Search results for: green network

Authors: Carine Muster, Prina Howald Erika

Abstract:

Today's ice rinks are important energy consumers for the production and maintenance of ice. At the same time, users demand that the other rooms should be tempered or heated. The building complex must equally provide cooled and heated zones, which does not translate as carbon-zero ice rinks. The study provides an analysis of how the civil engineering sector can significantly impact minimizing greenhouse gas emissions and optimizing synergies across an entire ice rink complex. The analysis focused on three distinct aspects: the layout, including the volumetric layout of the premises present in an ice rink; the materials chosen that can potentially use the most ecological structural approach; and the construction methods based on innovative solutions to reduce carbon footprint. The first aspect shows that the organization of the interior volumes and defining the shape of the rink play a significant role. Its layout makes the use and operation of the premises as efficient as possible, thanks to the differentiation between heated and cooled volumes while optimising heat loss between the different rooms. The sprayed concrete method, which is still little known, proves that it is possible to achieve the strength of traditional concrete for the structural aspect of the load-bearing and non-load-bearing walls of the ice rink by using materials excavated from the construction site and providing a more ecological and sustainable solution. The installation of an empty sanitary space underneath the ice floor, making it independent of the rest of the structure, provides a natural insulating layer, preventing the transfer of cold to the rest of the structure and reducing energy losses. The addition of active pipes as part of the foundation of the ice floor, coupled with a suitable system, gives warmth in the winter and storage in the summer; this is all possible thanks to the natural heat in the ground. In conclusion, this study provides construction recommendations for future ice rinks with a significantly reduced energy demand, using some simple preliminary design concepts. By optimizing the layout, materials, and construction methods of ice rinks, the civil engineering sector can play a key role in reducing greenhouse gas emissions and promoting sustainability.

Keywords: climate change, energy optimization, green building, sustainability

Procedia PDF Downloads 60

Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

Abstract:

Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

Procedia PDF Downloads 85

Authors: Sammar Zain Allam

Abstract:

Smart Growth, intelligent cities, and healthy cities cited by WHO world health organization; they all call for clean air and minimizing air pollutants considering human health. Air quality is a thriving matter to achieve ecological cities; towards sustainable environmental development of urban fabric design. Selection criteria depends on the strategic location of our area as it is located at the entry of the city of Alexandria from its agricultural road. Besides, it represents the city center for retail, business, and educational amenities. Our study is analyzing readings of definite factors affecting air quality in a centric area in Alexandria. Our readings will be compared to standard measures of carbon dioxide, carbon monoxide, suspended particles, and air velocity or air flow. Carbon emissions are pondered in our study, in addition to suspended particles and the air velocity or air flow. Carbon dioxide and carbon monoxide crystalize the main elements to necessitate environmental and sustainable studies with the appearance of global warming and the glass house effect. Nevertheless, particulate matters are increasing causing breath issues especially to children and elder people; still threatening future generations to meet their own needs; sustainable development definition. Analysis of carbon dioxide, carbon monoxide, suspended particles together with air velocity or air flow has taken place in our area of study to manifest the relationship between these elements and the urban fabric design and land use distribution. For conclusion, dense urban fabric affecting air flow, and thus result in the concentration of air pollutants in certain zones. The appearance of open space with green areas allow the fading of air pollutants and help in their absorption. Along with dense urban fabric, high rise buildings trap air carriers which contribute to high readings of our elements. Also, street design may facilitate the circulation of air which helps carrying these pollutant away and distribute it to a wider space which decreases its harms and effects.

