Search results for: climate network

Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

Procedia PDF Downloads 168

Authors: Amandeep Singh Dhaliwal

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Networking solutions, particularly wireless local area networks have revolutionized the technological advancement. Wireless Local Area Networks (WLANs) have gained a lot of popularity as they provide location-independent network access between computing devices. There are a number of access methods used in Wireless Networks among which DCF and PCF are the fundamental access methods. This paper emphasizes on the impact of DCF and PCF access mechanisms on the performance of the IEEE 802.11a, 802.11b and 802.11g standards. On the basis of various parameters viz. throughput, delay, load etc performance is evaluated between these three standards using above mentioned access mechanisms. Analysis revealed a superior throughput performance with low delays for 802.11g standard as compared to 802.11 a/b standard using both DCF and PCF access methods.

Keywords: DCF, IEEE, PCF, WLAN

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

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

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

Procedia PDF Downloads 261

Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Sadiye Didem Boztepe Erkis, Ismail Saritas, Selma Tasdemir

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Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modeled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the system developed, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.

Keywords: artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

Procedia PDF Downloads 159

Authors: Gheyzer Villegas, Edgardo Cedeño, Veruska Mata, Edmundo Chauran

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One of the most significant changes that occurred in global commerce was the emergence of cryptocurrencies and blockchain technology. There is still much debate about the adoption of the economic model based on crypto assets, and myriad international projects and initiatives are being carried out to try and explore the potential that this new field offers. The Hive blockchain is a prime example of this, featuring two use cases: of how trade based on its native currencies can give successful results in the exchange of goods and services and in the financing of social impact projects. Its decentralized management model and visionary administration of its development fund have become a key part of its success.

Keywords: Hive, business, network, blockchain

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Authors: Busra Mutlu Ipek, Merve Mutlu, Ahmet Mutlu

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Novel coronavirus COVID-19, which has recently influenced the world, poses a great threat to humanity. In order to overcome this challenging situation, scientists are working on developing effective vaccine against coronavirus. Many experts and researchers have also produced articles and done studies on this highly important subject. In this direction, this special topic was chosen for article to make a contribution to this area. The purpose of this article is to perform RNA sequence analysis of selected virus forms in the Coronaviridae family and predict/classify SARS-CoV-2 (COVID-19) from other selected complete genomes in coronaviridae family using proposed Artificial Neural Network(ANN) algorithm.

Keywords: Coronaviridae family, COVID-19, RNA sequencing, ANN, neural network

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Anderson Ngowa Chembe, John Olukuru

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Greenhouse emissions by human activities are known to irreversibly increase global temperatures through the greenhouse effect. This study seeks to propose a mortality model with sensitivity to heat-change effects as one of the underlying parameters in the model. As such, the study sought to establish the relationship between greenhouse emissions and mortality indices in five OECD countries (USA, UK, Japan, Canada & Germany). Upon the establishment of the relationship using correlation analysis, an additional parameter that accounts for the sensitivity of heat-changes to mortality rates was incorporated in the Lee-Carter model. Based on the proposed model, new parameter estimates were calculated using iterative algorithms for optimization. Finally, the goodness of fit for the original Lee-Carter model and the proposed model were compared using deviance comparison. The proposed model provides a better fit to mortality rates especially in USA, UK and Germany where the mortality indices have a strong positive correlation with the level of greenhouse emissions. The results of this study are of particular importance to actuaries, demographers and climate-risk experts who seek to use better mortality-modeling techniques in the wake of heat effects caused by increased greenhouse emissions.

Keywords: climate risk, greenhouse emissions, Lee-Carter model, OECD

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

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In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: Samai'la Abdullahi

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A graph is a pair of two set and so that a graph is a pictorial representation of a system using two basic element nodes and edges. A node is represented by a circle (either hallo shade) and edge is represented by a line segment connecting two nodes together. In this paper, we present a circuit network in the concept of graph theory application and also circuit models of graph are represented in logical connection method were we formulate matrix method of adjacency and incidence of matrix and application of truth table.

