Search results for: climate network

Authors: Abdelali Laamari, Morad Faiz, Ali Amamou And Mohamed Elkoudrim

Abstract:

This research seeks to develop multi-disciplinary, multi-criteria, and multi-institutional approaches that consider the three main pillars of sustainability (environmental, economic, and social aspects) at the level of decision making regarding the adoption of improved technologies in the targeted case study region in Morocco. The study is aimed at combining sound R&I with extensive skills in applied research and policy evaluation. The intention is to provide new simple, and transferable tools and agricultural practices that will enable the uptake of sustainability and the resiliency of agro-ecosystems. The study will understand the state-of-the-art of the impact of climate change and identify the core bottlenecks and climate change’s impact on crop and livestock productivity of the targeted value chains in Morocco. Studies conducted during 2021-2022 showed that most of the farmers are using since 2010 the direct seeding and the system can be improved by adopting new fertilizer and varieties of wheat. The alley-cropping technology is based on Atriplex plant or olive trees. The introduction of new varieties of oat and quinoa has improved biomass and grain production in a dry season. The research is targeting other issues, such as social enterprises, to diversify women’s income resources and create new job opportunities through diversification of end uses of durum wheat and barley grains. Women’s local knowledge is rich on the different end uses of durum and barley grains that can improve their added value if they are transformed as couscous, pasta, or any other products.

Keywords: agriculture, climate, production system, integration

Procedia PDF Downloads 76

Authors: Mirza Waseem Abbas, Abdul Qayyum, Muhammad Islam

Abstract:

Climate change has emerged as a significant threat to global food security, affecting crop production systems worldwide. This research paper aims to examine the specific impacts of climate change on wheat production in Pakistan, Punjab in particular, a country highly dependent on wheat as a staple food crop. Through a comprehensive review of scientific literature, field observations, and data analysis, this study assesses the key climatic factors influencing wheat cultivation and the subsequent implications for food security in the region. A comparison of two subsequent Wheat seasons in Punjab was examined through climatic conditions, area, yield, and production data. From the analysis, it is observed that despite a decrease in the area under cultivation in the Punjab during the Wheat 2023 season, the production and average yield increased due to favorable weather conditions. These uncertain climatic conditions have a direct impact on crop yields. Last year due to heat waves, Wheat crop in Punjab suffered a significant loss. Through crop insurance, Wheat growers were provided with yield loss protection keeping in view the devastating heat wave and floods last year. Under crop insurance by the Government of the Punjab, 534,587 Wheat growers were insured with a $1.6 million premium subsidy. However, due to better climatic conditions, no loss in the yield was recorded in the insured areas. Crop Insurance is one of the suitable options for policymakers to protect farmers against climatic losses in the future as well.

Keywords: climate change, crop insurance, heatwave, wheat yield punjab

Procedia PDF Downloads 82

Authors: Mrinal Saurabh Bhaskar, Rahul E Ravindranathan, Priyangana Borah

Abstract:

Energy plays a key role and as a driving force for economic and social growth for any country. To manage the energy sources and its efficient utilization in different economic sectors, energy policy of a country is critical. The energy performance of a country is measured in Energy Intensity and India’s Energy Intensity due to several policies interventions has reduced from 0.53 toe/1000USD (2010) in the year 2000 to 0.38 toe/1000USD (2010) in the year 2014, which is about 28 per cent reduction. The Government of India has taken several initiates to manage their increasing energy demand and meet the climate change goals defined by them. The major policy milestones in India related to energy are (i) Enactment of Energy Conservation (EC) Act 2001 (ii) Establishment of Bureau of Energy Efficiency 2001 (iii) National Action Plan on Climate Change (iv) Launch of Demand Side Management schemes (v) Amendment of EC Act 2010 (vi) Launch of Perform Achieve and Trade scheme 2012. Through a critical review, this paper highlights the key energy policy interventions by India, its benefits and impact, challenges faced and efforts of the Government to overcome such challenges. Such take away would be helpful for other countries who are proposing to prepare or amend their energy policy for their different economic sectors.