Keywords: carbon emissions, air quality measurements, arteries air quality, airflow or air velocity, particulate matter, clean air, urban density

Procedia PDF Downloads 421

Authors: Rahul Agarwal, Ashutosh Singh

Abstract:

Cervical cancer is one of the women related cancers which starts from the pre-cancerous cells and a fraction of women with pre-cancers of the cervix will develop cervical cancer. Cervical pre-cancers if treated in pre-invasive stage can prevent almost all true cervical squamous cell carcinoma. The present study investigates the genes and pathways that are involved in the progression of cervical cancer and are responsible in transition from pre-invasive stage to other advanced invasive stages. The study used GDS3292 microarray data to identify the stage specific genes in cervical cancer and further to generate the network of the significant genes. The microarray data GDS3292 consists of the expression profiling of 10 normal cervices, 7 HSILs and 21 SCCs samples. The study identifies 70 upregulated and 37 downregulated genes in HSIL stage while 95 upregulated and 60 downregulated genes in SCC stages. Biological process including cell communication, signal transduction are highly enriched in both HSIL and SCC stages of cervical cancer. Further, the ppi interaction of genes involved in HSIL and SCC stages helps in identifying the interacting partners. This work may lead to the identification of potential diagnostic biomarker which can be utilized for early stage detection.

Keywords: cervical cancer, HSIL, microarray, SCC

Procedia PDF Downloads 227

Authors: Meet Shah, Ankita Aditya, Dhruv Bindra, V. S. Omkar, Aashruti Seervi

Abstract:

In today's world, we need documents everywhere for a smooth workflow in the identification process or any other security aspects. The current system and techniques which are used for identification need one thing, that is ‘proof of existence’, which involves valid documents, for example, educational, financial, etc. The main issue with the current identity access management system and digital identification process is that the system is centralized in their network, which makes it inefficient. The paper presents the system which resolves all these cited issues. It is based on ‘blockchain’ technology, which is a 'decentralized system'. It allows transactions in a decentralized and immutable manner. The primary notion of the model is to ‘have everything with nothing’. It involves inter-linking required documents of a person with a single identity card so that a person can go anywhere without having the required documents with him/her. The person just needs to be physically present at a place wherein documents are necessary, and using a fingerprint impression and an iris scan print, the rest of the verification will progress. Furthermore, some technical overheads and advancements are listed. This paper also aims to layout its far-vision scenario of blockchain and its impact on future trends.

Keywords: blockchain, decentralized system, fingerprint impression, identity management, iris scan

Procedia PDF Downloads 121

Authors: Anjali G. Pillai, S. Chadrakaran

Abstract:

The extensive amount of sludge generated throughout the world, as a part of water treatment works, have caused various social and economic issues, such as a demand on landfill spaces, increase in environmental pollution and raising the waste management cost. With growing social awareness about toxic incinerator emissions and the increasing concern over the disposal of sludge on the agricultural land, the recovery of sewage sludge as a building and construction raw material can be considered as an innovative approach to tackle the sludge disposal problem. The proposed work aims at studying the recycling ability of the sludge, generated from the water treatment process, by incorporating it into the fired clay brick units. The work involves initial study of the geotechnical characteristics of the brick-clay and the sludge. Chemical compatibility of both the materials will be analyzed by X-ray fluorescence technique. The variation in the strength aspects with varying proportions of sludge i.e. 10%, 20%, 30% and 40% in the sludge-clay mix will also be determined by the proctor density test. Based on the optimum moisture content, the sludge-clay bricks will be manufactured in a brick manufacturing plant and the modified brick units will be tested to determine the variation in compressive strength, bulk density, firing shrinkage, shrinkage loss and initial water absorption rate with respect to the conventional clay bricks. The results will be compared with the specifications given in Indian Standards to arrive at the potential use of the new bricks. The durability aspect will be studied by conducting the leachate analysis test using atomic adsorption spectrometry. The lightweight characteristics of the sludge modified bricks will be ascertained with the scanning electron microscope technique which will be indicative of the variation in pore structure with the increase in sludge content within the bricks. The work will determine the suitable proportion of the sludge – clay mix in the brick which can then be effectively implemented. The feasibility aspect of the work will be determined for commercial production of the units. The work involves providing a strategy for conversion of waste to resource. Moreover, it provides an alternative solution to the problem of growing scarcity of brick-clay for the manufacturing of fired clay bricks.

