a new type of detection based on the high sensitivity of charge transport between double nanostructures and small violations of the shapes. It may have significant technological implications.

Keywords: double quantum dots, single electron levels, tunneling, electron localizations

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Authors: Umesh Kumar Soni, Suresh Kumar Soni, S. R. Awasthi

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Worldwide biggest difficulties are climate change, future availability of fossil fuels, and economical feasibility of renewable energy. They force us to use to a greater extent renewable energy and develop suitable hybrid renewable systems. Building heating/cooling consumes significant amount of energy. It can be conserved by use of proper heating/cooling techniques. This paper reviews and critically analyzes various active, passive and hybrid heating/cooling techniques used in green buildings.

Keywords: natural ventilation, energy conservation, hybrid ventilation techniques, climate change

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Authors: Gulden Aykinbaeva, Assem Umirzakova, Assel Makhadiyeva

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The problem of studying of young married couples in connection with a change of social institute of a family and marriage is represented very actual for family consultation, considering a family role in the development of modern society. Results of numerous researchs say that one of difficult in formation and stabilization of a matrimony is the period of a young family. This period is characterized by various processes of integration, adaptation and emotional compatibility of spouses. The young family in it the period endures the first standard crisis which postpones a print for the further development of the family scenario. Emergence new, earlier not existing, systems of values render a huge value on the process of formation of a young family and each of spouses separately. Possibly to solve the set family tasks at the development of the uniform system of the family relations in which socially mature persons capable to consider a family as the creativity of each other act as subjects. Due to the research objective in work the following techniques were used: a questionnaire of satisfaction with V. V. Stolin's marriage and A. N. Volkova's technique directed on detection of coherence of family values and role installations in a married couple, and also content – the analysis. Development of an internal basis of a family on mutual clearing of values is important during the work with married couples. 'The mature view' of the partner in the marriage union provides coherence between the expected and real behavior of the partner that is important for the realization of the purposes of adaptation in a family. For research of communication of the data obtained by means of A. N. Volkova's techniques, V. V. Stolina and content – the analysis, the correlation analysis, with the application of the criterion of Spirmen was used. The analysis of results of the conducted research allowed us to determine the number of consistent patterns: 1. Nature of change of satisfaction with marriage at spouses testifies that the matrimonial relations undergo high-quality changes at different stages of formation of a young family. 2. The matrimonial relations in the course of their development, formation and functioning in young marriage undergo considerable changes on psychological, social and psychological and insignificant — at the psychophysiological and sociocultural levels. The material received by us allows to plan ways of further detailed researches of the development of the matrimonial relations not only in the young marriage but also at further stages of development of a matrimony. We believe that the results received in this research can be almost applied at creation of algorithms of selection of marriage partners, at diagnostics of character and the maintenance of matrimonial disharmonies, at the forecast of stability of marriage and a family.

Keywords: married couples, formation of the young family, psychological consultation, matrimony

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Authors: Heming Yao

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Graphene-based percolative strain gauges could find applications in many places such as touch panels, artificial skins or human motion detection because of its advantages over conventional strain gauges such as flexibility and transparency. These strain gauges rely on a novel sensing mechanism that depends on strain-induced morphology changes. Once a compression or tension strain is applied to Graphene-based percolative strain gauges, the overlap area between neighboring flakes becomes smaller or larger, which is reflected by the considerable change of resistance. Tiny strain change on graphene-based percolative strain sensor can act as an important leverage to tremendously increase resistance of strain sensor, which equipped graphene-based percolative strain gauges with higher gauge factor. Despite ongoing research in the underlying sensing mechanism and the limits of sensitivity, neither suitable understanding has been obtained of what intrinsic factors play the key role in adjust gauge factor, nor explanation on how the strain gauge sensitivity can be enhanced, which is undoubtedly considerably meaningful and provides guideline to design novel and easy-produced strain sensor with high gauge factor. We here simulated the strain process by modeling graphene flakes and its percolative networks. We constructed the 3D resistance network by simulating overlapping process of graphene flakes and interconnecting tremendous number of resistance elements which were obtained by fractionizing each piece of graphene. With strain increasing, the overlapping graphenes was dislocated on new stretched simulation graphene flake simulation film and a new simulation resistance network was formed with smaller flake number density. By solving the resistance network, we can get the resistance of simulation film under different strain. Furthermore, by simulation on possible variable parameters, such as out-of-plane resistance, in-plane resistance, flake size, we obtained the changing tendency of gauge factor with all these variable parameters. Compared with the experimental data, we verified the feasibility of our model and analysis. The increase of out-of-plane resistance of graphene flake and the initial resistance of sensor, based on flake network, both improved gauge factor of sensor, while the smaller graphene flake size gave greater gauge factor. This work can not only serve as a guideline to improve the sensitivity and applicability of graphene-based strain sensors in the future, but also provides method to find the limitation of gauge factor for strain sensor based on graphene flake. Besides, our method can be easily transferred to predict gauge factor of strain sensor based on other nano-structured transparent optical conductors, such as nanowire and carbon nanotube, or of their hybrid with graphene flakes.