Keywords: euler circuit and path, graph representation of circuit networks, representation of graph models, representation of circuit network using logical truth table

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Authors: Soheil Roumi, Fan Zhang, Rodney Stewart

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Indoor environmental quality (IEQ) indexes represented the suitability of a place to study, work, and live. Many indexes have been introduced based on the physical measurement or occupant surveys in commercial buildings. The earlier studies did not elaborate on the relationship between energy consumption and IEQ in office buildings. Such a relationship can provide a comprehensive overview of the building's performance. Also, it would find the potential of already constructed buildings under the upcoming climate change. A commercial building in southeast Queensland, Australia, was evaluated in this study. Physical measurements of IEQ and Energy areconducted, and their relationship will be determined using statistical analysis. The case study building is modelled in TRNSys software, and it will be validatedusingthe actual building's BMS data. Then, the modelled buildingwill be simulated by predicted weather data developed by the commonwealth scientific and industrial research organisation of Australia to investigate the occupant resilience and energy consumption. Finally, recommendations will be presented to consume less energy while providinga proper indoor environment for office occupants.

Keywords: IEQ, office buildings, thermal comfort, occupant resilience

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Authors: Berkan Ural, Arif Eser, Sinan Apaydin

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Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

Procedia PDF Downloads 418

Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

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In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

Procedia PDF Downloads 157

Authors: Muhammad Sohail Sajid, Hafiz Muhammad Rizwan, Ashfaq Ahmad Chatta, Zafar Iqbal, Muhammad Saqib

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Changes in the climate are posing threats to the livestock community throughout the globe. Agro-grazing animals and natural vegetation as their forages are the most important components of animal production. Climate and local conditions not only determine the nature and kind of plants, their distribution, composition and nutritive value in different cropping belts and grazing sites but also influence number and kinds of grazing animals. Phytomineral supplementation can act as an indirect tool to boost-up immunological profile of animals leading to the development of resilience against parasitic infections. The present study correlates the trace element (Cu, Co, Mn, Zn) profile of grazing sheep, feedstuffs, respective soils and their GI helminths in a selected district of Sialkot, Punjab, Pakistan. Ten species of GI helminths were found during the survey. A significant (P < 0.05) variation in the concentrations (conc.) of Zn, Cu, Mn and Co was recorded in a total of 16 collected forages. During autumn, mean conc. of Cu, Zn and Co in sera were inversely proportional to the GI helminth burden; while, during spring, only Zn was inversely proportional to the GI helminth burden in grazing sheep. During autumn the highest conc. of Zn, Cu, Mn and Co were recorded in Echinochloa colona, Amaranthus viridis, Cannabis sativa, and Brachiaria ramose and during spring in Cichorium intybus, Cynodon dactylon, Parthenium hysterophorus and Coronopus didymus respectively. The trace element-rich forages, preferably Zn, found effective against helminth infection are advisable supplemental remedies to improve the trace element profile in grazing sheep. This mitigation strategy may ultimately improve the resilience against GI helminth infections especially in the resource poor countries like Pakistan.

Keywords: coprological examination, Trace elements, Sheep, Gastro-intestinal parasites, Prevalence, Sialkot, Pakistan

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Authors: Nijat S. İmamverdiyev

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The transition to renewable energy sources has become a critical component of global efforts to mitigate climate change and promote sustainable development. However, the deployment of renewable energy technologies can also have significant impacts on ecosystems and the services they provide, such as carbon sequestration, soil fertility, water quality, and biodiversity. It also highlights the potential co-benefits of renewable energy deployment for ecosystem services, such as reducing greenhouse gas emissions and improving air and water quality. Renewable energy sources, such as wind, solar, hydro, and biomass, are increasingly being used to meet the world's energy needs due to their environmentally friendly nature and the desire to reduce greenhouse gas emissions. However, the expansion of renewable energy infrastructure can also impact ecosystem services, which are the benefits that humans derive from nature, such as clean water, air, and food. This geographical assessment aims to evaluate the relationship between renewable energy infrastructure and ecosystem services. Here, also explores potential solutions to mitigate the negative effects of renewable energy infrastructure on ecosystem services, such as the use of ecological compensation measures, biodiversity-friendly design of renewable energy infrastructure, and stakeholder involvement in decision-making processes.