Keywords: eco-bricks, green construction material, sludge amended bricks, sludge disposal, waste management

Procedia PDF Downloads 298

Authors: Shriya Shukla, Lachin Fernando

Abstract:

Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

Procedia PDF Downloads 90

Authors: Sufia Khowaja, Nishat Fatima

Abstract:

Social networking sites are significant for visually impaired to overcome the unique challenges they face and access the resources they need to succeed in their education and beyond which might be difficult to obtain through traditional means. It provides them an opportunity to build relationships, stay connected with their support network as well as to develop social skills which give them emotional support to fell less isolated. In this connection the study is conducted with the aim to determine the use of social networking sites, purpose of using and activities performed by visually impaired at Delhi University, Delhi, Jawaharlal Nehru University, Delhi and Jamia Milia Islamia, Delhi. The study followed survey technique in which structured interview is followed to collect data from 137 visually impaired students and analysed using ‘SPSS ver23’. The findings of the study revealed that mostly used social networking sites are whatsapp by 89.23% students of DU, 95.12% of JNU, 87.09% of JMI, followed by e-mail by 78.46% of DU, 78.04% of JNU, 64.51%; youtube by 73.84% DU, 90.24% JNU, 80.64% JMI. Purpose for using these sites is for academics mentioned by 96.92% DU, 100% JNU, 93.54% JMI. Activities performed on sites are sending and receiving messaging 96.92% DU, 92.68% JNU, 93.55% JMI, communicating with friends and family as well as getting academic information. Findings of the study will be helpful for libraries to disseminate their services and resources as well as latest updates to their visually impaired users with the help of most used tools.

Keywords: social networking sites, visually impaired, Delhi University, Jawaharlal Nehru University, Jamia Milia Islamia

Procedia PDF Downloads 86

Authors: Jay L. Fu

Abstract:

Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

Procedia PDF Downloads 138

Authors: Dhritilekha Deka, Deepak Patwa, Ravi K., Archana M. Nair

Abstract:

Bioaccumulation of heavy metal contaminants due to intense anthropogenic interference degrades the environment and ecosystem functions. Conventional physicochemical methods involve energy-intensive and costly methodologies. Phytoremediation, on the other hand, provides an efficient nature-based strategy for the reclamation of heavy metal-contaminated sites. However, the slow process and adaptation to high-concentration contaminant sequestration often limit the efficiency of the method. This necessitates natural amendments such as biochar to improve phytoextraction and stabilize the green cover. Biochar is a highly porous structure with high carbon sequestration potential and containing negatively charged functional groups that provide binding sites for the positively charged metals. This study aims to develop and determine the synergy between sugarcane bagasse biochar content and phytoremediation. A 60-day pot experiment using perennial tussock vetiver grass (Chrysopogon zizanioides) was conducted for different biochar contents of 1%, 2%, and 4% for the removal of cadmium and zinc. A concentration of 500 ppm is maintained for the amended and unamended control (CK) samples. The survival rates of the plants, biomass production, and leaf area index were measured for the plant growth characteristics. Results indicate a visible change in the plant growth and the heavy metal concentration with the biochar content. The bioconcentration factor (BCF) in the plant improved significantly for the 4% biochar content by 57% in comparison to the control CK treatment in Cd-treated soils. The Zn soils indicated the highest reduction in the metal concentration by 50% in the 2% amended samples and an increase in the BCF in all the amended samples. The translocation from the rhizosphere to the shoots was low but not dependent on the amendment content and varied for each contaminant type. The root-to-shoot ratio indicates higher values compared to the control samples. The enhanced tolerance capacities can be attributed to the nutrients released by the biochar in the soil. The study reveals the high potential of biochar as a phytoremediation amendment, but its effect is dependent on the soil and heavy metal and accumulator species.