Keywords: energy, efficiency, climate, policy

Procedia PDF Downloads 341

Authors: Yassine Messaoud, Gordon Nigh, Faouzi Messaoud, Han Chen

Abstract:

The effect of climate change on tree growth in boreal and temperate forests has received increased interest in the context of global warming. However, most studies were conducted in small areas and with a limited number of tree species. Here, we examined the height growth responses of seventeen tree species to climate change in Western North America. 37009 stands from forest inventory databases in Canada and USA with varying establishment date were selected. Dominant and co-dominant trees from each stand were sampled to determine top tree height at 50 years breast height age. Height was related to historical mean annual and summer temperatures, annual and summer Palmer Drought Severity Index, tree establishment date, slope, aspect, soil fertility as determined by the rate of carbon organic matter decomposition (carbon/nitrogen), geographic locations (latitude, longitude, and elevation), species range (coastal, interior, and both ranges), shade tolerance and leaf form (needle leaves, deciduous needle leaves, and broadleaves). Climate change had mostly a positive effect on tree height growth. The results explained 62.4% of the height growth variance. Since 1880, height growth increase was greater for coastal, high shade tolerant, and broadleaf species. Height growth increased more on steep slopes and high soil fertility soils. Greater height growth was mostly observed at the leading range and upward. Conversely, some species showed the opposite pattern probably due to the increase of drought (coastal Mediterranean area), precipitation and cloudiness (Alaska and British Columbia) and peculiarity (higher latitudes-lower elevations and vice versa) of western North America topography. This study highlights the role of the species ecological amplitude and traits, and geographic locations as the main factors determining the growth response and its magnitude to the recent global climate change.

Keywords: Height growth, global climate change, species range, species characteristics, species ecological amplitude, geographic locations, western North America

Procedia PDF Downloads 185

Authors: Cabangile Ncengeni Ngwane, Gerald Mills

Abstract:

Anthropogenic climate change is exacerbating natural hazards such as droughts, heat waves and sea-level rise. The associated risks are the greatest in places where socio-ecological systems are exposed to these changes and the populations and infrastructure are vulnerable. Identifying the communities at risk and enhancing local resilience are key issues in responding to the current and project climate changes. This paper explores the types of risks associated with multiple overlapping hazards in Durban, South Africa where the social, cultural and economic dimensions that contribute to exposure and vulnerability are compounded by its history of apartheid. As a result, climate change risks are highly concentrated in marginalized communities that have the least adaptive capacity. In this research, a Geographic Information System is to explore the spatial correspondence among geographic layers representing hazards, exposure and vulnerability across Durban. This quantitative analysis will allow authors to identify communities at high risk and focus our study on the nature of the current human-environment relationships that result in risk inequalities. This work will employ qualitative methods to critically examine policies (including educational practices and financial support systems) and on-the-ground actions that are designed to improve the adaptive capacity of these communities and meet UN Sustainable Development Goals. This work will contribute to a growing body of literature on disaster risk management, especially as it relates to developing economies where socio-economic inequalities are correlated with ethnicity and race.

Keywords: adaptive capacity, disaster risk reduction, exposure, resilience, South Africa

Procedia PDF Downloads 150

Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

Abstract:

Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

Procedia PDF Downloads 172

Authors: Murat Zencirkıran, Elvan Ender

Abstract:

Climate change, which has become effective on a global scale, is accompanied by an increase in negative conditions for human, plant and animal life. Especially these negative conditions (drought, warming, glowing, etc.) are felt more rapidly in urban life and affect the sustainability of green areas which are of great importance in terms of life comfort. In this context, the choice of woody taxa used in the design and design of green spaces in the city increase one more time. Within the scope of this study, two of four urban parks located in the city center of Bursa province were selected and evaluated for woody taxa. Urban parks have been identified as the oldest and newest urban park in Bursa, and it has been tried to emphasize the differences that may exist over time. It was determined that 54 woody taxa took place in Resat Oyal Kulturpark and 76 woody taxa in Hudavendigar Urban Park. These taxa have been evaluated in terms of water consumption and ecological tolerances by taking into account climate change, and suggestions have been developed against possible problems.