Keywords: graphene, gauge factor, percolative transport, strain sensor

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Authors: Akila Murali, Prithika Manivel

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Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

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Authors: Abrahão S. Fontes, Carlos A. V. Cardoso, Levi P. B. Oliveira

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The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this article, these techniques are applied and compared on a real motor, which has the fault of eccentricity in the air-gap. It was used as a theoretical model of an electric induction motor without fault in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the fault of eccentricity in the air gap, whose comparison between these showed the suitability of each one.

Keywords: eccentricity in the air-gap, fault diagnosis, induction motors, predictive maintenance

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Authors: Leydi Noemi Peraza Gómez, Jose Álvarez Nemegyei, Damaris Francis Estrella Castillo

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In order to have an epidemiological tool to detect biomechanical stress (ERGO-Mex), which impose physical labor or recreational activities, a questionnaire is constructed in Spanish, validated and culturally adapted to the Mayan indigenous population of Yucatan. Through the seven steps proposed by Guillemin and Beaton the procedure was: initial translation, synthesis of the translations, feed back of the translation. After that review by a committee of experts, pre-test of the preliminary version, and presentation of the results to the committee of experts and members of the community. Finally the evaluation of its internal validity (Cronbach's α coefficient) and external (intraclass correlation coefficient). The results for the validation in Spanish indicated that 45% of the participants have biomechanical stress. The ERGO-Mex correlation was 0.69 (p <0.0001). Subjects with high biomechanical stress had a higher score than subjects with low biomechanical stress (17.4 ± 8.9 vs.9.8 ± 2.8, p = 0.003). The Cronbach's α coefficient was 0.92; and for validation in Cronbach's α maya it was 0.82 and CCI = 0.70 (95% CI: 0.58-0.79; p˂0.0001); ERGO-Mex is suitable for performing early detection of musculoskeletal diseases and helping to prevent disability.

Keywords: biomechanical stress, disability, musculoskeletal disorders, prevention

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Authors: Mohammed Russedul Islam, Shah M. Muniruzzaman, M. Kamrul-Al-Masud, Syed Sadat Morshed

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Bangladesh is one of the most climate vulnerable countries in the world. According to scientists it is predicted that temperature will raise 1-3% and precipitation 20% by 2050 in Bangladesh. Increased temperature and precipitation will deteriorate pavement structure in an accelerated rate. The study has found that pavement life will reduce significantly due to rise in temperature and precipitation in in a coastal road in Bangladesh. It will cause to increase the maintenance cost of the road. The study has found that reduction in pavement life will be caused due the decrease in stiffness and strength parameters of the pavement material due to high temperature and precipitation. It has found that use of new material costlier than the existing one will be necessary to prevent the reduction of pavement life. Eventually it will increase the re-construction cost of the road. The study has used mechanistic-empirical analysis method with a software GAMES (General analysis on multi-layered elastic systems) to find out the effect of temperature and precipitation rise on the pavement life. The study will help to guide road engineers of Bangladesh to prepare in advance to fight with the climate change effect.