Keywords: phytoextraction, biochar, heavy metals, chrysopogon zizanioides, bioaccumulation factor

Procedia PDF Downloads 60

Authors: Hassan Hussin Zwida

Abstract:

The liquified LPG (Liquefied Petroleum Gas) drying system at the Complex is designed to remove water and mercaptans from the LPG stream. Upon saturation of the desiccant beds, a regeneration cycle becomes necessary. The original design routed the regeneration gas, produced during the LPG dryer heating cycle, to the sulfur recovery unit to the incineration. However, concerns regarding high temperatures and potential unit disruptions led to a modification where the gas is currently vented to the acid flare for the initial hour before being diverted to the LP network fuel gas system. While this addresses the temperature concerns, it generates significant smoke due to the presence of liquid hydrocarbons. This paper proposes an approach to recover the regeneration gas and redirect it back to the gas plant's (off-gas compressors) instead of sending it to the AC (Acid Flare), by utilizing the existing pipe 6” and connected to off gas compressor KO (Knock-Out ) Drums . This option is simple to operate, flexible, environment-friendly solution as long-term solution, lower in capital expenditure and increase the company's profitability. The feasibility of this proposal is supported by dynamic simulations. The simulations suggest the possibility of operating two out of the three off-gas compressors and LPG (Liquefied petroleum gas) as a liquid phase, is foreseen to be carried over and gathered at the bottom level of the KO (Knock-Out) Drum.

Keywords: thermal incinerator, off-gas compressors, environment, knock-out drums, acid flare

Procedia PDF Downloads 35

Authors: Felix Boehnisch, Alexander Lutz

Abstract:

Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

Procedia PDF Downloads 174

Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

Abstract:

Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

Procedia PDF Downloads 306

Authors: Bandar Alahmadi, Lethia Jackson

Abstract:

Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

Procedia PDF Downloads 333

Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

Abstract:

The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

Procedia PDF Downloads 69

Authors: Emelia Noronha, Milind Malshe

Abstract:

The phatic function of communication is a vital element of any conversation. This research paper looks into this function with respect to personal blogs maintained by Indian bloggers. This paper is a study into the phenomenon of phatic communication maintained by bloggers through their blogs. Based on a linguistic analysis of the posts of twenty eight Indian bloggers, writing in English, studied over a period of three years, the study indicates that though the blogging phenomenon is not conversational in the same manner as face-to-face communication, it does make ample provision for feedback that is conversational in nature. Ordinary day to day offline conversations use conventionalized phatic utterances; those on the social media are in a perpetual mode of innovation and experimentation in order to sustain contact with its readers. These innovative methods and means are the focus of this study. Though the personal blogger aims to chronicle his/her personal life through the blog, the socializing function is crucial to these bloggers. In comparison to the western personal blogs which focus on the presentation of the ‘bounded individual self’, we find Indian personal bloggers engage in the presentation of their ‘social selves’. These bloggers yearn to reach out to the readers on the internet and the phatic function serves to initiate, sustain and renew social ties on the blogosphere thereby consolidating the social network of readers and bloggers.

Keywords: personal blogs, phatic, social-selves, blog readers

Procedia PDF Downloads 354

Authors: O. A. Ajigini, E. N. Mwim

Abstract:

Cybersecurity is the protection of computers, programs, networks, and data from attack, damage, unauthorised, unintended access, change, or destruction. Organisations collect, process and store their confidential and sensitive information on computers and transmit this data across networks to other computers. Moreover, the advent of internet technologies has led to various cyberattacks resulting in dangerous consequences for organisations. Therefore, with the increase in the volume and sophistication of cyberattacks, there is a need to develop models and make recommendations for the management of cybersecurity threats in organisations. This paper reports on various threats that cause malicious damage to organisations in cyberspace and provides measures on how these threats can be eliminated or reduced. The paper explores various aspects of protection measures against cybersecurity threats such as handling of sensitive data, network security, protection of information assets and cybersecurity awareness. The paper posits a model and recommendations on how to manage cybersecurity threats in organisations effectively. The model and the recommendations can then be utilised by organisations to manage the threats affecting their cyberspace. The paper provides valuable information to assist organisations in managing their cybersecurity threats and hence protect their computers, programs, networks and data in cyberspace. The paper aims to assist organisations to protect their information assets and data from cyberthreats as part of the contributions toward community engagement.