Keywords: ecological hardiness, urban park, water consumption, woody plants

Procedia PDF Downloads 297

Authors: Germán Díaz Flórez, Carlos Alberto Olvera Olvera, Domingo José Gómez Meléndez, Francisco Eneldo López Monteagudo

Abstract:

In recent decades, population growth has exerted great pressure on natural resources. Two of the most scarce and difficult to obtain resources, arable land, and water, are closely interrelated, to the satisfaction of the demand for food production. In Mexico, the agricultural sector uses more than 70% of water consumption. Therefore, maximize the efficiency of current production systems is inescapable. It is essential to utilize techniques and tools that will enable us to the significant savings of water, labor and fertilizer. In this study, we present a production module of hydroponic green forage (HGF), which is a viable alternative in the production of livestock feed in the semi-arid and arid zones. The equipment in addition to having a forage production module, has a climate and irrigation control system that operated with photovoltaics. The climate control, irrigation and power management is based on fuzzy control techniques. The fuzzy control provides an accurate method in the design of controllers for nonlinear dynamic physical phenomena such as temperature and humidity, besides other as lighting level, aeration and irrigation control using heuristic information. In this working, firstly refers to the production of the hydroponic green forage, suitable weather conditions and fertigation subsequently presents the design of the production module and the design of the controller. A simulation of the behavior of the production module and the end results of actual operation of the equipment are presented, demonstrating its easy design, flexibility, robustness and low cost that represents this equipment in the primary sector.

Keywords: fuzzy, climate control system, hydroponic green forage, forage production module

Procedia PDF Downloads 397

Authors: Amjad Khan

Abstract:

The security of Vehicular ad hoc networking is of great importance as it involves serious life threats. Thus to provide secure communication amongst Vehicles on road, the conventional security system is not enough. It is necessary to prevent the network resources from wastage and give them protection against malicious nodes so that to ensure the data bandwidth availability to the legitimate nodes of the network. This work is related to provide a non conventional security system by introducing some constraints to minimize the DoS (Denial of services) especially data and bandwidth. The data packets received by a node in the network will pass through a number of tests and if any of the test fails, the node will drop those data packets and will not forward it anymore. Also if a node claims to be the nearest node for forwarding emergency messages then the sender can effectively identify the true or false status of the claim by using these constraints. Consequently the DoS(Denial of Services) attack is minimized by the instant availability of data without wasting the network resources.

Keywords: black hole attack, grey hole attack, intransient traffic tempering, networking

Procedia PDF Downloads 284

Authors: Christina Kalogirou

Abstract:

Investigating the integration of bioclimatic design into vernacular architecture could lead to interesting results regarding the preservation of cultural heritage while enhancing the energy efficiency of historic buildings. Furthermore, these recognized principles and systems of bioclimatic design in vernacular settlements could be applied to modern architecture and thus to new buildings in such areas. This study introduces an approach to categorizing distinct technologies and design principles of bioclimatic design based on a thoughtful approach to various climatic zones and environment in Greece (mountainous areas, islands and lowlands). For this purpose, various types of dwellings are evaluated for their response to climate, regarding the layout of the buildings (orientation, floor plans’ shape, semi-open spaces), the site planning, the openings (size, position, protection), the building envelope (walls: construction materials-thickness, roof construction detailing) and the migratory living pattern according to seasonal needs. As a result, various passive design principles (that could be adapted to current architectural practice in such areas, in order to optimize the relationship between site, building, climate and energy efficiency) are proposed.

Keywords: bioclimatic design, buildings physics, climatic zones, energy efficiency, vernacular architecture

Procedia PDF Downloads 387

Authors: Yashasvi Thakur, Vikas K. Sharma

Abstract:

The fragile Himalayan ecosystems are sensitive to environmental stresses, including direct and indirect impacts of climate stresses. A total of 297 naturalized alien plant species belonging to 65 families in the IHR have already been reported. Of the total 297 naturalized alien plant species in IHR, the maximum species occur in Himachal Pradesh (232; 78.1%), followed by Jammu & Kashmir (192; 64.6%) and Uttarakhand (181; 60.90%). The present study reports the spread of some invasive and existing weed species like Ageratum conyzoides, Bidens pilosa, Chromolaena odorata, Lantana camara, Brossnetia papyrifera, Oxalis corniculata, Galinsoga parviflora, Panicum maximum at an extent that they are not only invading the agricultural fields but are also replacing the native plant species and degrading the existing grassland quality. Moreover, the degradation of grassland has led to the dry fodder shortage for livestock in the lower Shivalik ranges of the state of Himachal Pradesh and has also encouraged the use of herbicides at an extensive scale. This article provides a mapping of the current spread of some of these species at the block level to allow the development of appropriate management strategies and policy planning for addressing issues pertaining to plant invasion, agricultural fields, and grasslands across the IHR states.