Keywords: renewable energy, solar energy, climate change, energy production

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Authors: Nabil Elkhatri, Mohamed Louay Metougui, Ngonidzashe Chirinda

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Mining activities have left a legacy of degraded landscapes, prompting urgent efforts for ecological restoration. Reforestation holds promise as a potent tool to rehabilitate these old mining sites, with the potential to sequester carbon and contribute to climate change mitigation. This study focuses on evaluating the carbon stock and sequestration potential of reforestation species in the context of Morocco's mining areas, employing the DeNitrification-DeComposition (DNDC) model. The research is grounded in recognizing the need to connect theoretical models with practical implementation, ensuring that reforestation efforts are informed by accurate and context-specific data. Field data collection encompasses growth patterns, biomass accumulation, and carbon sequestration rates, establishing an empirical foundation for the study's analyses. By integrating the collected data with the DNDC model, the study aims to provide a comprehensive understanding of carbon dynamics within reforested ecosystems on old mining sites. The major findings reveal varying sequestration rates among different reforestation species, indicating the potential for species-specific optimization of reforestation strategies to enhance carbon capture. This research's significance lies in its potential to contribute to sustainable land management practices and climate change mitigation strategies. By quantifying the carbon stock and sequestration potential of reforestation species, the study serves as a valuable resource for policymakers, land managers, and practitioners involved in ecological restoration and carbon management. Ultimately, the study aligns with global objectives to rejuvenate degraded landscapes while addressing pressing climate challenges.

Keywords: carbon stock, carbon sequestration, DNDC model, ecological restoration, mining sites, Morocco, reforestation, sustainable land management.

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Authors: Beenish Mujahid, Sana Malik

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Bioclimatic design Strategies plays a dynamic role in construction of Sustainable Buildings. This approach leads to reduction in the mechanical cooling of building which provides comfort to the occupants in sustainable manner. Such bioclimatic measures provide a complete framework of building design through responding to climatic features of particular site. The featured Passive cooling techniques for hot climatic region provides comfortable indoor temperature with ecological and financial benefits. The study is based on highlighting this approach to produce energy efficient buildings for Semi-Arid climate like Lahore, Pakistan. Being part of developing country, energy savings in Lahore city would help the Power Sector and resolves the World Issues of Global Warming and Ozone Layer Depletion. This article reviews the bioclimatic design strategies and their critical analysis to drive guidelines for Sustainable buildings in Lahore. The study shows that the demand for mechanical cooling systems including air conditioning, fans, and air coolers can be reduced through regional climatic design.

Keywords: bioclimatic design, buildings, comfort, energy efficient, Lahore

Procedia PDF Downloads 271

Authors: Kamyar Fakhr, Roozbeh Salmani

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Alphonse Bertillon developed the first biometric security system in the 1800s. Today, many governments and giant companies are considering or have procured biometrically enabled security schemes. Iris is a kaleidoscope of patterns and colors. Each individual holds a set of irises more unique than their thumbprint. Every single day, giant companies like Google and Apple are experimenting with reliable biometric systems. Now, after almost 200 years of improvements, face ID does not work with masks, it gives access to fake 3D images, and there is no global usage of biometric recognition systems as national identity (ID) card. The goal of this paper is to demonstrate the advantages of iris recognition overall biometric recognition systems. It make two extensions: first, we illustrate how a very large amount of internet fraud and cyber abuse is happening due to bugs in face recognition systems and in a very large dataset of 3.4M people; second, we discuss how establishing a secure global network of iris recognition devices connected to authoritative convolutional neural networks could be the safest solution to this dilemma. Another aim of this study is to provide a system that will prevent system infiltration caused by cyber-attacks and will block all wireframes to the data until the main user ceases the procedure.

Keywords: biometric system, convolutional neural network, cyber-attack, secure

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Authors: A. Briseno, A, Zorrilla

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Firms are increasingly responding to social and environmental claims from society. Practices oriented to attend issues such as poverty, work equality, or renewable energy, are being implemented more frequently by firms to address impacts on sustainability. However, questions remain on how the responses of firms vary across industries and regions between the social and the economic objectives. Using concepts from organizational theory and social network theory, this paper aims to create a theoretical framework that explains the internal and external influences that make a firm establish its objective. The framework explains why firms might have a different objective orientation in terms of its economic and social prioritization.