Keywords: climate change, maintenance cost, mechanistic-empirical method, pavement life

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

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The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Hongyu Chen, Li Jiang

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Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.

Keywords: GAN, discrete feature, Wasserstein distance, multiple intermediate layers

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: T. Hopkin, S. Lu, P. Rogers, M. Sexton

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Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

Keywords: defects, new homes, housing association, organizational learning

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Authors: Abdelghani Boudiaf

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The profusion of studies relating to the concept of organizational learning shows the importance that has been given to this concept in the management sciences. A few years ago, companies leaned towards ISO 9001 certification; this requires the implementation of the quality management system (QMS). In order for this objective to be achieved, companies must have a set of skills, which pushes them to develop learning through continuous training. The results of empirical research have shown that implementation of the QMS in the company promotes the development of learning. It should also be noted that several types of learning are developed in this sense. Given the nature of skills development is normative in the context of the quality demarche, companies are obliged to qualify and improve the skills of their human resources. Continuous training is the keystone to develop the necessary learning. To carry out continuous training, companies need to be able to identify their real needs by developing training plans based on well-defined engineering. The training process goes obviously through several stages. Initially, training has a general aspect, that is to say, it focuses on topics and actions of a general nature. Subsequently, this is done in a more targeted and more precise way to accompany the evolution of the QMS and also to make the changes decided each time (change of working method, change of practices, change of objectives, change of mentality, etc.). To answer our problematic we opted for the method of qualitative research. It should be noted that the case study method crosses several data collection techniques to explain and understand a phenomenon. Three cases of companies were studied as part of this research work using different data collection techniques related to this method.

Keywords: changing mentalities, continuing training, organizational learning, quality management system, skills development

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Authors: S. Y. Park, S. M. Lee, S. H. Lee, K. M. Lim

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In this research analyzed the effects that phase formation reaction change in the grey cast iron makes on characteristics of microstructures, liquidity, and mechanical properties through cooling curve when adding rare earth elements (R.E). This research was analyzed with comparison between the case of not adding the rare earth elements (R.E) into the grey cast iron with the standard composition (as 3.3%C-2.1%Si-0.7%Mn-0.1%S) and the case of adding 0.3% rare earth elements (R.E). The thermal analysis parameters have been drawn through eutectic temperature theoretically calculated, recalescence temperature, and undercooling temperature measured from start of eutectic reaction to end of solidification in the cooling curve obtained by thermal analysis to analyze formation behavior of graphite, and the effects by addition of rare earth elements on this have been reviewed. When adding rare earth elements (R.E), the cause of liquidity slowdown was analyzed trough the solidification starting temperature and change of solidification ending temperature. The strength and hardness have been measured to evaluate the mechanical properties, and the sound tensile strength has been evaluated through quality coefficient after measuring relative hardness and normality degree of tensile strength by calculating theoretical tensile strength and theoretical hardness. The change of Pearlite Inter-lamellar Spacing of matrix microstructure and eutectic cell count of macrostructure was measured to analyze the effects of the rare earth elements on the sound tensile strength. The change of eutectic cell count has been clarified through activation of the eutectic reaction, and the cause of pearlite inter-lamellar spacing clarified through eutectoid reaction temperature.

Keywords: cooling curve, element, grey cast iron, thermal analysis, rare earth element

Procedia PDF Downloads 343

Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Xiaodi Su, Xin Ting Zheng, Laura Sutarlie, Nur Asinah binte Mohamed Salleh, Yong Yu

Abstract:

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We have developed paper-like battery-free multiplexed sensors for holistic wound assessment via quantitative detection of multiple inflammation and infection markers. In one of the designs, the sensor patch consists of a wax-printed paper panel with five colorimetric sensor channels arranged in a pattern resembling a five-petaled flower (denoted as a ‘Petal’ sensor). The five sensors are for temperature, pH, trimethylamine, uric acid, and moisture. The sensor patch is sandwiched between a top transparent silicone layer and a bottom adhesive wound contact layer. In the second design, a palm-like-shaped paper strip is fabricated by a paper-cutter printer (denoted as ‘Palm’ sensor). This sensor strip carries five sensor regions connected by a stem sampling entrance that enables rapid colorimetric detection of multiple bacteria metabolites (aldehyde, lactate, moisture, trimethylamine, tryptophan) from wound exudate. For both the “\’ Petal’ and ‘Palm’ sensors, color images can be captured by a mobile phone. According to the color changes, one can quantify the concentration of the biomarkers and then determine wound healing status and identify/quantify bacterial species in infected wounds. The ‘Petal’ and ‘Palm’ sensors are validated with in-situ animal and ex-situ skin wound models, respectively. These sensors have the potential for integration with wound dressing to allow early warning of adverse events without frequent removal of the plasters. Such in-situ and early detection of non-healing condition can trigger immediate clinical intervention to facilitate wound care management.

Keywords: wound infection, colorimetric sensor, paper fluidic sensor, wound care

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Authors: Bhogendra Mishra, Andy Nelson, Mirco Boschetti, Lorenzo Busetto, Alice Laborte

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Rice is grown on over 100 million hectares in almost every country of Asia. It is the most important staple crop for food security and has high economic and cultural importance in Asian societies. The combination of genetic diversity and management options, coupled with the large geographic extent means that there is a large variation in seasonality (when it is grown) and cropping intensity (how often it is grown per year on the same plot of land), even over relatively small distances. Seasonality and intensity can and do change over time depending on climatic, environmental and economic factors. Detecting where and when these changes happen can provide information to better understand trends in regional and even global rice production. Remote sensing offers a unique opportunity to estimate these trends. We apply the recently published PhenoRice algorithm to 14 years of moderate resolution remote sensing (MODIS) data (utilizing 250m resolution 16 day composites from Terra and Aqua) to estimate seasonality and cropping intensity per year and changes over time. We compare the results to the surveyed data collected by International Rice Research Institute (IRRI). The study results in a unique and validated dataset on the extent and change of extent, the seasonality and change in seasonality and the cropping intensity and change in cropping intensity between 2003 and 2016 for the Philippines. Observed trends and their implications for food security and trade policies are also discussed.

Keywords: rice, cropping intensity, moderate resolution remote sensing (MODIS), phenology, seasonality

Procedia PDF Downloads 285

Authors: Youn Young Shim, Ji Hye Kim, Jae Youl Cho, Martin JT Reaney

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Flaxseed (Linumusitatissimum L.) is gaining popularity in the food industry as a superfood due to its health-promoting properties. Linusorbs (LOs, a.k.a. Cyclolinopeptide) are bioactive compounds present in flaxseed exhibiting potential health effects. The study focused on the effects of processing and storage on the stability of flaxseed-derived LOs added to various bakery products. The flaxseed meal fortified gluten-free (GF) bakery bread was prepared, and the changes of LOs during the bread-making process (meal, fortified flour, dough, and bread) and storage (0, 1, 2, and 4 weeks) at different temperatures (−18 °C, 4 °C, and 22−23 °C) were analyzed by high-performance liquid chromatography-diode array detection. The total oxidative LOs and LO1OB2 were almost kept stable in flaxseed meals at storage temperatures of 22−23 °C, −18 °C, and 4 °C for up to four weeks. Processing steps during GF-bread production resulted in the oxidation of LOs. Interestingly, no LOs were detected in the dough sample; however, LOs appeared when the dough was stored at −18 °C for one week, suggesting that freezing destroyed the sticky structure of the dough and resulted in the release of LOs. The final product, flaxseed meal fortified bread, could be stored for up to four weeks at −18 °C and 4 °C, and for one week at 22−23 °C. All these results suggested that LOs may change during processing and storage and that flaxseed flour-fortified bread should be stored at low temperatures to preserve effective LOs components.