Keywords: confidential information, cyberattacks, cybersecurity, cyberspace, sensitive information

Procedia PDF Downloads 252

Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

Abstract:

This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

Procedia PDF Downloads 246

Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

Abstract:

The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

Procedia PDF Downloads 91

Authors: Lucia Ceccherini Nelli, Alessandra Donato

Abstract:

The ABITA Master course of the University of Florence offered by the Department of Architecture covers nearly all the energy-relevant issues that can arise in public and private companies and sectors. The main purpose of the Master course, active since 2003, is to analyse the energy consumption of building technologies, components, and structures at the conceptual design stage, so it could be very helpful, for designers, when making decisions related to the selection of the most suitable design alternatives and for the materials choice that will be used in an energy-efficient building. The training course provides a solid basis for increasing the knowledge and skills of energy managers and is developed with an emphasis on practical experiences related to the knowledge through case studies, measurements, and verification of energy-efficient solutions in buildings, in the industry and in the cities. The main objectives are: i)To raise the professional standards of those engaged in energy auditing, ii) To improve the practice of energy auditors by encouraging energy auditing professionals in a continuing education program of professional development, iii) Implement in the use of instrumentations for the typical measurements, iv) To propose an integrated methodology that links energy analysis tools with green building certification systems. This methodology will be applied at the early design stage of a project’s life. The final output of the practical training is to achieve an elevated professionalism in the study of environmental design and Energy management in buildings. The results are the redaction of line guides instruction for the energy refurbishment of Public schools in Florence. The school heritage of the Municipality of Florence requires interventions for the control of energy performance, as old construction buildings are often made without taking into account the necessary envelope performance. For this reason, every year, the Master's course aims to study groups of public schools to enable the Municipality to carry out energy redevelopment interventions on the existing building heritage. The future challenges of the education and training program are related to follow-up activities, the development of interactive tools and the curriculum's customization to meet the constantly growing needs of energy experts from industry.

Keywords: expert in energy, energy auditing, public buildings, thermal analysis

Procedia PDF Downloads 183

Authors: L. Edirisinghe, P. Mukherjee, H. Edirisinghe

Abstract:

Virtual Container Yard (VCY) is a modern concept that helps to reduce the empty container repositioning cost of carriers. The concept of VCY is based on container interchange between shipping lines. Although this mechanism has been theoretically accepted by the shipping community as a feasible solution, it has not yet achieved the necessary momentum among container shipping lines (CSL). This paper investigates whether there is any legal influence on this industry myopia about the VCY. It is believed that this is the first publication that focuses on the legal aspects of container exchange between carriers. Not much literature on this subject is available. This study establishes with statistical evidence that there is a phobia prevailing in the shipping industry that exchanging containers with other carriers may lead to various legal implications. The complexity of exchange is two faceted. CSLs assume that offering a container to another carrier (obviously, a competitor in terms of commercial context) or using a container offered by another carrier may lead to undue legal implications. This research reveals that this fear is reflected through four types of perceived components, namely: shipping associate; warehouse associate; network associate; and trading associate. These components carry eighteen subcomponents that comprehensively cover the entire process of a container shipment. The statistical explanation has been supported through regression analysis; INCO terms were used to illustrate the shipping process.