Keywords: organizational identity, social network theory, firm objective, value maximization, social responsibility

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Authors: Alessandra Rossodivita, Alexei Tikhomirov, Andrey Trufanov, Nikolay Kinash, Olga Berestneva, Svetlana Nikitina, Fabio Casati, Alessandro Visconti, Tommaso Saporito

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It is well known that world and national societies are exposed to diverse threats: anthropogenic, technological, and natural. Anthropogenic ones are of greater risks and, thus, attract special interest to researchers within wide spectrum of disciplines in efforts to lower the pertinent risks. Some researchers showed by means of multilayered, complex network models how media promotes the prevention of disease spread. To go further, not only are mass-media sources included in scope the paper suggests but also personificated social bots (socbots) linked according to reflexive theory. The novel scope considers information spread over conscious and unconscious agents while counteracting both natural and man-made threats, i.e., infections and terrorist hazards. Contrary to numerous publications on misinformation disseminated by ‘bad’ bots within social networks, this study focuses on ‘good’ bots, which should be mobilized to counter the former ones. These social bots deployed mixture with real social actors that are engaged in concerted actions at spreading, receiving and analyzing information. All the contemporary complex network platforms (multiplexes, interdependent networks, combined stem networks et al.) are comprised to describe and test socbots activities within competing information sharing tools, namely mass-media hubs, social networks, messengers, and e-mail at all phases of disasters. The scope and concomitant techniques present evidence that embedding such socbots into information sharing process crucially change the network topology of actor interactions. The change might improve or impair robustness of social network environment: it depends on who and how controls the socbots. It is demonstrated that the topological approach elucidates techno-social processes within the field and outline the roadmap to a safer world.

Keywords: complex network platform, counterterrorism, information sharing topology, social bots

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Authors: Adel A. Ghoneim

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Desalination using solar energy is an interesting option specifically at regions with abundant solar radiation since such areas normally have scarcity of clean water resources. Desalination is the procedure of eliminating dissolved minerals from seawater or brackish water to generate fresh water. In this work, a simulation program is developed to determine the performance of reverse osmosis (RO) water desalination plant powered by high concentrated photovoltaic (HCPV) panels in Kuwait climate conditions. The objective of such a photovoltaic thermal system is to accomplish a double output, i.e., co-generation of both electricity and fresh water that is applicable for rural regions with high solar irradiation. The suggested plan enables to design an RO plant that does not depend on costly batteries or additional land and significantly reduce the government costs to subsidize the water generation cost. Typical weather conditions for Kuwait is employed as input to the simulation program. The simulation program is utilized to optimize the system efficiency as well as the distillate water production. The areas and slopes of HCPV modules are varied to attain maximum yearly power production. Maximum yearly distillate production and HCPV energy generation are found to correspond to HCPV facing south with tilt of 27° (Kuwait latitude-3°). The power needed to produce 1 l of clean drinking water ranged from 2 to 8 kW h/m³, based on the salinity of the feed water and the system operating conditions. Moreover, adapting HCPV systems achieve an avoided greenhouse gases emission by about 1128 ton CO₂ annually. Present outcomes certainly illustrate environmental advantages of water desalination system powered by high concentrated photovoltaic systems in Kuwait climate conditions.

Keywords: desalination, high concentrated photovoltaic systems, reverse osmosis, solar radiation

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Authors: Abdelmoniem A. Attaelmanan, Ahmed A. H. Siddig