Keywords: linum usitatissimum L., flaxseed, linusorb, stability, gluten-free, peptides, cyclolinopeptide

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Authors: Faith Eweluegim Enahoro-Ofagbe

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Worldwide, every nation is experiencing the effects of global warming. In developing countries, due to the heavy reliance on agriculture for socioeconomic growth and security, among other things, these countries are more affected by climate change, particularly with the availability of water. Floods, droughts, rising temperatures, saltwater intrusion, groundwater depletion, and other severe environmental alterations are all brought on by climatic change. Life depends on water, a vital resource; these ecological changes affect all water use, including agriculture and household water use. Therefore adequate and adaptive water usage strategies for sustainability are essential in developing countries. Therefore, this paper investigates Nigeria's challenges due to climate change and adaptive techniques that have evolved in response to such issues to ensure water management and sustainability for irrigation and provide quality water to residents. Questionnaires were distributed to respondents in the study area, central Nigeria, for quantitative evaluation of sustainable water resource management techniques. Physicochemical analysis was done, collecting soil and water samples from several locations under investigation. Findings show that farmers use different methods, ranging from intelligent technologies to traditional strategies for water resource management. Also, farmers need to learn better water resource management techniques for sustainability. Since more residents obtain their water from privately held sources, the government should enforce legislation to ensure that private borehole construction businesses treat water sources of poor quality before the general public uses them.

Keywords: developing countries, irrigation, strategies, sustainability, water resource management, water usage

Procedia PDF Downloads 100

Authors: Mohammad Arif, Soumen Chatterjee, Krishnendu Gupta

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The peri-urban interface is an area of transition where the urban and rural areas meet and interact. So the peri-urban areas, which is characterized by strong urban influence, easy access to markets, services and other inputs, are ready supplies of labour but distant from the land paucity and pollution related to urban growth. Hence, the present study is primarily aimed at quantifying the spatio-temporal pattern of land use/land cover change during the last three decades (i.e., 1987 to 2016) in the peri-urban area of Burdwan city. In the recent past, the morphology of the study region has rapid change due to high growth of population and establishment of industries. The change has predominantly taken place along the State and National Highway 2 (NH-2) and around the Burdwan Municipality for meeting both residential and commercial purposes. To ascertain the degree of change in land use and land cover, over the specified time, satellite imageries and topographical sheets are employed. The data is processed through appropriate software packages to arrive at a deduction that most of the land use changes have occurred by obliterating agricultural land & water bodies and substituting them by built area and industrial spaces. Geospatial analysis of study area showed that this area has experienced a steep increase (30%) of built-up areas and excessive decrease (15%) in croplands between 1987 and 2016. Increase in built-up areas is attributed to the increase of out-migration during this period from the core city. This study also examined social, economic and institutional factors that lead to this rapid land use change in peri-urban areas of the Burdwan city by carrying out a field survey of 250 households in peri-urban areas. The research concludes with an urgency for regulating land subdivisions in peri-urban areas to prevent haphazard land use development. It is expected that the findings of the study would go a long way in facilitating better policy making.

Keywords: growth, land use land cover, morphology, peri-urban, policy making

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Authors: S. Delgado, C. Cerrada, R. S. Gómez

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This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

Procedia PDF Downloads 52

Authors: S. A. M. Sabrina, Gouget Lammel, Anne Chantal, Chazalviel, Jean Noël, Ozanam François, Etcheberry Arnaud, Tighlit Fatma Zohra, B. Samia, Gabouze Noureddine

Abstract:

The elaboration and characterization of hybrid nano materials give rise to considerable interest due to the new properties that arising. They are considered as an important category of new materials having innovative characteristics by combining the specific intrinsic properties of inorganic compounds (semiconductors) with the grafted organic species. This open the way to improved properties and spectacular applications in various and important fields, especially in the environment. In this work, nano materials based-semiconductors were elaborated by chemical route. The obtained surfaces were grafted with organic functional groups. The functionalization process was optimized in order to confer to the hybrid nano material a good stability as well as the right properties required for the subsequent applications. Different characterization techniques were used to investigate the resulting nano structures, such as SEM, UV-Visible, FTIR, Contact angle and electro chemical measurements. Finally, applications were envisaged in environmental area. The elaborated nano structures were tested for the detection and the elimination of pollutants.

Keywords: hybrid materials, porous silicon, peptide, metal detection

Procedia PDF Downloads 481