Keywords: virtual container yard, legal, maritime law, inventory

Procedia PDF Downloads 159

Authors: Elif Tugce Aksun Tumerkan

Abstract:

Fluorescent proteins (FPs) have become very popular since green fluorescent protein discovered from crystal jellyfish. It is known that Anthozoa species have a wide range of chromophore organisms, and the initial crystal structure for non-fluorescent chromophores obtained from the reef-building coral has been determined. There are also differently coloured pigments in non-bioluminescent Anthozoa zooxanthellate and azooxanthellate which are frequently members of the GFP-like protein family. The development of fluorescent proteins (FPs) and their applications is an outstanding example of basic science leading to practical biotechnological and medical applications. Fluorescent proteins have several applications in science and are used as important indicators in molecular biology and cell-based research. With rising interest in cell biology, FPs have used as biosensor indicators and probes in pharmacology and cell biology. Using fluorescent proteins in genetically encoded metabolite sensors has many advantages than chemical probes for metabolites such as easily introduced into any cell or organism in any sub-cellular localization and giving chance to fixing to fluoresce of different colours or characteristics. There are different factors effects to signalling mechanism when they used as a biosensor. While there are wide ranges of research have been done on the significance and applications of fluorescent proteins, the cell signalling response of FPs and target cell are less well understood. In this study, it was aimed to clarify the response of adaptive mechanisms of coral species such as pH, temperature and symbiotic relationship and target cells properties on the signalling capacity. Corals are a rich natural source of fluorescent proteins that change with environmental conditions such as light, heat stress and injury. Adaptation mechanism of coral species to these types of environmental variations is important factor due to FPs properties have affected by this mechanism. Since fluorescent proteins obtained from nature, their own ecological property like the symbiotic relationship is observed very commonly in coral species and living conditions have the impact on FPs efficiency. Target cell properties also have an effect on signalling and visualization. The dynamicity of detector that used for reading fluorescence and the level of background fluorescence are key parameters for the quality of the fluorescent signal. Among the factors, it can be concluded that coral species adaptive characteristics have the strongest effect on FPs signalling capacity.

Keywords: biosensor, cell biology, environmental conditions, fluorescent protein, sea anemone

Procedia PDF Downloads 162

Authors: Prashansa Singh, Manjot Kaur, Rohit Bajaj

Abstract:

The proliferation of wireless sensor networks and Internet of Things (IoT) devices in the quickly changing digital landscape has highlighted the urgent need for strong security solutions that can handle these systems’ limited resources. A key solution to this problem is the emergence of ultralightweight security protocols, which provide strong security features while respecting the strict computational, energy, and memory constraints imposed on these kinds of devices. This in-depth analysis explores the field of ultralightweight security protocols, offering a thorough examination of their evolution, salient features, and the particular security issues they resolve. We carefully examine and contrast different protocols, pointing out their advantages and disadvantages as well as the compromises between resource limitations and security resilience. We also study these protocols’ application domains, including the Internet of Things, RFID systems, and wireless sensor networks, to name a few. In addition, the review highlights recent developments and advancements in the field, pointing out new trends and possible avenues for future research. This paper aims to be a useful resource for researchers, practitioners, and developers, guiding the design and implementation of safe, effective, and scalable systems in the Internet of Things era by providing a comprehensive overview of ultralightweight security protocols.

Keywords: wireless sensor network, machine-to-machine, MQTT broker, server, ultralightweight, TCP/IP

Procedia PDF Downloads 68

Authors: Dermot O'Brien, Vasileios Christaras, Georgios Fontaras, Igor Nai Fovino, Ioannis Kounelis

Abstract:

With the rise of the Internet of Things (IoT), 5G, and blockchain (BC) technologies, vehicles are becoming ever increasingly connected and are already transmitting substantial amounts of data to the original equipment manufacturers (OEMs) servers. This data could be used to help detect mileage fraud and enable more accurate vehicle emissions monitoring. This would not only help regulators but could enable applications such as permitting efficient drivers to pay less tax, geofencing for air quality improvement, as well as pollution tolling and trading platforms for transport-related businesses and EU citizens. Other applications could include traffic management and shared mobility systems. BC enables the transmission of data with additional security and removes single points of failure while maintaining data provenance, identity ownership, and the possibility to retain varying levels of privacy depending on the requirements of the applied use case. This research performs simulations of vehicles interacting with European member state authorities and European Commission BC nodes that are running hyperleger fabric and explores whether the technology is currently feasible for transport applications such as the emission monitoring use-case.