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This study was conducted in light of continual global climate changes that projected increasing aridity, changes in soil fertility, and pollution. Plant growth and development largely depend on the combination of availing water and nutrients in the soil. Changes in the climate and atmospheric chemistry can cause serious effects on these growth factors. Plant carbon (C), nitrogen (N), and hydrogen (H) play a fundamental role in the maintenance of ecosystem structure and function. Hashab (Acacia senegal), which produces gum Arabic, supports dryland ecosystems in tropical zones by its potentiality to restore degraded soils; hence it is ecologically and economically important for the dry areas of sub-Saharan Africa. The study aims at investigating the effects of water stress (simulated drought) and poor soil type on Acacia senegal C, N, and H contents. Seven days old seedlings were assigned to the treatments in Split- plot design for four weeks. The main plot is irrigation interval (well-watered and water-stressed), and the subplot is soil types (silt and sand soils). Seedling's C%, N%, and H% were measured using CHNS-O Analyzer and applying Standard Test Method. Irrigation intervals and soil types had no effects on seedlings and leaves C%, N%, and H%, irrigation interval had affected stem C and H%, both irrigation intervals and soil types had affected root N% and interaction effect of water and soil was found on leaves and root's N%. Synthesis application of well-watered irrigation with soil that is rich in N and other nutrients would result in the greatest seedling C, N, and H content which will enhance growth and biomass accumulation and can play a crucial role in ecosystem productivity and services in the dryland regions.

Keywords: Acacia senegal, Africa, climate change, drylands, nutrients biomass, Sub-Saharan, Sudan

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Authors: Eylem Koç, Hasan Arda Burhan

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Location selection presents a crucial decision problem in today’s business world where strategic decision making processes have critical importance. Thus, location selection has strategic importance for companies in boosting their strength regarding competition, increasing corporate performances and efficiency in addition to lowering production and transportation costs. A right choice in location selection has a direct impact on companies’ commercial success. In this study, a store location selection problem of Carglass Turkey which operates in vehicle glass branch is handled. As this problem includes both tangible and intangible criteria, Analytic Network Process (ANP) was accepted as the main methodology. The model consists of control hierarchy and BOCR subnetworks which include clusters of actors, alternatives and criteria. In accordance with the management’s choices, five different locations were selected. In addition to the literature review, a strict cooperation with the actor group was ensured and maintained while determining the criteria and during whole process. Obtained results were presented to the management as a report and its feasibility was confirmed accordingly.

Keywords: analytic network process (ANP), BOCR, multi-actor decision making, multi-criteria decision making, real-life problem, location selection

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Bimal Raj Regmi, Cassandra Star

Abstract:

Climate change adaptation is now the priority of many Least Developed Countries (LDCs). The country governments in LDCs are designing institutional and financing architecture to implement adaptation programmes. Nepal has introduced the concept of Local Adaptation Plan of Action (LAPA) to facilitate adaptation at the local level. However, there is lack of clarity and ambiguity on whether or not LAPA can be effective means to reach to the most vulnerable. This research paper aims to generate evidences to assess the applicability and significance of LAPA. The study used a case study approach and relied on data gathered from field studies carried out in Pyuthan and Nawalparasi district of Nepal. The findings show that LAPA has potentials to link the community based adaptation with national adaptation initiatives and thus act as middle range approach to adaptation planning. However, the current scale of LAPA and its approaches to planning and delivery are constraints by socio-economic and governance barriers. This research paper argue that the in order to address the constraints a more flexible and co-management approach to LAPA is needed.

Keywords: community based adaptation, local adaptation, co-management, climate change

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Authors: Robert Saputra, Tomas Havlicek

Abstract:

This study discusses the design of collaborative rural governance strategies to enhance the rural economy through Village-owned Enterprises (VOE) in Riau Province, Indonesia. Using Soft Systems Methodology (SSM) combined with Textual Network Analysis (TNA) in the Rich Picture stage of SSM, we investigated the current state of VOE management. Significant obstacles identified include insufficient business feasibility analyses, lack of managerial skills, misalignment between strategy and practice, and inadequate oversight. To address these challenges, we propose a collaborative strategy involving regional governments, academic institutions, NGOs, and the private sector. This strategy emphasizes community needs assessments, efficient resource mobilization, and targeted training programs. A dedicated working group will ensure continuous monitoring and iterative improvements. Our research highlights the novel integration of SSM with TNA, providing a robust framework for improving VOE management and demonstrating the potential of collaborative efforts in driving rural economic development.

Keywords: village-owned enterprises (VOE), rural economic development, soft system methodology (SSM), textual network analysis (TNA), collaborative governance

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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