Keywords: climate change, invasive alien species, agriculture, grassland, IHR

Procedia PDF Downloads 74

Authors: Shotiros Protong

Abstract:

The incidents of sudden landslides in Thailand during the past decade have occurred frequently and more severely. It is necessary to focus on the principal parameters used for analysis such as land cover land use, rainfall values, characteristic of soil and digital elevation model (DEM). The combination of intense rainfall and severe monsoons is increasing due to global climate change. Landslide occurrences rapidly increase during intense rainfall especially in the rainy season in Thailand which usually starts around mid-May and ends in the middle of October. The rain-triggered landslide hazard analysis is the focus of this research. The combination of geotechnical and hydrological data are used to determine permeability, conductivity, bedding orientation, overburden and presence of loose blocks. The regional landslide hazard mapping is developed using the Slope Stability Index SINMAP model supported on Arc GIS software version 10.1. Geological and land use data are used to define the probability of landslide occurrences in terms of geotechnical data. The geological data can indicate the shear strength and the angle of friction values for soils above given rock types, which leads to the general applicability of the approach for landslide hazard analysis. To address the research objectives, the methods are described in this study: setup and calibration of the SINMAP model, sensitivity of the SINMAP model, geotechnical laboratory, landslide assessment at present calibration and landslide assessment under future climate simulation scenario A2 and B2. In terms of hydrological data, the millimetres/twenty-four hours of average rainfall data are used to assess the rain triggered landslide hazard analysis in slope stability mapping. During 1954-2012 period, is used for the baseline of rainfall data at the present calibration. The climate change in Thailand, the future of climate scenarios are simulated by spatial and temporal scales. The precipitation impact is need to predict for the climate future, Statistical Downscaling Model (SDSM) version 4.2, is used to assess the simulation scenario of future change between latitude 16o 26’ and 18o 37’ north and between longitude 98o 52’ and 103o 05’ east by SDSM software. The research allows the mapping of risk parameters for landslide dynamics, and indicates the spatial and time trends of landslide occurrences. Thus, regional landslide hazard mapping under present-day climatic conditions from 1954 to 2012 and simulations of climate change based on GCM scenarios A2 and B2 from 2013 to 2099 related to the threshold rainfall values for the selected the study area in Uttaradit province in the northern part of Thailand. Finally, the landslide hazard mapping will be compared and shown by areas (km2 ) in both the present and the future under climate simulation scenarios A2 and B2 in Uttaradit province.

Keywords: landslide hazard, GIS, slope stability index (SINMAP), landslides, Thailand

Procedia PDF Downloads 564

Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

Abstract:

In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

Procedia PDF Downloads 364

Authors: Huiqiang Yang, Domingo Santana

Abstract:

This paper proposes a novel configuration which introduces a natural draft dry cooling tower system in a molten salt power tower. A three-dimensional numerical modeling was developed based on the novel configuration. A plan of building 20 new concentrating solar power plants has been announced by Chinese government in September 2016, and among these 20 new plants, most of them are located in regions with long winter and harsh winter climate. The innovative configuration proposed includes an external receiver concrete tower at the center, a natural draft dry cooling tower which is surrounding the external receiver concrete tower and whose shell is fixed on the external receiver concrete tower, and a power block (including a steam generation system, a steam turbine system and hot/cold molten salt tanks, and water treatment systems) is covered by the roof of the natural draft dry cooling tower. Heat exchanger bundles are vertically installed at the furthest edge of the power block. In such a way, all power block equipment operates under suitable environmental conditions through whole year operation. The monthly performance of the novel configuration is simulated as compared to a standard one. The results show that the novel configuration is much more efficient in each separate month in a typical meteorological year. Moreover, all systems inside the power block have less thermal losses at low ambient temperatures, especially in harsh winter climate. It is also worthwhile mentioning that a photovoltaic power plant can be installed on the roof of the cooling tower to reduce the parasites of the molten salt power tower.

Keywords: molten salt power tower, natural draft dry cooling, commercial scale, power block, harsh winter climate

Procedia PDF Downloads 341

Authors: U. V. Okpala

Abstract:

Flood is an overflow of water that submerges or ‘drowns’ land. In developing countries it occurs as a result of blocking of natural and man-made drainages and poor maintenance of water dams/reservoirs which seldom give way after persistent heavy down pours. In coastal lowlands and swamp lands, flooding is aided mainly by blocked channels and indiscriminate sand fling of coastal swamp areas and natural drainage channel for urban development/constructions. In this paper, the causes of flood and possible scientific, technological, political, economic and social impacts of flood disaster on the environment a case study of Anambra State have been studied. Often times flooding is caused by climate change, especially in the developed economy where scientific mitigating options are highly employed. Researchers have identified Green Houses Gases (GHG) as the cause of global climate change. The recent flood disaster in Anambra State which caused physical damage to structures, social dislocation, contamination of clean drinking water, spread of water-borne diseases, shortage of crops and food supplies, death of non-tolerant tree species, disruption in transportation system, serious economic loss and psychological trauma is a function of climate change. There is need to encourage generation of renewable energy sources, use of less carbon intensive fuels and other energy efficient sources. Carbon capture/sequestration, proper management of our drainage systems and good maintenance of our dams are good option towards saving the environment.

Keywords: flooding, climate change, carbon capture, energy systems

Procedia PDF Downloads 375

Authors: Mina Ganji Morad, Maziar Azadisoleimanieh, Sina Ganji Morad

Abstract:

A green roof is formed by green plants alive and has many positive impacts in the regional climatic, as well as indoor. Green roof system to prevent solar radiation plays a role in the cooling space. The cooling is done by reducing thermal fluctuations on the exterior of the roof and by increasing the roof heat capacity which cause to keep the space under the roof cool in the summer and heating rate increases during the winter. A roof garden is one of the recommended ways to reduce energy consumption in large cities. Despite the scale of the city green roofs have effective functions, such as beautiful view of city and decontaminating the urban landscape and reduce mental stress, and in an exchange of energy and heat from outside to inside spaces. This article is based on a review of 20 articles and 10 books and valid survey results on the positive effects of green roofs to prevent energy waste in the building. According to these publications, three of the conventional roof, green roof typical and green roof with certain administrative details (layers of glass) and the use of resistant plants and shrubs have been analyzed and compared their heat transfer. The results of these studies showed that one of the best green roof systems for mountainous climate is tree and shrub system that in addition to being resistant to climate change in mountainous regions, will benefit from the other advantages of green roof. Due to the severity of climate change in mountainous areas it is essential to prevent the waste of buildings heating and cooling energy. Proper climate design can greatly help to reduce energy.

Keywords: green roof, heat transfer, reducing energy consumption, mountainous areas, sustainable architecture

Procedia PDF Downloads 397

Authors: Meiyan Gao, Zongmin Wang, Haibo Yang, Qiuhua Liang

Abstract:

Under the background of global climate change and rapid development of modern urbanization, the frequency of climate disasters such as extreme precipitation in cities around the world is gradually increasing. In this paper, Hi-PIMS model is used to simulate the "720" flood in Zhengzhou, and the continuous stages of flood resilience are determined with the urban flood stages are divided. The flood resilience curve under the influence of multiple factors were determined and the urban flood toughness was evaluated by combining the results of resilience curves. The flood resilience of urban unit grid was evaluated based on economy, population, road network, hospital distribution and land use type. Firstly, the rainfall data of meteorological stations near Zhengzhou and the remote sensing rainfall data from July 17 to 22, 2021 were collected. The Kriging interpolation method was used to expand the rainfall data of Zhengzhou. According to the rainfall data, the flood process generated by four rainfall events in Zhengzhou was reproduced. Based on the results of the inundation range and inundation depth in different areas, the flood process was divided into four stages: absorption, resistance, overload and recovery based on the once in 50 years rainfall standard. At the same time, based on the levels of slope, GDP, population, hospital affected area, land use type, road network density and other aspects, the resilience curve was applied to evaluate the urban flood resilience of different regional units, and the difference of flood process of different precipitation in "720" rainstorm in Zhengzhou was analyzed. Faced with more than 1,000 years of rainstorm, most areas are quickly entering the stage of overload. The influence levels of factors in different areas are different, some areas with ramps or higher terrain have better resilience, and restore normal social order faster, that is, the recovery stage needs shorter time. Some low-lying areas or special terrain, such as tunnels, will enter the overload stage faster in the case of heavy rainfall. As a result, high levels of flood protection, water level warning systems and faster emergency response are needed in areas with low resilience and high risk. The building density of built-up area, population of densely populated area and road network density all have a certain negative impact on urban flood resistance, and the positive impact of slope on flood resilience is also very obvious. While hospitals can have positive effects on medical treatment, they also have negative effects such as population density and asset density when they encounter floods. The result of a separate comparison of the unit grid of hospitals shows that the resilience of hospitals in the distribution range is low when they encounter floods. Therefore, in addition to improving the flood resistance capacity of cities, through reasonable planning can also increase the flood response capacity of cities. Changes in these influencing factors can further improve urban flood resilience, such as raise design standards and the temporary water storage area when floods occur, train the response speed of emergency personnel and adjust emergency support equipment.