Keywords: future transportation systems, technological innovations, policy approaches for transportation future, economic and regulatory trends, blockchain

Procedia PDF Downloads 168

Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 312

Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

Abstract:

The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

Procedia PDF Downloads 489

Authors: Bel G Raggad

Abstract:

An e-Learning setting is a distributed computing environment where information resources can be connected to any public network. Public networks are very unsecure which can compromise the reliability of an e-Learning environment. This study is only concerned with the intrusion detection aspect of e-Learning security and how incident responses are planned. The literature reported great advances in intrusion detection system (ids) but neglected to study an important ids weakness: suspected events are detected but an intrusion is not determined because it is not defined in ids databases. We propose an incident response generator (DIRG) that produces incident responses when the working ids system suspects an event that does not correspond to a known intrusion. Data involved in intrusion detection when ample uncertainty is present is often not suitable to formal statistical models including Bayesian. We instead adopt Dempster and Shafer theory to process intrusion data for the unknown event. The DIRG engine transforms data into a belief structure using incident scenarios deduced by the security administrator. Belief values associated with various incident scenarios are then derived and evaluated to choose the most appropriate scenario for which an automatic incident response is generated. This article provides a numerical example demonstrating the working of the DIRG system.

Keywords: decision support system, distributed computing, e-Learning security, incident response, intrusion detection, security risk, statefull inspection

Procedia PDF Downloads 429

Authors: Giselle Maggie Fer Castañeda, Diego Iván González

Abstract:

The objective of this work was to study the autoshaped choice behavior in the Donahoe, Burgos and Palmer (DBP) neural network model and analyze it under the matching law. Autoshaped choice can be viewed as a form of economic behavior defined as the preference between alternatives according to their relative outcomes. The Donahoe, Burgos and Palmer (DBP) model is a connectionist proposal that unifies operant and Pavlovian conditioning. This model has been used for more than three decades as a neurobehavioral explanation of conditioning phenomena, as well as a generator of predictions suitable for experimental testing with non-human animals and humans. The study consisted of different simulations in which, in each one, a ratio of reinforcement was established for two alternatives, and the responses (i.e., activations) in each of them were measured. Choice studies with animals have demonstrated that the data generally conform closely to the generalized matching law equation, which states that the response ratio equals proportionally to the reinforcement ratio; therefore, it was expected to find similar results with the neural networks of the Donahoe, Burgos and Palmer (DBP) model since these networks have simulated and predicted various conditioning phenomena. The results were analyzed by the generalized matching law equation, and it was observed that under some contingencies, the data from the networks adjusted approximately to what was established by the equation. Implications and limitations are discussed.

Keywords: matching law, neural networks, computational models, behavioral sciences

Procedia PDF Downloads 64

Authors: Zhiwen Pan, Jesus Pacheco, Salim Hariri, Yiqiang Chen, Bozhi Liu

Abstract:

The Internet of Things (IoT) will lead to the development of advanced Smart Home services that are pervasive, cost-effective, and can be accessed by home occupants from anywhere and at any time. However, advanced smart home applications will introduce grand security challenges due to the increase in the attack surface. Current approaches do not handle cybersecurity from a holistic point of view; hence, a systematic cybersecurity mechanism needs to be adopted when designing smart home applications. In this paper, we present a generic intrusion detection methodology to detect and mitigate the anomaly behaviors happened in Smart Home Systems (SHS). By utilizing our Smart Home Context Data Structure, the heterogeneous information and services acquired from SHS are mapped in context attributes which can describe the context of smart home operation precisely and accurately. Runtime models for describing usage patterns of home assets are developed based on characterization functions. A threat-aware action management methodology, used to efficiently mitigate anomaly behaviors, is proposed at the end. Our preliminary experimental results show that our methodology can be used to detect and mitigate known and unknown threats, as well as to protect SHS premises and services.

Keywords: Internet of Things, network security, context awareness, intrusion detection

Procedia PDF Downloads 182

Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

Abstract:

The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

Procedia PDF Downloads 65