Keywords: urban flood resilience, resilience assessment, hydrodynamic model, resilience curve

Procedia PDF Downloads 40

Authors: Volker Wannack

Abstract:

Regional, national, and international strategies focusing on hydrogen (H₂) and blockchain are driving significant advancements in hydrogen and blockchain technology worldwide. These strategies lay the foundation for the groundbreaking "Blockchain Based Hydrogen Market (BBH₂)" project. The primary goal of this project is to develop a functional Blockchain Minimum Viable Product (B-MVP) for the hydrogen market. The B-MVP will leverage blockchain as an enabling technology with a common database and platform, facilitating secure and automated transactions through smart contracts. This innovation will revolutionize logistics, trading, and transactions within the hydrogen market. The B-MVP has transformative potential across various sectors. It benefits renewable energy producers, surplus energy-based hydrogen producers, hydrogen transport and distribution grid operators, and hydrogen consumers. By implementing standardized, automated, and tamper-proof processes, the B-MVP enhances cost efficiency and enables transparent and traceable transactions. Its key objective is to establish the verifiable integrity of climate-friendly "green" hydrogen by tracing its supply chain from renewable energy producers to end users. This emphasis on transparency and accountability promotes economic, ecological, and social sustainability while fostering a secure and transparent market environment. A notable feature of the B-MVP is its cross-border operability, eliminating the need for country-specific data storage and expanding its global applicability. This flexibility not only broadens its reach but also creates opportunities for long-term job creation through the establishment of a dedicated blockchain operating company. By attracting skilled workers and supporting their training, the B-MVP strengthens the workforce in the growing hydrogen sector. Moreover, it drives the emergence of innovative business models that attract additional company establishments and startups and contributes to long-term job creation. For instance, data evaluation can be utilized to develop customized tariffs and provide demand-oriented network capacities to producers and network operators, benefitting redistributors and end customers with tamper-proof pricing options. The B-MVP not only brings technological and economic advancements but also enhances the visibility of national and international standard-setting efforts. Regions implementing the B-MVP become pioneers in climate-friendly, sustainable, and forward-thinking practices, generating interest beyond their geographic boundaries. Additionally, the B-MVP serves as a catalyst for research and development, facilitating knowledge transfer between universities and companies. This collaborative environment fosters scientific progress, aligns with strategic innovation management, and cultivates an innovation culture within the hydrogen market. Through the integration of blockchain and hydrogen technologies, the B-MVP promotes holistic innovation and contributes to a sustainable future in the hydrogen industry. The implementation process involves evaluating and mapping suitable blockchain technology and architecture, developing and implementing the blockchain, smart contracts, and depositing certificates of origin. It also includes creating interfaces to existing systems such as nomination, portfolio management, trading, and billing systems, testing the scalability of the B-MVP to other markets and user groups, developing data formats for process-relevant data exchange, and conducting field studies to validate the B-MVP. BBH₂ is part of the "Technology Offensive Hydrogen" funding call within the research funding of the Federal Ministry of Economics and Climate Protection in the 7th Energy Research Programme of the Federal Government.

Keywords: hydrogen, blockchain, sustainability, innovation, structural change

Procedia PDF Downloads 168

Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

Abstract:

Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

Procedia PDF Downloads 227

Authors: A. A. Sadia, A. Emdad, E. Hossain

Abstract:

The increasing importance of mushrooms as a source of nutrition, health benefits, and even potential cancer treatment has raised awareness of the impact of climate-sensitive variables on their cultivation. Factors like temperature, relative humidity, air quality, and substrate composition play pivotal roles in shaping mushroom growth, especially in Bangladesh. Oyster mushrooms, a commonly cultivated variety in this region, are particularly vulnerable to climate fluctuations. This research explores the climatic dynamics affecting oyster mushroom cultivation and, presents an approach to address these challenges and provides tangible solutions to fortify the agro-economy, ensure food security, and promote the sustainability of this crucial food source. Using climate and production data, this study evaluates the performance of three clustering algorithms -KMeans, OPTICS, and BIRCH- based on various quality metrics. While each algorithm demonstrates specific strengths, the findings provide insights into their effectiveness for this specific dataset. The results yield essential information, pinpointing the optimal temperature range of 13°C-22°C, the unfavorable temperature threshold of 28°C and above, and the ideal relative humidity range of 75-85% with the suitable production regions in three different seasons: Kharif-1, 2, and Robi. Additionally, a user-friendly web application is developed to support mushroom farmers in making well-informed decisions about their cultivation practices. This platform offers valuable insights into the most advantageous periods for oyster mushroom farming, with the overarching goal of enhancing the efficiency and profitability of mushroom farming.

Keywords: climate variability, mushroom cultivation, clustering techniques, food security, sustainability, web-application

Procedia PDF Downloads 68

Authors: Oluwaponmile D. Alao

Abstract:

In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

Procedia PDF Downloads 347

Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

Abstract:

This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

Procedia PDF Downloads 426

Authors: Ashkan Zakaryazad, Ekrem Duman

Abstract:

A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

Procedia PDF Downloads 475

Authors: Shuo Liu

Abstract:

In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

Procedia PDF Downloads 100

Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

Abstract:

Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

Procedia PDF Downloads 326

Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee

Abstract:

Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.

Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform

Procedia PDF Downloads 512

Authors: Beatriz Martinez-Pastor, Maria Nogal, Alan O'Connor

Abstract:

During the last years, the number of extreme weather events has increased. A variety of extreme weather events, including river floods, rain-induced landslides, droughts, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions worldwide. These events have a devastating impact on critical infrastructure systems resulting in high social, economical and environmental costs. These events have a huge impact in transport systems. Since, transport networks are completely exposed to every kind of climatological perturbations, and its performance is closely related with these events. When a traffic network is affected by a climatological hazard, the quality of its service is threatened, and the level of the traffic conditions usually decreases. With the aim of understanding this process, the concept of resilience has become most popular in the area of transport. Transport resilience analyses the behavior of a traffic network when a perturbation takes place. This holistic concept studies the complete process, from the beginning of the perturbation until the total recovery of the system, when the perturbation has finished. Many concepts are included in the definition of resilience, such as vulnerability, redundancy, adaptability, and safety. Once the resilience of a transport network can be evaluated, in this case, the methodology used is a dynamic equilibrium-restricted assignment model that allows the quantification of the concept, the next step is its improvement. Through the improvement of this concept, it will be possible to create transport networks that are able to withstand and have a better performance under the presence of climatological hazards. Analyzing the impact of a perturbation in a traffic network, it is observed that the response of the different links, which are part of the network, can be completely different from one to another. Consequently and due to this effect, many questions arise, as what makes a link more critical before an extreme weather event? or how is it possible to identify these critical links? With this aim, and knowing that most of the times the owners or managers of the transport systems have limited resources, the identification of the critical links of a transport network before extreme weather events, becomes a crucial objective. For that reason, using the available resources in the areas that will generate a higher improvement of the resilience, will contribute to the global development of the network. Therefore, this paper wants to analyze what kind of characteristic makes a link a critical one when an extreme weather event damages a transport network and finally identify them.

Keywords: critical links, extreme weather events, hazard, resilience, transport network

Procedia PDF Downloads 286

Authors: Rupu Yang, Cong Toan Tran, Assaad Zoughaib

Abstract:

The work provides an iterative nonlinear programming method to synthesize a heat exchanger network by manipulating the trade-offs between the heat load of process heat exchangers (HEs) and utilities. We consider for the synthesis problem two cases, the first one without fixed cost for HEs, and the second one with fixed cost. For the no fixed cost problem, the nonlinear programming (NLP) model with all the potential HEs is optimized to obtain the global optimum. For the case with fixed cost, the NLP model is iterated through adding/removing HEs. The method was applied in five case studies and illustrated quite well effectiveness. Among which, the approach reaches the lowest TAC (2,904,026$/year) compared with the best record for the famous Aromatic plants problem. It also locates a slightly better design than records in literature for a 10 streams case without fixed cost with only 1/9 computational time. Moreover, compared to the traditional mixed-integer nonlinear programming approach, the iterative NLP method opens a possibility to consider constraints (such as controllability or dynamic performances) that require knowing the structure of the network to be calculated.

Keywords: heat exchanger network, synthesis, NLP, optimization

Procedia PDF Downloads 162

Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

Procedia PDF Downloads 56

Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

Abstract:

The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

Procedia PDF Downloads 101